diff --git a/Ch02-statlearn-lab.ipynb b/Ch02-statlearn-lab.ipynb
index dfebaef2..09156b64 100644
--- a/Ch02-statlearn-lab.ipynb
+++ b/Ch02-statlearn-lab.ipynb
@@ -146,7 +146,27 @@
      "shell.execute_reply": "2024-06-05T01:13:21.524324Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mSignature:\u001b[39m print(*args, sep=\u001b[33m' '\u001b[39m, end=\u001b[33m'\\n'\u001b[39m, file=\u001b[38;5;28;01mNone\u001b[39;00m, flush=\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[31mDocstring:\u001b[39m\n",
+      "Prints the values to a stream, or to sys.stdout by default.\n",
+      "\n",
+      "sep\n",
+      "  string inserted between values, default a space.\n",
+      "end\n",
+      "  string appended after the last value, default a newline.\n",
+      "file\n",
+      "  a file-like object (stream); defaults to the current sys.stdout.\n",
+      "flush\n",
+      "  whether to forcibly flush the stream.\n",
+      "\u001b[31mType:\u001b[39m      builtin_function_or_method"
+     ]
+    }
+   ],
    "source": [
     "print?\n"
    ]
@@ -291,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "id": "8bcb5c5b",
    "metadata": {
     "execution": {
@@ -308,7 +328,7 @@
        "[3, 4, 5, 4, 9, 7]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -365,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "ac55fedf",
    "metadata": {
     "execution": {
@@ -401,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "c4d9f140",
    "metadata": {
     "execution": {
@@ -440,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "106ef1ea",
    "metadata": {
     "execution": {
@@ -458,7 +478,7 @@
        "array([ 7, 13, 12])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -487,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "d111ed03",
    "metadata": {
     "execution": {
@@ -506,7 +526,7 @@
        "       [3, 4]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -538,7 +558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "d9562687",
    "metadata": {
     "execution": {
@@ -555,7 +575,7 @@
        "2"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -576,7 +596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "4fe5b7cf",
    "metadata": {
     "execution": {
@@ -594,7 +614,7 @@
        "dtype('int64')"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -616,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "c4c43c7d",
    "metadata": {
     "execution": {
@@ -634,7 +654,7 @@
        "dtype('float64')"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -655,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "62851cb3",
    "metadata": {
     "execution": {
@@ -666,7 +686,135 @@
     },
     "lines_to_next_cell": 0
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mDocstring:\u001b[39m\n",
+      "array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0,\n",
+      "      like=None)\n",
+      "\n",
+      "Create an array.\n",
+      "\n",
+      "Parameters\n",
+      "----------\n",
+      "object : array_like\n",
+      "    An array, any object exposing the array interface, an object whose\n",
+      "    ``__array__`` method returns an array, or any (nested) sequence.\n",
+      "    If object is a scalar, a 0-dimensional array containing object is\n",
+      "    returned.\n",
+      "dtype : data-type, optional\n",
+      "    The desired data-type for the array. If not given, NumPy will try to use\n",
+      "    a default ``dtype`` that can represent the values (by applying promotion\n",
+      "    rules when necessary.)\n",
+      "copy : bool, optional\n",
+      "    If true (default), then the object is copied.  Otherwise, a copy will\n",
+      "    only be made if ``__array__`` returns a copy, if obj is a nested\n",
+      "    sequence, or if a copy is needed to satisfy any of the other\n",
+      "    requirements (``dtype``, ``order``, etc.).\n",
+      "order : {'K', 'A', 'C', 'F'}, optional\n",
+      "    Specify the memory layout of the array. If object is not an array, the\n",
+      "    newly created array will be in C order (row major) unless 'F' is\n",
+      "    specified, in which case it will be in Fortran order (column major).\n",
+      "    If object is an array the following holds.\n",
+      "\n",
+      "    ===== ========= ===================================================\n",
+      "    order  no copy                     copy=True\n",
+      "    ===== ========= ===================================================\n",
+      "    'K'   unchanged F & C order preserved, otherwise most similar order\n",
+      "    'A'   unchanged F order if input is F and not C, otherwise C order\n",
+      "    'C'   C order   C order\n",
+      "    'F'   F order   F order\n",
+      "    ===== ========= ===================================================\n",
+      "\n",
+      "    When ``copy=False`` and a copy is made for other reasons, the result is\n",
+      "    the same as if ``copy=True``, with some exceptions for 'A', see the\n",
+      "    Notes section. The default order is 'K'.\n",
+      "subok : bool, optional\n",
+      "    If True, then sub-classes will be passed-through, otherwise\n",
+      "    the returned array will be forced to be a base-class array (default).\n",
+      "ndmin : int, optional\n",
+      "    Specifies the minimum number of dimensions that the resulting\n",
+      "    array should have.  Ones will be prepended to the shape as\n",
+      "    needed to meet this requirement.\n",
+      "like : array_like, optional\n",
+      "    Reference object to allow the creation of arrays which are not\n",
+      "    NumPy arrays. If an array-like passed in as ``like`` supports\n",
+      "    the ``__array_function__`` protocol, the result will be defined\n",
+      "    by it. In this case, it ensures the creation of an array object\n",
+      "    compatible with that passed in via this argument.\n",
+      "\n",
+      "    .. versionadded:: 1.20.0\n",
+      "\n",
+      "Returns\n",
+      "-------\n",
+      "out : ndarray\n",
+      "    An array object satisfying the specified requirements.\n",
+      "\n",
+      "See Also\n",
+      "--------\n",
+      "empty_like : Return an empty array with shape and type of input.\n",
+      "ones_like : Return an array of ones with shape and type of input.\n",
+      "zeros_like : Return an array of zeros with shape and type of input.\n",
+      "full_like : Return a new array with shape of input filled with value.\n",
+      "empty : Return a new uninitialized array.\n",
+      "ones : Return a new array setting values to one.\n",
+      "zeros : Return a new array setting values to zero.\n",
+      "full : Return a new array of given shape filled with value.\n",
+      "\n",
+      "\n",
+      "Notes\n",
+      "-----\n",
+      "When order is 'A' and ``object`` is an array in neither 'C' nor 'F' order,\n",
+      "and a copy is forced by a change in dtype, then the order of the result is\n",
+      "not necessarily 'C' as expected. This is likely a bug.\n",
+      "\n",
+      "Examples\n",
+      "--------\n",
+      ">>> np.array([1, 2, 3])\n",
+      "array([1, 2, 3])\n",
+      "\n",
+      "Upcasting:\n",
+      "\n",
+      ">>> np.array([1, 2, 3.0])\n",
+      "array([ 1.,  2.,  3.])\n",
+      "\n",
+      "More than one dimension:\n",
+      "\n",
+      ">>> np.array([[1, 2], [3, 4]])\n",
+      "array([[1, 2],\n",
+      "       [3, 4]])\n",
+      "\n",
+      "Minimum dimensions 2:\n",
+      "\n",
+      ">>> np.array([1, 2, 3], ndmin=2)\n",
+      "array([[1, 2, 3]])\n",
+      "\n",
+      "Type provided:\n",
+      "\n",
+      ">>> np.array([1, 2, 3], dtype=complex)\n",
+      "array([ 1.+0.j,  2.+0.j,  3.+0.j])\n",
+      "\n",
+      "Data-type consisting of more than one element:\n",
+      "\n",
+      ">>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])\n",
+      ">>> x['a']\n",
+      "array([1, 3])\n",
+      "\n",
+      "Creating an array from sub-classes:\n",
+      "\n",
+      ">>> np.array(np.mat('1 2; 3 4'))\n",
+      "array([[1, 2],\n",
+      "       [3, 4]])\n",
+      "\n",
+      ">>> np.array(np.mat('1 2; 3 4'), subok=True)\n",
+      "matrix([[1, 2],\n",
+      "        [3, 4]])\n",
+      "\u001b[31mType:\u001b[39m      builtin_function_or_method"
+     ]
+    }
+   ],
    "source": [
     "np.array?\n"
    ]
@@ -681,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "816dac08",
    "metadata": {
     "execution": {
@@ -699,7 +847,7 @@
        "dtype('float64')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -719,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "9a81115b",
    "metadata": {
     "execution": {
@@ -737,7 +885,7 @@
        "(2, 2)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -762,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "8f051245",
    "metadata": {
     "execution": {
@@ -780,7 +928,7 @@
        "10"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -800,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "906c3a0e",
    "metadata": {
     "execution": {
@@ -818,7 +966,7 @@
        "10"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -846,7 +994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "e8b8d6db",
    "metadata": {
     "execution": {
@@ -897,7 +1045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "47c39f33",
    "metadata": {
     "execution": {
@@ -915,7 +1063,7 @@
        "1"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -935,7 +1083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "d74a5638",
    "metadata": {
     "execution": {
@@ -953,7 +1101,7 @@
        "6"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -976,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "5a19217f",
    "metadata": {
     "execution": {
@@ -1034,7 +1182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "116ec7b8",
    "metadata": {
     "execution": {
@@ -1051,10 +1199,10 @@
      "evalue": "'tuple' object does not support item assignment",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[23], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m my_tuple \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmy_tuple\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m my_tuple = (\u001b[32m3\u001b[39m, \u001b[32m4\u001b[39m, \u001b[32m5\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mmy_tuple\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m = \u001b[32m2\u001b[39m\n",
+      "\u001b[31mTypeError\u001b[39m: 'tuple' object does not support item assignment"
      ]
     }
    ],
@@ -1074,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "ec5dc91e",
    "metadata": {
     "execution": {
@@ -1095,7 +1243,7 @@
        "        [3, 6]]))"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1118,7 +1266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "31fbaf86",
    "metadata": {
     "execution": {
@@ -1136,7 +1284,7 @@
        "       2.44948974])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1155,7 +1303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "c0e63f89",
    "metadata": {
     "execution": {
@@ -1172,7 +1320,7 @@
        "array([25,  4,  9, 16, 25, 36])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1191,7 +1339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "365d1248",
    "metadata": {
     "execution": {
@@ -1210,7 +1358,7 @@
        "       2.44948974])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1238,7 +1386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "e2756bb9",
    "metadata": {
     "execution": {
@@ -1252,24 +1400,25 @@
     {
      "data": {
       "text/plain": [
-       "array([ 0.63214394,  0.34288365,  0.85005043, -0.47385094,  0.32864279,\n",
-       "       -0.78796854,  0.51125391, -1.12947776, -0.71391649, -1.92367418,\n",
-       "       -0.42609273, -0.7309404 ,  0.83090301, -0.47570241, -0.5852834 ,\n",
-       "        0.89788488, -1.27638353,  2.59421427,  1.25973331, -0.61486026,\n",
-       "        1.3979914 ,  0.79368659, -1.14506316, -2.00869422,  0.19502313,\n",
-       "       -0.45956661,  0.65944298,  0.69723473, -1.09211872,  0.15972909,\n",
-       "       -0.22530418,  0.32276968,  0.50374192,  0.80506415, -0.48224507,\n",
-       "       -0.53214642, -1.09662644, -1.52722433, -2.10241781, -0.42403843,\n",
-       "        1.32310673,  2.00932483, -0.16726754,  2.30113584, -0.68976675,\n",
-       "       -0.67621251, -0.78371139, -0.62709974, -1.39453964,  0.25195549])"
+       "array([-0.4508872 ,  1.17883376, -0.32853253,  0.20756942, -0.32215492,\n",
+       "       -0.05519633, -0.8680871 , -1.11742288, -1.37391049, -1.46642031,\n",
+       "        0.42209949, -1.700909  ,  0.04053901,  1.24846134, -1.17289235,\n",
+       "       -1.67739525,  0.05835306, -0.39049108, -1.26939779, -0.48574264,\n",
+       "       -0.1772349 ,  2.06224408, -0.07338148,  1.43027811,  1.28497703,\n",
+       "        0.89882284, -0.49436552,  0.52077866, -0.23063094, -0.42880002,\n",
+       "       -0.40939347,  0.24736457, -1.13203337, -1.22495674,  0.33685895,\n",
+       "       -0.64899066, -0.10449238, -0.40618371,  0.43670778, -0.45498746,\n",
+       "        0.48986445,  0.08621589,  2.63460155,  0.33753548,  0.30457715,\n",
+       "       -0.78430298,  0.66188239, -0.40779866, -0.70139484, -0.90822623])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "import numpy as np\n",
     "x = np.random.normal(size=50)\n",
     "x\n"
    ]
@@ -1284,7 +1433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "d336dde1",
    "metadata": {
     "execution": {
@@ -1311,7 +1460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "cad26f2b",
    "metadata": {
     "execution": {
@@ -1325,11 +1474,11 @@
     {
      "data": {
       "text/plain": [
-       "array([[1.        , 0.71781515],\n",
-       "       [0.71781515, 1.        ]])"
+       "array([[1.        , 0.70871734],\n",
+       "       [0.70871734, 1.        ]])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1351,7 +1500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "a621aa6c",
    "metadata": {
     "execution": {
@@ -1367,8 +1516,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-0.97181857  3.2315884 ]\n",
-      "[ 0.73365791 -7.23683363]\n"
+      "[-6.01168657  9.41451861]\n",
+      "[-0.69335752 -8.33379176]\n"
      ]
     }
    ],
@@ -1418,7 +1567,7 @@
      "output_type": "stream",
      "text": [
       "[ 4.09482632 -1.07485605]\n",
-      "[ 4.09482632 -1.07485605]\n"
+      "[  4.09482632  -1.07485605 -10.15364596   1.13406146  -4.14030566]\n"
      ]
     }
    ],
@@ -1426,7 +1575,7 @@
     "rng = np.random.default_rng(1303)\n",
     "print(rng.normal(scale=5, size=2))\n",
     "rng2 = np.random.default_rng(1303)\n",
-    "print(rng2.normal(scale=5, size=2)) "
+    "print(rng2.normal(scale=5, size=5)) "
    ]
   },
   {
@@ -1502,7 +1651,7 @@
     {
      "data": {
       "text/plain": [
-       "(2.7243406406465125, 2.7243406406465125, 2.7243406406465125)"
+       "(3.027045156273903, 2.7243406406465125, 2.7243406406465125)"
       ]
      },
      "execution_count": 34,
@@ -1511,7 +1660,8 @@
     }
    ],
    "source": [
-    "np.var(y), y.var(), np.mean((y - y.mean())**2)"
+    "np.var(y,ddof=1), y.var(), np.mean((y - y.mean())**2)\n",
+    "#np.var?"
    ]
   },
   {
@@ -1525,7 +1675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 37,
    "id": "2499a37e",
    "metadata": {
     "execution": {
@@ -1539,16 +1689,16 @@
     {
      "data": {
       "text/plain": [
-       "(1.6505576756498128, 1.6505576756498128)"
+       "(1.6505576756498128, 1.7398405548422828)"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.sqrt(np.var(y)), np.std(y)"
+    "np.sqrt(np.var(y)), np.std(y,ddof=1)"
    ]
   },
   {
@@ -1562,7 +1712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "id": "3c308346",
    "metadata": {
     "execution": {
@@ -1588,7 +1738,7 @@
        "       [ 1.02130682, -0.95964476, -1.66861984]])"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1608,7 +1758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 40,
    "id": "7b5e8fcd",
    "metadata": {
     "execution": {
@@ -1625,7 +1775,7 @@
        "array([ 0.15030588,  0.14030961, -0.34238602])"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1639,12 +1789,12 @@
    "id": "d2617ab3",
    "metadata": {},
    "source": [
-    "The following yields the same result."
+    "The following yields the a different result."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 41,
    "id": "157d4a21",
    "metadata": {
     "execution": {
@@ -1659,24 +1809,17 @@
     {
      "data": {
       "text/plain": [
-       "array([ 0.15030588,  0.14030961, -0.34238602])"
+       "array([-0.13604387, -0.70466596,  0.40038349,  0.45675943, -0.04765406,\n",
+       "        0.73533095, -0.04256834,  0.39038958, -0.68884708, -0.53565259])"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "X.mean(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cdaef318",
-   "metadata": {},
-   "source": [
-    "    "
+    "X.mean(1)"
    ]
   },
   {
@@ -1714,7 +1857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 44,
    "id": "8b11e94e",
    "metadata": {
     "execution": {
@@ -1727,7 +1870,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1739,7 +1882,10 @@
    "source": [
     "from matplotlib.pyplot import subplots\n",
     "fig, ax = subplots(figsize=(8, 8))\n",
+    "rng = np.random.default_rng(1303)\n",
     "x = rng.standard_normal(100)\n",
+    "# testing with reseeding\n",
+    "rng = np.random.default_rng(1303)\n",
     "y = rng.standard_normal(100)\n",
     "ax.plot(x, y);\n"
    ]
@@ -1756,7 +1902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 45,
    "id": "04eb327c",
    "metadata": {
     "execution": {
@@ -1769,7 +1915,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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AKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQ0olBdt25dzJgxI6qqqqK+vj62b9/+ntZt3LgxJkyYEPPnzx/JwwIAcBopOVQ3bdoUzc3N0dLSEjt37oyZM2dGU1NTvPHGG++67vXXX49/+Id/iKuvvnrEmwUA4PRRcqg+/PDDceutt8aSJUviE5/4RKxfvz7OPvvseOKJJ064pr+/P2666aa4//7744ILLnhfGwYA4PRQUqj29fXFjh07orGx8Q93UFYWjY2N0d7efsJ1X/nKV2Ly5Mlx8803v6fH6e3tjZ6eniE3AABOLyWF6qFDh6K/vz9qa2uHjNfW1kZnZ+ewa1544YV4/PHHY8OGDe/5cVpbW6OmpmbwVldXV8o2AQA4BYzpp/4PHz4cCxcujA0bNsSkSZPe87oVK1ZEd3f34O3AgQNjuEsAADI6o5TJkyZNivLy8ujq6hoy3tXVFVOmTDlu/i9+8Yt4/fXXY968eYNjAwMDv3/gM86IV155JS688MLj1lVWVkZlZWUpWwMA4BRT0hXVioqKmD17drS1tQ2ODQwMRFtbWzQ0NBw3/+KLL46XXnopOjo6Bm+f/exn49prr42Ojg4v6QMAcEIlXVGNiGhubo7FixfHnDlzYu7cubF27do4cuRILFmyJCIiFi1aFNOnT4/W1taoqqqKSy65ZMj6c889NyLiuHEAAPj/Sg7VBQsWxMGDB2PVqlXR2dkZs2bNiq1btw5+wGr//v1RVuYPvAIA4P2ZUBRFcbI38cf09PRETU1NdHd3R3V19cneDgAA7zAWvebSJwAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICURhSq69atixkzZkRVVVXU19fH9u3bTzh3w4YNcfXVV8fEiRNj4sSJ0djY+K7zAQAgYgShumnTpmhubo6WlpbYuXNnzJw5M5qamuKNN94Ydv62bdvihhtuiB//+MfR3t4edXV18ZnPfCZ+9atfve/NAwBw6ppQFEVRyoL6+vq44oor4pFHHomIiIGBgairq4s777wzli9f/kfX9/f3x8SJE+ORRx6JRYsWvafH7OnpiZqamuju7o7q6upStgsAwDgYi14r6YpqX19f7NixIxobG/9wB2Vl0djYGO3t7e/pPt566604evRonHfeeSec09vbGz09PUNuAACcXkoK1UOHDkV/f3/U1tYOGa+trY3Ozs73dB933313TJs2bUjsvlNra2vU1NQM3urq6krZJgAAp4Bx/dT/mjVrYuPGjfHMM89EVVXVCeetWLEiuru7B28HDhwYx10CAJDBGaVMnjRpUpSXl0dXV9eQ8a6urpgyZcq7rn3ooYdizZo18cMf/jAuu+yyd51bWVkZlZWVpWwNAIBTTElXVCsqKmL27NnR1tY2ODYwMBBtbW3R0NBwwnUPPvhgPPDAA7F169aYM2fOyHcLAMBpo6QrqhERzc3NsXjx4pgzZ07MnTs31q5dG0eOHIklS5ZERMSiRYti+vTp0draGhER//zP/xyrVq2Kp556KmbMmDH4XtYPfehD8aEPfWgUnwoAAKeSkkN1wYIFcfDgwVi1alV0dnbGrFmzYuvWrYMfsNq/f3+Ulf3hQu03vvGN6Ovri8997nND7qelpSW+/OUvv7/dAwBwyir5e1RPBt+jCgCQ20n/HlUAABgvQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACkJFQBAEhJqAIAkJJQBQAgJaEKAEBKQhUAgJSEKgAAKQlVAABSEqoAAKQkVAEASEmoAgCQklAFACAloQoAQEpCFQCAlIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAIKURheq6detixowZUVVVFfX19bF9+/Z3nf/d7343Lr744qiqqopLL700tmzZMqLNAgBw+ig5VDdt2hTNzc3R0tISO3fujJkzZ0ZTU1O88cYbw85/8cUX44Ybboibb745du3aFfPnz4/58+fHz3/+8/e9eQAATl0TiqIoSllQX18fV1xxRTzyyCMRETEwMBB1dXVx5513xvLly4+bv2DBgjhy5Ej84Ac/GBz7i7/4i5g1a1asX7/+PT1mT09P1NTURHd3d1RXV5eyXQAAxsFY9NoZpUzu6+uLHTt2xIoVKwbHysrKorGxMdrb24dd097eHs3NzUPGmpqa4tlnnz3h4/T29kZvb+/gz93d3RHx+78BAADk83anlXgN9F2VFKqHDh2K/v7+qK2tHTJeW1sbe/bsGXZNZ2fnsPM7OztP+Ditra1x//33HzdeV1dXynYBABhn//3f/x01NTWjcl8lhep4WbFixZCrsG+++WZ8+MMfjv3794/aEyevnp6eqKuriwMHDnirx2nAeZ9enPfpxXmfXrq7u+P888+P8847b9Tus6RQnTRpUpSXl0dXV9eQ8a6urpgyZcqwa6ZMmVLS/IiIysrKqKysPG68pqbGP+inkerqaud9GnHepxfnfXpx3qeXsrLR+/bTku6poqIiZs+eHW1tbYNjAwMD0dbWFg0NDcOuaWhoGDI/IuL5558/4XwAAIgYwUv/zc3NsXjx4pgzZ07MnTs31q5dG0eOHIklS5ZERMSiRYti+vTp0draGhERd911V1xzzTXxta99La6//vrYuHFj/OxnP4vHHntsdJ8JAACnlJJDdcGCBXHw4MFYtWpVdHZ2xqxZs2Lr1q2DH5jav3//kEu+V155ZTz11FNx7733xj333BMf/ehH49lnn41LLrnkPT9mZWVltLS0DPt2AE49zvv04rxPL8779OK8Ty9jcd4lf48qAACMh9F7tysAAIwioQoAQEpCFQCAlIQqAAAppQnVdevWxYwZM6Kqqirq6+tj+/bt7zr/u9/9blx88cVRVVUVl156aWzZsmWcdspoKOW8N2zYEFdffXVMnDgxJk6cGI2NjX/0nw9yKfX3+20bN26MCRMmxPz588d2g4yqUs/7zTffjKVLl8bUqVOjsrIyLrroIv9O/wAp9bzXrl0bH/vYx+Kss86Kurq6WLZsWfzud78bp90yUj/5yU9i3rx5MW3atJgwYUI8++yzf3TNtm3b4lOf+lRUVlbGRz7ykXjyySdLf+AigY0bNxYVFRXFE088Ufznf/5nceuttxbnnntu0dXVNez8n/70p0V5eXnx4IMPFi+//HJx7733FmeeeWbx0ksvjfPOGYlSz/vGG28s1q1bV+zatavYvXt38bd/+7dFTU1N8V//9V/jvHNGotTzfttrr71WTJ8+vbj66quLv/7rvx6fzfK+lXrevb29xZw5c4rrrruueOGFF4rXXnut2LZtW9HR0THOO2ckSj3vb3/720VlZWXx7W9/u3jttdeK5557rpg6dWqxbNmycd45pdqyZUuxcuXK4umnny4ionjmmWfedf6+ffuKs88+u2hubi5efvnl4utf/3pRXl5ebN26taTHTRGqc+fOLZYuXTr4c39/fzFt2rSitbV12Pmf//zni+uvv37IWH19ffF3f/d3Y7pPRkep5/1Ox44dK84555ziW9/61lhtkVE0kvM+duxYceWVVxbf/OY3i8WLFwvVD5BSz/sb3/hGccEFFxR9fX3jtUVGUannvXTp0uIv//Ivh4w1NzcXV1111Zjuk9H1XkL1S1/6UvHJT35yyNiCBQuKpqamkh7rpL/039fXFzt27IjGxsbBsbKysmhsbIz29vZh17S3tw+ZHxHR1NR0wvnkMZLzfqe33norjh49Guedd95YbZNRMtLz/spXvhKTJ0+Om2++eTy2ySgZyXl///vfj4aGhli6dGnU1tbGJZdcEqtXr47+/v7x2jYjNJLzvvLKK2PHjh2Dbw/Yt29fbNmyJa677rpx2TPjZ7RareQ/mWq0HTp0KPr7+wf/ZKu31dbWxp49e4Zd09nZOez8zs7OMdsno2Mk5/1Od999d0ybNu24XwDyGcl5v/DCC/H4449HR0fHOOyQ0TSS8963b1/86Ec/iptuuim2bNkSe/fujTvuuCOOHj0aLS0t47FtRmgk533jjTfGoUOH4tOf/nQURRHHjh2L22+/Pe65557x2DLj6ESt1tPTE7/97W/jrLPOek/3c9KvqEIp1qxZExs3boxnnnkmqqqqTvZ2GGWHDx+OhQsXxoYNG2LSpEknezuMg4GBgZg8eXI89thjMXv27FiwYEGsXLky1q9ff7K3xhjYtm1brF69Oh599NHYuXNnPP3007F58+Z44IEHTvbWSOqkX1GdNGlSlJeXR1dX15Dxrq6umDJlyrBrpkyZUtJ88hjJeb/toYceijVr1sQPf/jDuOyyy8Zym4ySUs/7F7/4Rbz++usxb968wbGBgYGIiDjjjDPilVdeiQsvvHBsN82IjeT3e+rUqXHmmWdGeXn54NjHP/7x6OzsjL6+vqioqBjTPTNyIznv++67LxYuXBi33HJLRERceumlceTIkbjtttti5cqVUVbm+tmp4kStVl1d/Z6vpkYkuKJaUVERs2fPjra2tsGxgYGBaGtri4aGhmHXNDQ0DJkfEfH888+fcD55jOS8IyIefPDBeOCBB2Lr1q0xZ86c8dgqo6DU87744ovjpZdeio6OjsHbZz/72bj22mujo6Mj6urqxnP7lGgkv99XXXVV7N27d/B/SCIiXn311Zg6dapITW4k5/3WW28dF6Nv/0/K7z+jw6li1FqttM95jY2NGzcWlZWVxZNPPlm8/PLLxW233Vace+65RWdnZ1EURbFw4cJi+fLlg/N/+tOfFmeccUbx0EMPFbt37y5aWlp8PdUHSKnnvWbNmqKioqL43ve+V/z6178evB0+fPhkPQVKUOp5v5NP/X+wlHre+/fvL84555zi7//+74tXXnml+MEPflBMnjy5+Kd/+qeT9RQoQann3dLSUpxzzjnFv/3bvxX79u0r/v3f/7248MILi89//vMn6ynwHh0+fLjYtWtXsWvXriIiiocffrjYtWtX8ctf/rIoiqJYvnx5sXDhwsH5b3891T/+4z8Wu3fvLtatW/fB/XqqoiiKr3/968X5559fVFRUFHPnzi3+4z/+Y/CvXXPNNcXixYuHzP/Od75TXHTRRUVFRUXxyU9+sti8efM475j3o5Tz/vCHP1xExHG3lpaW8d84I1Lq7/f/J1Q/eEo97xdffLGor68vKisriwsuuKD46le/Whw7dmycd81IlXLeR48eLb785S8XF154YVFVVVXU1dUVd9xxR/E///M/479xSvLjH/942P8Wv32+ixcvLq655prj1syaNauoqKgoLrjgguJf//VfS37cCUXhWjsAAPmc9PeoAgDAcIQqAAApCVUAAFISqgAApCRUAQBISagCAJCSUAUAICWhCgBASkIVAICUhCoAACkJVQAAUhKqAACk9H+BS2ylrk0bNwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1794,7 +1940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 46,
    "id": "e678d266",
    "metadata": {
     "execution": {
@@ -1808,7 +1954,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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tlaDWVNSMZ1NTUzQ1NZW7DAAoymxbJhXbWglqTUXNeAJANVrT3nLUlkmpOHi6fU17S5JlQcUp6YznT3/603jiiSemfh8eHo5HH300Wlpa4pRTTinlRwNASRx4KR9//dBT8eORn8WpLYvjPWtPi0ULGqKnuyM2bN1xRMukyTDa093hIBF1L1UoFKY7hDcvvvWtb8W6deuOeP2aa66Ju++++5jPj46ORiaTiVwuF0uXLi1BhQAwe7f9/VD8xT8Px6H/5mxIRVz/pvbYdEXHjH08e7o7oqtTY3hqUzF5raQznpdeemmUMNcCQGKu/6vvxdeH9h7xer4Q8bkHhyMiYtMVHXF5R2sMDI9omQTTqKjDRQBQib726K5pQ+ehPv/t4fjwr/0fsWhBg5ZJMAOHiwDgKCbyhdj0lR8cc1y+EPHXDz1V+oKgigmeAHAUA8MjsW9sYlZjfzzysxJXA9VN8ASAoyim6fupLYtLWAlUP3s8AeAVJvKFqQNCz+8bn9UzDamI96w9rbSFQZUTPAHgENt3ZuNj2wZjZP+BqdcaUgf3cB7NdW9sj0ULLCTC0QieAPCyzduHplojHepYofPyjhXxB2/tKFFVUDsETwCIiO07d08bOg/1ypnP5nRjbL7qrPiNc19X4uqgNgieANS9iXwh/ue9O485Ll+IuPWtZ8YJzU2aw8McCJ4A1L2Hn3whfjo+u5ZJJzQ3xZVmOGFO7IIGoO499KPnZz12RXO6hJVAbRM8ASBmt1z+mqbGWNPeUuJaoHYJngDUvdnerf67bzzdnk54FezxBKCuHNocfvKA0BtOXxbHL14YP/nZizM+t6SpMW5+8+sTrBRqj+AJQN3oH8xGb99QZHO/uAazLZOOnu6O+MQ7zor3b90x47P/++pzzHbCq2SpHYC60D+YjQ1bdxwWOiMi9uTGYsPLgfOz61dH69LDDw+1ZdLx2fWro6uzLbFaoVaZ8QSg5k3kC9HbNxTTXUBUiINHi3r7huKfP/qrcXlH6xFL8WY6YX4IngDUvIHhkSNmOg9ViIhsbiwGhkdi7aplsz5sBBTHUjsANW/vvplD51zGAXNjxhOAmnTo6fXn943P6hnN4aG0BE8Aas50p9cbUgfvWp9OKiJaM2nN4aHEBE8Aasrk6fVXZsyjhc6IiJ7uDoeIoMTs8QSgZhzt9PqkV2bL1kw6tmiXBIkw4wlAzTjW6fWIgzOft771zDihuUm7JEiY4AlA1Zs8SPQPg9lZjT+huSmuPPd1Ja4KeCXBE4CqNt1BomNxeh3KQ/AEoGrNdJBoJk6vQ3k5XARAVZrNQaJDOb0O5WfGE4CqNJuDRIdqzaSjp7vD6XUoI8ETgKo02+st37v21HhLZ5vT61ABBE8AqtJsDwi9pbMt1q5aVuJqgNmwxxOAqrSmvSXaMumYaQ4zFRFtDhJBRRE8AahKjQ2p6OnuiIg4Inw6SASVSfAEoGp1dbbFlvWrozVz+LK7azChMtnjCUBV6+psi8s7WmNgeCT27htzDSZUMMETgKrX2JBygAiqgOAJQMWZvHvdDCbUFsETgIoy3d3rbZq/Q01wuAiAirF9Zzbev3XHETcS7cmNxYatO6J/MFumyoD5IHgCUBG279wdN/3Njmnfm7yPvbdvKCbys72dHag0gicAZdc/mI0P3PNIHC1TFiIimxuLgeGRxOoC5pfgCUBZTeQL0ds3NOvxs72jHag8gicAZTUwPHLEns6jme0d7UDlcaodgLKYbJn0D0UcGHL3OlQ3wROAxE3XMmk23L0O1U3wBCBR23fujg/c80hRzzSkIm5/t7vXodrZ4wlAYrbvzMZNf1Nc6IyIuP3d58UVZwudUO3MeAKQiO07s/GBe6bv0zkTNxZBbRE8ASi5g83hZz/T+d61p8ZbOtvc0Q41RvAEoKQmm8MX4y2dbbF21bISVQSUiz2eAJRMsc3hI7RMglomeAJQEhP5Qtz9nWEtk4ApltoBmHdz6dN5sGXSeQ4SQQ0TPAGYV/2D2diwdUcUinzu9nev1jIJapyldgDmzYGX8nHLfT8oKnQ2pCL+/LeFTqgHgicA86J/MBtv2Hx/jOx/sajnNIeH+mGpHYBXbS7L65rDQ/0RPAF4VSZbJhUTOm9965lx7cXtTq9DnRE8AZizYlsmpSKiNZMWOqFOCZ4AzMn2nbvjY9sGi97TqU8n1C/BE4Cibd4+FJ97cLioZ1qWLIyPv/0sezqhjgmeABSl7193Fx06ly1ZFA9tenMsWqCZCtQzwROAWdu+c3f89799ZNbjJxfUb3t7p9AJCJ4AzE7/YDY+cM/sQ2fEwYNEWiYBkwRPAI5psmVSMbRMAl7JugcAR1Vsy6SIgweJhE7glcx4AjCj/sFs9PYNFRU6IyL++MpOoRM4guAJwLTmcg1mRMQNl7THFWefVJKagOomeAJwhLlcg5lKRfzZu86L7nOETmB6gicAh5nLns6IiDvefZ6ZTuCoBE8ApsxlT2eblknALAmeAETE3PZ0apkEFEPwBKDoPZ2pONgcXugEiiF4AtSxnx+YiI9vH4pHn/nJrJfXJ2NmT3eH0AkURfAEqFPX/9X34utDe4t+zjWYwFwJngB16Hf/ciD+6d+fK/o5ezqBV0PwBKgz2x7ZVXTotKcTmA/uageoI/2D2fj9Lz5a1DP2dALzxYwnQJ2YPLleLHs6gfkieALUiYHhkaIaw7cuTcef/Na5saa9xUwnMC8ET4A6sXdfcVdgXnPRqbF21bISVQPUI3s8AerEiuZ0UeOve+PpJaoEqFeCJ0CdWNPeEm2ZdMxm0fyGS9pj0QL/igDmlz9VAOpEY0Mqero7IiKOGj5vuKQ9Nl3RkUxRQF1JJHjecccdcdppp0U6nY4LL7wwBgYGkvhYAF6hq7MttqxfHa2Zw5fdFy9qjHeufl38xx+/RegESqbkh4u++MUvxoc+9KH47Gc/GxdeeGF8+tOfjl//9V+Pxx9/PFasWFHqjwfgFbo62+LyjtYYGB6JvfvGYkVz2sl1IBGpQqFQKOUHXHjhhXHBBRfE7bffHhER+Xw+Vq5cGTfffHNs3LjxqM+Ojo5GJpOJXC4XS5cuLWWZADVhIl8QKIFEFZPXSjrjeeDAgfj+978fmzZtmnqtoaEhLrvssnjooYeOGD8+Ph7j4+NTv4+OjpayPICa0j+Yjd6+ocN6dbZp/g5UkJLu8Xz++edjYmIiTjzxxMNeP/HEE2PPnj1HjN+8eXNkMpmpn5UrV5ayPICaMJEvxJ/+03/E+7fuOKJB/J7cWGzYuiP6B7Nlqg7gFyrqVPumTZsil8tN/TzzzDPlLgmgovUPZuPiT3wj/uSffjjt+5N7qXr7hmIiX9KdVQDHVNKl9hNOOCEaGxvj2WefPez1Z599NlpbW48Y39TUFE1NTaUsCaBm9A9m4/1bdxxzXCEisrmxGBgecRMRUFYlnfFctGhRnH/++XH//fdPvZbP5+P++++PtWvXlvKjAWraRL4QG7/8g6KeKfbKTID5VvJ2Sh/60IfimmuuiV/5lV+JNWvWxKc//enYv39/vO997yv1RwPUrId/9EL85GcvFvVMsVdmAsy3kgfP3/qt34rnnnsu/vAP/zD27NkT5557bvT39x9x4AiA2ZnIF+Lv/r/Z74FPRURr5mBrJYByKnkfz1dDH0+AX5g8vX7nt38UYy/mZ/1cKiK2rF+tpRJQEhXTxxOA+dE/mI2b/+aReHGiuLmC1y5eEJvfcbbQCVQEwROgws329PorvaZpQXz3lstj0YKK6pwH1DF/GgFUsIl8IT76dzvn9Oynrj5b6AQqij+RACrYw0++ELmxl4p6pnVpU3zWnk6gAllqB6hgD/3o+aLG3/rWM+Pai9ujsSFVoooA5s6MJ0BFm32AXJpeIHQCFc2MJ0AFmcgXYmB4JPbuG4sVzem4sL0lbv/m7J79+FVnCZ1ARRM8ASpE/2A2evuGIpv7xdWWrUubYsmixth/YOKoz1525vL4jXNPKnWJAK+K4AlQAfoHs7Fh6454ZZfOZ0fHj3jtlS47c0X8xTUXlKo0gHkjeAKU2US+EBu//INpA2YhDu7yzCxeGOkFjbFn9BezoUvTC+K2t58V3eeY6QSqg+AJUGa3f+OH8ZOfvTjj+4WI+MnPXowvXLc6GhpSU/s/17S32NMJVBXBE6CMDryUjzsf/NGsxj6/fzyuPPd1Ja4IoHS0UwIok/7BbLxh8/3HPDg0aUVzusQVAZSWGU+AMpjpMNFMjl+8MNa0t5S0JoBSEzwBEjSRL8TDT74QG++d/jDRTN53kcbwQPUTPAESMl2fztk4fvHCuOlXzyhRVQDJETwBElDs0vqhPvEONxIBtcHhIoASm8gXordvqOjQ2bJkYXx2/ero6mwrSV0ASTPjCVBiD//ohaKX15ctWRQPbXpzLFpgfgCoHYInQAn1D2Zj470/mPX4yQX1297eKXQCNUfwBCiRuezrbM2ko6e7w/I6UJMET4ASOPBSPm65b3D2fTqPWxh3/LfV8YbTlzlIBNQswRNgnvUPZuOW+34QI/tnvn/9UKmI+MRvnhUXn3FCaQsDKDPBE2CeTOQLcfs3fhh/8k8/nPUzxy9eGJ94x1mW1oG6IHgCzIPtO3fHH3xlMP7rZ7Ob5Zx0x7tXx8WvN9MJ1AfBE+BV2rx9KD734HBRz6Ti4EGiN6xaVpqiACqQXh0Ar8L2ndmiQ+eknu4OB4mAuiJ4AszRzw9MxIf/30eLfm7ZkkWxxY1EQB2y1A4wB5u3D8Wd3x6OQpH3YLYsWehGIqBuCZ4ARZrLns6Ig/s6P/72s4ROoG750w+gCAdeysedcwidrUubLK8Ddc+MJ8AsTeQLcf1ffa+oKzAjIn7/za+P//7m1ztIBNQ9wRNgFvoHs/F/f/XfYs/oeFHP3XBJe/yPy3+pRFUBVBfBE+AY+gezsWHrjqJmOhcvbIhPXX1uXHG2pXWASYInwFFM5AvR2zdU9PL692/9tThuUWNJagKoVg4XAcxgIl+Iu78zHNncWFHPdZ/dKnQCTMOMJ8A0tu/cHR/bNhgj+4u7e31JU2N8+l2rS1QVQHUTPAFe4ba//7f4/LefKvq5VET876vPcXodYAaCJ8Ahbvv7oTmFzrZMOnq6O/TpBDgKwRPgZdt3ZuPz3y6+Ofz/uOz1cdOv6tMJcCyCJ0AcPEj0P+/dWdQzZjkBiiN4AkTE7d/4Yfx0/KVZj7/1rWfGtRe3m+UEKIJ2SkDdm8gX4q7vPDXr8S1LFgqdAHMgeAJ1b2B4JH7y89m3TfrjKzuFToA5sNQO1KWJfCEGhkdi776x+OGzP531c9e/6bS44uyTSlgZQO0SPIG60z+Yjd6+oaJvJPqNs9riD976f5aoKoDaJ3gCdaV/MBsbtu4o+u714xcvjD9993klqQmgXtjjCdSNiXwhevuGig6dERGfeMdZ9nUCvEqCJ1A3BoZHil5eb8uk47PrV+vVCTAPLLUDdWPvvtmFzpvWrYrXn9gcK5rTsaa9xUwnwDwRPIG6saI5PatxF5+xPNauWlbiagDqj6V2oG6saW+Jtkw6Zpq/TMXBpfU17S1JlgVQNwRPoCZN5Avx0JMvxLZHd8VDT74QE/lCNDakoqe7IyLiiPA5+XtPd4eldYASsdQO1JztO7PxsW2DMbL/wNRrbZl09HR3RFdnW2xZv/qIPp6th7wPQGmkCoXCXDqLJGJ0dDQymUzkcrlYunRpucsBqsDm7UPxuQeHp30vFRFbXj6hfujNRQ4RAcxdMXnNjCdQM7726K4ZQ2dERCEievuG4vKO1mhsSDlABJAwezyBmvC1R3fHTX/76DHHZXNjMTA8UvqCADiCGU+g6h1teX06s+3nCcD8MuMJVLXtO7NFhc6I2ffzBGB+CZ5A1ZrIF+Jj2waLembZkkX6dAKUieAJVK2B4ZHDWibNxh9d2en0OkCZ2OMJVJ0DL+Xjrx96Kr71H88V9dwNl7THFWfr0wlQLoInUFU2bx+Kz397OPJFdiC+/V3nxm+c+7rSFAXArAieQNUo9vT6pNvfdV78xrknlaAiAIphjydQFX5+YCLu/HbxofOGS9qFToAKYcYTqHj9g9n4yN/tjGIu+F22ZFH80ZWd9nQCVBDBE6ho23dm4wP37Jj1+De9/oT4wKVnuHsdoAIJnkDF2r5zd9z0N48U9cylv7TcHewAFUrwBCrS9p274wP3FBc6G1IR71l7WmkKAuBVc7gIqDh9/7o7bixypjMi4vo3tceiBf5YA6hUZjyBijKXlkmpVMTvvak9Nl3RUaKqAJgPgidQMbbv3F106FyaXhDfveWyOG5RY4mqAmC+WJMCKsJEvhAf2zZY9HP/651nC50AVULwBCrCwPBIjOx/cdbjG1IRf/7bq6OrU59OgGoheAIVYe++saLG3/7u8zSHB6gygidQEVY0p2c1LpWK+PPfPi+uONs1mADVRvAEKsKa9pZoyxw7fP7Zu4ROgGoleAKJm8gX4qEnX4htj+6Kh558ISbyhWhsSEVPd0cc7ZLLGy5pj+5zhE6AaqWdEpCo/sFs9PYNRTb3iz2dbZl09HR3RFdnW2xZv/qI91uWLIw/vrLTTCdAlUsVCoVCuYuYyejoaGQymcjlcrF06dJylwO8Sv2D2diwdUe88g+dyVnOLesPnlKfyBdiYHgk9u4bixXN6VjT3hKNDUebCwWgXIrJa2Y8gUQceCkft9z3gyNCZ0REIQ6Gz96+obi8ozUaG1KxdtWyhCsEoNTs8QRKrn8wG2/YfP9R+3QWIiKbG4uB4ZHkCgMgUWY8gZKaaXl9JsX28wSgepRsxvO2226Liy66KBYvXhzHH398qT4GqGAT+UL09g3NOnRGzL6fJwDVp2TB88CBA3H11VfHhg0bSvURQIUbGB457HT60aTi4On2Ne0tpS0KgLIp2VJ7b29vRETcfffdpfoIoMIVu2ze093h9DpADauoPZ7j4+MxPj4+9fvo6GgZqwHm4ucHJuLj24fiqRd+FukFs1tUaVmyMD7+9rOiq9Pd6wC1rKKC5+bNm6dmSoHqc/1ffS++PrS3qGeWLVkUD216cyyaZUgFoHoV9Sf9xo0bI5VKHfXnsccem3MxmzZtilwuN/XzzDPPzPnvBSTrd/9yoKjQmXr557a3dwqdAHWiqBnPD3/4w3Httdcedczpp58+52Kampqiqalpzs8D5bHtkV3xT//+XFHPtB5yTSYA9aGo4Ll8+fJYvnx5qWoBqlD/YDZ+/4uPzmrs5WeuiN845yTXYALUqZLt8Xz66adjZGQknn766ZiYmIhHH300IiLOOOOMeM1rXlOqjwUSNNmnc7bGXsrHlee+roQVAVDJShY8//AP/zD+8i//cur38847LyIivvnNb8all15aqo8FElRMn86IiNOWLS5hNQBUupLt6L/77rujUCgc8SN0Qu0otk/nLVd0lKgSAKqBo6TAnBVzveXlHSviuEWNJawGgEpXUX08gco2kS/EwPBI7N03Fiua03H+qa+Ntkw69uTGjnof+2VnLo/Pv/eCxOoEoDIJnsCs9A9mo7dv6LA9nW2ZdLztnLa488HhSEVMGz7/9LfOjSvPc6AIAEvtwCz0D2Zjw9YdRxwk2pMbizsfHI7fu6Q9WjOHL7u3ZdLx2fWrhU4AppjxBI7qwEv5uOW+wWlnMwtx8Pahr/5rNh74yLr4/o//a2oZXp9OAF5J8ARmtH3n7vjol3fGvrGJGccUIiKbG4vv//i/Yu2qZckVB0DVETyBad3290Px+W8Pz3p8sa2VAKg/9ngCRyg2dEYU11oJgPpkxhM4zPadu4sKnamIaM0c3NMJAEdjxhOYMpEvxMe2DRb9XE93h4NEAByT4AlMGRgeiZH9L856/LIli2LL+tXR1dlWwqoAqBWW2oEpxRwQak43xkOb3hyLFvj/rwDMjn9jAFOKOSD0/7zjbKETgKL4twYwZU17S7Rljh0+r39Te1xx9kkJVARALRE8gSmNDano6e6Iox0Tuv5N7fEHb+1IrCYAaofgCRymq7MttqxffcTMZ8uShfHnv32e0AnAnDlcBByhq7MtLu9ojYHhEXevAzBvBE9gWo0NKXevAzCvLLUDAJAIM55QBybyBcvmAJSd4Ak1rn8wG719Q5HN/aI5fFsmHT3dHW4cAiBRltqhhvUPZmPD1h2Hhc6IiD25sdiwdUf0D2bLVBkA9UjwhBo1kS9Eb99QFKZ5b/K13r6hmMhPNwIA5p/gCTVqYHjkiJnOQxUiIpsbi4HhkeSKAqCuCZ5Qo/bumzl0zmUcALxagifUqBXNx75zvZhxAPBqCZ5Qo9a0t0RbJj3jveupOHi6fU17S5JlAVDHBE+oUY0NqejpPniv+ivD5+TvPd0d+nkCkBjBE2pYV2dbbFm/Olozhy+nt2bSsWX9an08AUiUBvJQxWZzI1FXZ1tc3tHq5iIAyk7whCpVzI1EjQ2pWLtqWdIlAsBhLLVDFXIjEQDVSPCEKuNGIgCqleAJVcaNRABUK8ETqowbiQCoVoInVBk3EgFQrQRPqDJuJAKgWgmeUGXcSARAtRI8oQq5kQiAaqSBPFQpNxIBUG0ET6hibiQCoJpYagcAIBGCJwAAiRA8AQBIhOAJAEAiBE8AABLhVDuUyUS+oBUSAHVF8IQy6B/MRm/fUGRzY1OvtWXS0dPdofk7ADXLUjskrH8wGxu27jgsdEZE7MmNxYatO6J/MFumygCgtARPSMhEvhDfeeL52HjvD6IwzfuTr/X2DcVEfroRAFDdLLVDAqZbWp9OISKyubEYGB5xIxEANUfwhBKbXFovZg5z776jB1QAqEaW2qGEJvKF6O0bKip0RkSsaE6XpB4AKCcznlBCA8Mjx1xeP1QqIlozB1srAUCtMeMJJVTMkvlkB8+e7g79PAGoSWY8YZ4d2hj++X3js36uVR9PAGqc4AnzaLrT6w2piKN1Rzp+8cK4492r4w2rlpnpBKCmCZ4wT2Y6vT5T6JyMmJ94x1lx8etPKGVpAFAR7PGEeTCb0+uvnMxszaRjy/rVltYBqBtmPGEezOb0er4Qcetbz4wTmptiRfPBk+uW1gGoJ4InzIPZnl4/obkprjz3dSWuBgAqk6V2mAezbfiuMTwA9UzwhHmwpr0l2jLpmGnhPBURbRrDA1DnBE+YB40Nqejp7oiIOCJ8agwPAAcJnjBPujrbYsv61dGaOXw53el1ADjI4SKYR12dbXF5R+vUzUVOrwPALwieMM8aG1KxdtWycpcBABXHUjsAAIkQPAEASITgCQBAIgRPAAASIXgCAJAIwRMAgEQIngAAJELwBAAgEYInAACJEDwBAEiE4AkAQCIETwAAEiF4AgCQCMETAIBECJ4AACRC8AQAIBGCJwAAiShZ8Hzqqafiuuuui/b29jjuuONi1apV0dPTEwcOHCjVRwIAUMEWlOpv/Nhjj0U+n4/Pfe5zccYZZ8Tg4GBcf/31sX///vjUpz5Vqo+FKRP5QgwMj8TefWOxojkda9pborEhVe6yAKBupQqFQiGpD/vkJz8ZW7ZsiR/96EfTvj8+Ph7j4+NTv4+OjsbKlSsjl8vF0qVLkyqTGtA/mI3evqHI5samXmvLpKOnuyO6OtvKWBkA1JbR0dHIZDKzymuJ7vHM5XLR0tIy4/ubN2+OTCYz9bNy5coEq6NW9A9mY8PWHYeFzoiIPbmx2LB1R/QPZstUGQDUt8SC5xNPPBGf+cxn4oYbbphxzKZNmyKXy039PPPMM0mVR42YyBeit28oppvGn3ytt28oJvKJTfQDAC8rOnhu3LgxUqnUUX8ee+yxw57ZtWtXdHV1xdVXXx3XX3/9jH/vpqamWLp06WE/UIyB4ZEjZjoPVYiIbG4sBoZHkisKAIiIORwu+vCHPxzXXnvtUcecfvrpU3+9e/fuWLduXVx00UVx5513Fl0gFGPvvplD51zGAQDzp+jguXz58li+fPmsxu7atSvWrVsX559/ftx1113R0KBtKKUxeYL9h8/+dFbjVzSnS1wRAPBKJWuntGvXrrj00kvj1FNPjU996lPx3HPPTb3X2tpaqo+lDk13gn0mqYhozRxsrQQAJKtkwfPrX/96PPHEE/HEE0/EySeffNh7CXZwosZNnmCfzT9Rkx08e7o79PMEgDIo2dr3tddeG4VCYdofmA9HO8E+ndZMOrasX62PJwCUSclmPKGUJvKFuPs7w7NaXr9p3aq4+Izlbi4CgDITPKk6xezpjIh4/YnNsXbVshJXBQAci+BJVSlmT+ckJ9gBoDIInlSNYvd0OsEOAJVFY02qxrFuJTqUE+wAUHnMeFI1irltqDWTjp7uDifYAaCCCJ5Ujdnu1bz1rWfGtRe3m+kEgApjqZ2qsaa9Jdoy6ZgpTqYioi2TFjoBoEIJnlSNxoZU9HR3REQcET7t6QSAyid4UlW6Ottiy/rV0Zo5fNndrUQAUPns8aTqdHW2xeUdrTEwPBJ7943Fiua0W4kAoAoInlSlxoaU24gAoMpYagcAIBGCJwAAiRA8AQBIhOAJAEAiBE8AABIheAIAkAjBEwCARAieAAAkQvAEACARgicAAIlwZSYlM5EvuE8dAJgieFIS/YPZ6O0bimxubOq1tkw6ero7oquzrYyVAQDlYqmdedc/mI0NW3ccFjojIvbkxmLD1h3RP5gtU2UAQDkJnsyriXwhevuGojDNe5Ov9fYNxUR+uhEAQC0TPJlXA8MjR8x0HqoQEdncWAwMjyRXFABQEQRP5tXefTOHzrmMAwBqh+DJvFrRnJ7XcQBA7RA8mVdr2luiLZOOmZompeLg6fY17S1JlgUAVADBk3nV2JCKnu6OiIgjwufk7z3dHfp5AkAdEjyZd12dbbFl/epozRy+nN6aSceW9av18QSAOqWBPCXR1dkWl3e0urkIAJgieFIyjQ2pWLtqWbnLAAAqhKV2AAASIXgCAJAIwRMAgEQIngAAJELwBAAgEYInAACJEDwBAEiE4AkAQCIETwAAEiF4AgCQCMETAIBECJ4AACRC8AQAIBGCJwAAiRA8AQBIhOAJAEAiBE8AABIheAIAkAjBEwCARAieAAAkQvAEACARgicAAIkQPAEASITgCQBAIgRPAAASIXgCAJAIwRMAgEQIngAAJELwBAAgEYInAACJEDwBAEiE4AkAQCIETwAAEiF4AgCQCMETAIBECJ4AACRC8AQAIBGCJwAAiRA8AQBIhOAJAEAiBE8AABIheAIAkAjBEwCARAieAAAkQvAEACARgicAAIkQPAEASITgCQBAIgRPAAASUdLg+ba3vS1OOeWUSKfT0dbWFu95z3ti9+7dpfxIAAAqVEmD57p16+JLX/pSPP7443HvvffGk08+Ge985ztL+ZEAAFSoVKFQKCT1YV/96lfjqquuivHx8Vi4cOExx4+OjkYmk4lcLhdLly5NoEIAAIpRTF5bkFBNMTIyEl/4whfioosumjF0jo+Px/j4+NTvo6OjSZUHAECJlfxw0Uc/+tFYsmRJLFu2LJ5++unYtm3bjGM3b94cmUxm6mflypWlLg8AgIQUHTw3btwYqVTqqD+PPfbY1PiPfOQj8cgjj8Q//uM/RmNjY7z3ve+NmVb3N23aFLlcburnmWeemft/MwAAKkrRezyfe+65eOGFF4465vTTT49FixYd8fp//ud/xsqVK+Nf/uVfYu3atcf8LHs8AQAqW0n3eC5fvjyWL18+p8Ly+XxExGH7OAEAqA8lO1z03e9+N773ve/FG9/4xnjta18bTz75ZNx6662xatWqWc12AgBQW0p2uGjx4sXx5S9/Od785jfHL//yL8d1110XZ599djzwwAPR1NRUqo8FAKBClWzG86yzzopvfOMbpfrbAwBQZdzVDgBAIgRPAAASIXgCAJAIwRMAgEQIngAAJELwBAAgEYInAACJEDwBAEiE4AkAQCIETwAAEiF4AgCQCMETAIBELCh3AZViIl+IgeGR2LtvLFY0p2NNe0s0NqTKXRYAQM0QPCOifzAbvX1Dkc2NTb3WlklHT3dHdHW2lbEyAIDaUfdL7f2D2diwdcdhoTMiYk9uLDZs3RH9g9kyVQYAUFvqOnhO5AvR2zcUhWnem3ytt28oJvLTjQAAoBh1HTwHhkeOmOk8VCEisrmxGBgeSa4oAIAaVdfBc+++mUPnXMYBADCzug6eK5rT8zoOAICZ1XXwXNPeEm2ZdMzUNCkVB0+3r2lvSbIsAICaVNfBs7EhFT3dHRERR4TPyd97ujv08wQAmAd1HTwjIro622LL+tXRmjl8Ob01k44t61fr4wkAME80kI+D4fPyjlY3FwEAlJDg+bLGhlSsXbWs3GUAANSsul9qBwAgGYInAACJEDwBAEiE4AkAQCIETwAAEiF4AgCQCMETAIBECJ4AACRC8AQAIBGCJwAAiRA8AQBIhOAJAEAiBE8AABIheAIAkAjBEwCARAieAAAkQvAEACARgicAAIkQPAEASITgCQBAIhaUu4CjKRQKERExOjpa5koAAJjOZE6bzG1HU9HBc9++fRERsXLlyjJXAgDA0ezbty8ymcxRx6QKs4mnZZLP52P37t3R3NwcqVSq3OXMi9HR0Vi5cmU888wzsXTp0nKXU1d89+Xjuy8f3335+O7Lx3efrEKhEPv27YuTTjopGhqOvouzomc8Gxoa4uSTTy53GSWxdOlS/2MoE999+fjuy8d3Xz6++/Lx3SfnWDOdkxwuAgAgEYInAACJEDwT1tTUFD09PdHU1FTuUuqO7758fPfl47svH999+fjuK1dFHy4CAKB2mPEEACARgicAAIkQPAEASITgCQBAIgRPAAASIXiW0dve9rY45ZRTIp1OR1tbW7znPe+J3bt3l7usmvfUU0/FddddF+3t7XHcccfFqlWroqenJw4cOFDu0urCbbfdFhdddFEsXrw4jj/++HKXU/PuuOOOOO200yKdTseFF14YAwMD5S6p5j344IPR3d0dJ510UqRSqfjKV75S7pLqxubNm+OCCy6I5ubmWLFiRVx11VXx+OOPl7ssDiF4ltG6deviS1/6Ujz++ONx7733xpNPPhnvfOc7y11WzXvssccin8/H5z73ufi3f/u3+JM/+ZP47Gc/G7fccku5S6sLBw4ciKuvvjo2bNhQ7lJq3he/+MX40Ic+FD09PbFjx44455xz4td//ddj79695S6tpu3fvz/OOeecuOOOO8pdSt154IEH4sYbb4yHH344vv71r8eLL74Yv/Zrvxb79+8vd2m8TB/PCvLVr341rrrqqhgfH4+FCxeWu5y68slPfjK2bNkSP/rRj8pdSt24++6744Mf/GD85Cc/KXcpNevCCy+MCy64IG6//faIiMjn87Fy5cq4+eabY+PGjWWurj6kUqm477774qqrrip3KXXpueeeixUrVsQDDzwQl1xySbnLIcx4VoyRkZH4whe+EBdddJHQWQa5XC5aWlrKXQbMmwMHDsT3v//9uOyyy6Zea2hoiMsuuyweeuihMlYGycnlchER/nyvIIJnmX30ox+NJUuWxLJly+Lpp5+Obdu2lbukuvPEE0/EZz7zmbjhhhvKXQrMm+effz4mJibixBNPPOz1E088Mfbs2VOmqiA5+Xw+PvjBD8bFF18cnZ2d5S6Hlwme82zjxo2RSqWO+vPYY49Njf/IRz4SjzzySPzjP/5jNDY2xnvf+96w+2Fuiv3uIyJ27doVXV1dcfXVV8f1119fpsqr31y+e4BSuvHGG2NwcDD+9m//ttylcIgF5S6g1nz4wx+Oa6+99qhjTj/99Km/PuGEE+KEE06IX/qlX4ozzzwzVq5cGQ8//HCsXbu2xJXWnmK/+927d8e6devioosuijvvvLPE1dW2Yr97Su+EE06IxsbGePbZZw97/dlnn43W1tYyVQXJuOmmm+JrX/taPPjgg3HyySeXuxwOIXjOs+XLl8fy5cvn9Gw+n4+IiPHx8fksqW4U893v2rUr1q1bF+eff37cdddd0dBg8v/VeDX/3FMaixYtivPPPz/uv//+qYMt+Xw+7r///rjpppvKWxyUSKFQiJtvvjnuu++++Na3vhXt7e3lLolXEDzL5Lvf/W5873vfize+8Y3x2te+Np588sm49dZbY9WqVWY7S2zXrl1x6aWXxqmnnhqf+tSn4rnnnpt6z0xQ6T399NMxMjISTz/9dExMTMSjjz4aERFnnHFGvOY1rylvcTXmQx/6UFxzzTXxK7/yK7FmzZr49Kc/Hfv374/3ve995S6tpv30pz+NJ554Yur34eHhePTRR6OlpSVOOeWUMlZW+2688ca45557Ytu2bdHc3Dy1nzmTycRxxx1X5uqIiIgCZbFz587CunXrCi0tLYWmpqbCaaedVnj/+99f+M///M9yl1bz7rrrrkJETPtD6V1zzTXTfvff/OY3y11aTfrMZz5TOOWUUwqLFi0qrFmzpvDwww+Xu6Sa981vfnPaf8avueaacpdW82b6s/2uu+4qd2m8TB9PAAASYWMbAACJEDwBAEiE4AkAQCIETwAAEiF4AgCQCMETAIBECJ4AACRC8AQAIBGCJwAAiRA8AQBIhOAJAEAi/n/iYUP2qK0duAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1842,7 +1988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 48,
    "id": "b4cff8b7",
    "metadata": {
     "execution": {
@@ -1855,7 +2001,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1866,7 +2012,8 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax.scatter(x, y, marker='o');"
+    "ax.scatter(x, y, marker='o');\n",
+    "ax.grid()"
    ]
   },
   {
@@ -1882,7 +2029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 49,
    "id": "9e59d427",
    "metadata": {
     "execution": {
@@ -1897,16 +2044,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x122230260>"
+       "<matplotlib.collections.PathCollection at 0x7f2143e6c8d0>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1945,7 +2092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 50,
    "id": "c7408ab0",
    "metadata": {
     "execution": {
@@ -1958,7 +2105,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1969,7 +2116,7 @@
    ],
    "source": [
     "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax.scatter(x, y, marker='o')\n",
+    "ax.scatter(x, (y + y[::-1])/2, marker='o')\n",
     "ax.set_xlabel(\"this is the x-axis\")\n",
     "ax.set_ylabel(\"this is the y-axis\")\n",
     "ax.set_title(\"Plot of X vs Y\");"
@@ -1986,7 +2133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 51,
    "id": "52bac447",
    "metadata": {
     "execution": {
@@ -2000,19 +2147,20 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x300 with 1 Axes>"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "fig.set_size_inches(12,3)\n",
-    "fig"
+    "fig\n",
+    "#np.corrcoef(x,0.5*(y+y[::-1]))"
    ]
   },
   {
@@ -2040,7 +2188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 52,
    "id": "0e4d4d4e",
    "metadata": {
     "execution": {
@@ -2054,9 +2202,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1500x500 with 6 Axes>"
+       "<Figure size 1200x400 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -2066,7 +2214,8 @@
    "source": [
     "fig, axes = subplots(nrows=2,\n",
     "                     ncols=3,\n",
-    "                     figsize=(15, 5))"
+    "                     figsize=(15, 5))\n",
+    "fig.set_size_inches(12,4)"
    ]
   },
   {
@@ -2080,7 +2229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 53,
    "id": "12d9932f",
    "metadata": {
     "execution": {
@@ -2094,19 +2243,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1500x500 with 6 Axes>"
+       "<Figure size 1200x400 with 6 Axes>"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "axes[0,1].plot(x, y, 'o')\n",
-    "axes[1,2].scatter(x, y, marker='+')\n",
+    "axes[1,2].scatter(x, y[::-1], marker='+')\n",
     "fig"
    ]
   },
@@ -2133,7 +2282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 54,
    "id": "17d46f32",
    "metadata": {
     "execution": {
@@ -2160,7 +2309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 55,
    "id": "24a361a3",
    "metadata": {
     "execution": {
@@ -2173,19 +2322,19 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1500x500 with 6 Axes>"
+       "<Figure size 1200x400 with 6 Axes>"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "axes[0,1].set_xlim([-1,1])\n",
-    "fig.savefig(\"Figure_updated.jpg\")\n",
+    "#fig.savefig(\"Figure_updated.jpg\")\n",
     "fig"
    ]
   },
@@ -2210,7 +2359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 56,
    "id": "facce2ec",
    "metadata": {
     "execution": {
@@ -2224,7 +2373,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -2251,7 +2400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 57,
    "id": "1683862d",
    "metadata": {
     "execution": {
@@ -2265,7 +2414,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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QWmOkMrdQ+4tjDBqQdOYiiRxPMB5qNzdXYKNVXZMk8jwdcsvx1JKlzPRs0g3MT2eqiTNF8azw6jvbdWqnZ1k3FLnkIOo+s52jDRZW0p16dGqxYcVTvPa67+KfMBZqFwTBaKhdd88oFPKc6gh6Y3JRKnXbLSwUWFoYvh66ezw3xfNvImma8peqzVe0z0MZorTMbFRahT+/dTx1RsjCIoovK9Se42gvJMJcWEhIfzXnVZR4LXI8e/bsSbdu3ahUqRKVK1dm9uzZ2NnZcfHixaI8LQAeWqk4IknaZPA6Qvdm9iqVVHJ5iXPSPSATCkFdlclkOQtAQh5C5f83H50zPo/76xYsU0xJOhUmUcJBrndcnTpqQImEv80YyUaOZ2dnfIzOFGLImZ5TyzPRQC1PXRF5CXL5dwH5dMlySNa2lljZiMQ9NiJR8vieWlXw2eNIye0vQqd4ht1/nqd6rC8TofefA6bldz7X1vv08DXcXlVHTp0NFI9PjDMeak/Ufm+67/FFZGYoUWaL35udRA6n7p6xs7eWNDZpNMILOZ7SxE5XTcEuF6KoS4HJTZlMSjFtnA5/F5C3MGk8/P2MsFCYYWWCgUnyGLpySoXkak8o5NC9IcRq1wbnV0hZzFKqSNK+QLjYvDrzKkpkq1TEal8CXhtXu1qtZuPGjaSlpdG0qX5vX4CsrCySk5P/7y+/qOElKhVng0PzfYx/Gyq4i4vJzWe5L6gvC+XcnTGTy4hJSSMisWhfAsq4OgHwIDymcI7nXrDj+XqI+z98Hpun/Uq5iUpTaGRCrmO93R2Ry2Qkp2YSm2Bc3S+tDTE/CTM8H+9S4pjQMMMu7pwxodJjSvuI92GwgfC0t5b0BD+S3u5bzh2AR/fC9ba5eNhj52iNRq3h0V397QDlq5cG4NaFR5LbqzeuCMDdS4+JNHItAPwblMfCypznj6K4d1n6eMWNo+vOAlCzZZVcx57YegmAGk0rS25XZqu4c/UJAOWrlZYcE3TrKQC+Eg767GwVoY9Ecqv7Hl/Ek4fiNmcXW2xs9YlZsLZYf2kfZ8lzP3sej1qtwcJCgYtE1ySAEO29622kQL5KrSH0mTiutIGyUjlzfireIz6eho+ngyAIhEaIv1tvd+PHfRG6Z0QZDyeTWg5KnffBc/E5pXtuFQRqjYZHkdo5FbDne264+TQCgEpextNEXiZuPhfXUDc7G5xeQjmpVwHnQ56iEQQ87GyLvIRUkRPPwMBA7OzssLS05L333mPHjh1Uq1ZNcux3332Ho6Njzp+vr2++z9vRX1xcLoU8JbGQaju+6mhavgwAl0OeoVIX3GBTGLCzssTfS1yAroc8L9Jz1SzrBcDtsMIxgtQqVwqAW8ERBdr/Tkhknr4P/zKichUUmnt419rKHD8f8YF974nxF44qFcXrc9+I0udfUSQTT0JjyDZQu7JyZfE4Dx5KH8e/qrd4jMfRZEq03qxaw0ecb+Azyf2r1xXv4zs39F8a5XI5NRuWByDw0mPJ/Ru1qw7AlePSKT2l/Nyp27oqgiBw8K+zkmN0cHCxo/WAJgDsWXbM6NjiwMMbwdy/8hiFuRldRrU2OjY+KokLBwIA6DaqpeSY25cfk5GahbO7PZVq6T9/M9OzeaAlnrWbVNDb/uheONlZKhydbfDxc9Pbfv+2+J1XqekjSbDuaV8mqhggvQ+191zFCh6YGWhHGfRIHFO5ouHSUmHP48jMUmFtZU4Zb8PqL8Bd7e+lankvo+MAouJTSE7LxMxMTvnSphOpwCfiM6Zm+VIm7/MiQqMTSE7PwtLcDP/S+oQ/r3gSFU96lhJrC3MqFiEhFASB6yHid97AT/o7Lw5cfBIGQJNyZfL1IvBvxOH74ot1B/+KyIv4Mxc58fT39ycgIIBLly7x/vvvM3r0aO7evSs5dvLkySQlJeX8PX36NN/nLevihL+HG2pB4PhD6QXqdUO1Uh7YW1mSkpnFvchXJyetblmRiNwIlVaoCgs1y2iJYlj+iOI/UduvYMTTz8sFOysLMrNVPA43XfWsrCWeD8NM+w6rajvs3M0ldFxVRzwfGR7n5emIg70VKpWGJyHSSm8l7XGePIlGJRHudvewx8XVDo1a4FGQ/rkqV/NGLpcRG51MTJR+VKN63bKASDylwts1G4nE89alJ5Lza9RefLG9ee4hWRnSRrNuo1sBcOivs7kWh+/5TnsAzuy4TJyB8H1xYefSwwC06NMwp4apIRxefw61SkO1RhXwqyq9yF85Jj6bG7SpKtnu8t6NUFRKNR7eTnhKqJJ3rosvC9XrlpUmllriqXv5+Cfu3xVfTqtU85bc/kCrmOruQSkEaRVX/0qGx9zT/gb8K3gaJLAAicnphMeIqQe635kxPAgTfzPlvF1MqvmpQ2CwOJ+a5fJHPG9qn1HVfD0xVxS8r3pgmDif6r6eRdqn/Gl8EnGp6SjM5FT3yZ3YvyxcDBa5R5Ny+Re//k1QaTQc0/KkTlrRrihR5MTTwsKCihUrUr9+fb777jtq167NokWLJMdaWlrmOOB1fwWBTvU8EvTfIJ5mcjmN/MQH+gXtG9urgHp+L4t4igvD46g40grB2a5bBB5F5O94crmMGuXEh6lO0TAFlcuIisWDsBiT8gqrVRDneS+Xkj9VtKHR4GdxZBj4PDKZjMraRf3BI+njeXs7YWtriVKpJjRUn1DLZDKqaonDfYlwuZW1BeW0atT9O/qqZ+UaPigUZsRFpxD1XD/doFZjUWm7fTVYMs/Tr4o3bqWcyMpUcvPCQ8nP0LRrbZw9HEiITubioVuSY3Twr1+eqo0qoMxWMf+t33JttfmycH7PNY6uPwdA7/c7Gh2r0Wg4sPYM8DfplsLl4yLxbNi+uuT2m1qVuWbjCpLEUqdS614e/on7gX8rnv9EdpaKJ9r0C0PEU6d4Vq4sTQLVag2PtHms/kbIqY54VqlgnOzcC9amDXg5m5Tj+UD7sqh7eTQFao2Gu6HieXTPi7wiMESrmOaTuOodT0s8a/rmTrYLAt2aUN3bM9+5rYWNtKxsbmnT1ZqU/28Qz+tPw0lIz8DJ2oqGZYpeeX7pdTw1Gg1ZWaYX1C4IdMz97JMQ0rILTkT+DdC9oV18kn+1uLBRt6x4Iz+IjCUtq+i+B3cHO7yc7BEEuPOs4OF2D0c7SjnboxEE7oTmL2+2alktwXpqep5oudKuKMzkpKRnERGbe56zv59WIc0lNO/uao+rsy0ajcBDA2om/B1uDzIQSpfJZFSqJI4xGG7XEc+70ukVVWqK98R9iXC7pZU5lWuI229f1w+3l6vihZ2jNRlpWZJ5njKZLEf11Cl4/4TCXEGnYc0B2P/nackxL+LTX8diZWtJwKm7/DlzW67jixrPHkaw4J3fAeg3oTPVGlcyOv76ibtEhcVh52RDy171JceEB8fw/Ek0Zgo59Vr6S47RpTfU0qrOL0Kj0XA3QHzhrVHPT2+7TuGWy2VUrqpPLB89jESt1uDkbIunRN6lRiPwUKt4VjagZj59nkBGphIrS3PK+BgOoQdpyWlVI+QU/v5N6X5juUGXHuOfB+IZFp1IRpYSKwsF5UoZD/sbgi4qo4vSFBQ5xLNs4RzPEHTEs66f9ItGceBq6HNUGg2+zo74OJuep/tvxuEg8QW9XaXymJsVXDHPDUVKPCdPnszp06cJCQkhMDCQyZMnc/LkSYYPH16Up82Bv4cbvk6OZKnUnHkc8lLOWdzQ5XleC3tO1ivSX9rT0Q5vJwc0gsCtp4UTBjeEmmXEheRWaOGcR5eneTMkf8fz9xXVy6Cnpqc+mCvMcvLDdKE7Y6jg64ZcJiM+KZ24ROPVA0wJt+tClIYUT/h74X/wQHqMLkfvvgEDUE6e521pYvpiuP2fMCXPs6E2z/Py8bsGVeOuI1sik8m4fuIuEUaIOECZKqX59NexAGz6YS/n91wzOr4okZmWxbfDfiE9OYPqzSrz1qzBue6jI9cdBjfF0oDb+rI2J7ZGowrYSpQ6ykzPzjEW6VTnF/EsJJbkxHQsrcypUEWfsNzXftd+FTywttGfg+5eqVK1lKSaGh6eQFp6FubmZpQtq58/CvBAe19XMpIDqlSqeaT9vnNTPHUvaJX8TMub1P1edVELU3Bfq5JWKu2er7B2WmY2jyJEo1StQlA807OUPIwQIxm652lRIUBHPMu+OsTzQk5+539D7RQEISe/s+NLCLNDERPP6OhoRo0ahb+/P+3bt+fKlSscOnSIjh2Nh4UKCzKZjE5VxAt5OOjVdKQWNiq4u+BuZ0OWSk3As6IleXlBTp5nSNGG22tpH5S6N/aCoqZWQQjMJ/Gsog3/P3wWi1JteohWF6p7YEKep5WlOb6lxHy7h6HGCZRuob1nhHjqQu3GDEa6HDtDimflKqWQySAqMokEiS5D/lri+fBeuGSeqDHiCbnnedZpXglzSwXRz+IJeyA9R6+ybtRrKyqjujC0MbTu35i+4zsDsOCd33lu5BoWFQRBYOGElYTcfYaLpyNT10zIvXZnRGJOOoHRMLtWHW7YTtr8efd6CGqVJtf8zio1fVCY66smLxqLpHA/N2OR9kWofHkPFAbyGP82FhkmTI9DY1Cq1DjYWeGdS17sQy2RrFQ2dwUzJS0zJ0JRKR/Es0oeVNIXcSc0Eo0gUMrFAXfHgtdfvPssSnQ3O9ji5SRdUqswkJyRyaNokTDXKVO0ympecEmX36kVcV53BEZEEZmSio25Oc3LSafIFDaKlHiuWLGCkJAQsrKyiI6O5ujRoy+NdOqgC7effBRM9n+gb7tMJqNxOfEH82qF219SnmeOs71wSEHtcjriGZmvOo4+bo7YWlmQrVITYkJ5JB1ezPM0BbqFLrdwuynOdi8Ph1wNRjpn++Mn0aglHPu2tpaU0apSUqqnTxlX7OytyM5SSZZVqlZHvIfDnsRI1vPMLc/TysaS2s3E8LMub1EKOiJ2eP05lCZ0qBo7ezDVm1UmPTmDmUN/JjOXVqWFjd3LjnJi8wXkZnKmrJ2Aq7a0lTEcXHcWjVpDjSaVKFNZeoHPSMsi8JL4ct64g3R+563LIsnPNb+znoH8Tq3imauxqLohY5GWVBoxDemMRcYc7bp7v0oFL6OO5YxMJWER8eLxTCCeut9qKVeHPNXS1EVDquSBrL4IXTSmll/hqJMvK8x+MywCQRDL4Lm9Iv3QE9IyuBcpfo+Ny0nfp68bjmhFudYV/V5anu1r2av9RdQuXQoPO1tSs7K5EPLqELGiRFNtQvSF4FfHYKTL4bn5NLJQeqkbQjUfT8zkMqKT04gshLqh/qXdMVeYkZCaQVhMYp73l8tlVPbRhttNdKnD3wudKYon/B0KzJV4ag1GT8MTDHY7MsVgVNrbGRsbC7KzVYRIGIzgb4OIFPGUy2X4Vzec5+ngZEOZCuI10OUNvoj/y/O8Ix2u15VVunzMcKe0xp1q4uzhQGJMSk6pIWNQmCuYumYCLp6OhNx9xqIPVr20wvJ3Lz3k9y/XA/D27CHUbC6dh/ki1GoNB7VqblcDJZQAbpwJQpWtplRZN0ob6PduLL8TjBuL1CoND3TEUkLxTExIIyI8EQD/KgaI5wPjxDPPxqJc8jufPItFEMDF0QZXA33n/29+ujB7WdMJpCAIOYqnv2/+FE9dfmdhhNkBArVVQYo6zH79FQyzX9JyhEoerkVey/JVwP+H2Y3niRcmXnviKZfJ6OD/3wq360IEgc8jSS1CM09eUMnTFTtLC9KysnkQmbeC6nmBtYU5lbxEpa0wwu0W5gqqaheE/IfbxYXofh7yPCtrc0Mj41JyOqwYgy4U+DAXhdTJwYZS2raSOoOFFHR5noYMRnK5jEpaVelhbgYjCWc7QNWaujzPgtXzzK2s0t2rwaQY6JKkMFfQeUQLwDSTEYBrKSemrJ2A3EzO8U3n2fN70df3TIxOZvaIxaiUalr1b0TfCZ1N2u/qsdvEPE/A3tnWoKkI/ibnjdpXk1QBc8vvjItOJuJZAnK5jCoS9T+DH0eRlaXCzt4KnzL6dSGD7ou/LV+tEv5PCILAQ10Y3YCj3VRj0f0cR7txx7YubcXUsHl+HO0R8Skkp2ehkMup6J33epmCIOQ8l2oVkkHnltZIWauIieeNV5B46qKETf8jYfbHsfEExydgbmZGm4p+L+28rz3xBOjoLz4ojz14XKRq26uC0k4O+Do7otYIXA2VXtRfNszkcmpp83heVrg9sJAMRjqn6M181vPU5W4F5cHZbmdjmdP55KEJ++kU0rDwBDKz9Iu2/998KpheSD7ISB5jJZ3ByBDx1CpXD+9HSKqCVbQh1/sGiaeJeZ6XpQ1Gnr6ulKnshUat4dqp+4Y+Ro7JKOD0fcINdFvSO3dzf8ZqTT3LJq3j7iXpsk2FAbVKzZzRS4gNT8DXvxSfLHnL5KLWL5qKDPVmFwSBK7oySu2kw+x3ruWS36lVpctV9sJWove5rlmAf/XSyOX6c3+gJZ6Vq0qrdhGRSaSmisYiPwOKYpAJxqLMLCXB2o5FpjraK5noaM+PsUgXZi/v7Zqnup86hMUkkpiWiYXCjCo+BS8cH52USlRSKnKZjOpFWEpJqVYT+FT8vl4l4qmLEv5XyijpxLhmfmWwszTeYrYw8Z8gno3K+OBoZUl8egbXnxUt6XlVoPvhXPgv5nn6ap3thWUw0nUwyrezXUs8w6LRaEwPy/prF7CgXAxDAK5Otjg72KARBB4/Na4om+Js14Xag0NjDXcwyiGe0sppuQoemJubkZKSSbhEPU5dqP15WLxk3/YaWuL58E44WRIdkHTK2x0DeZ4AjbRGGUNdjEAkqA20dSv3rzFN9QTo90EXWvZrhEqpZvaIxUTnct3zA41Gw/IpG7l5+h7WdlZMX/8hNhI9zqUQ8zyeK0cCAeg22nCY/fHtZ8RHJ2NlY0HNJtKu1sDLudTvvG5afmeVGtLGoQdaVTy3MHs5P3fMJYxL8Lej3Zix6EGw+Bt0dbbF3dW4ceaBjniaEDrPVqp4Ei4SWn8T8kF1+DvMnj/SqAuzV/X1KNTC8eU9XbCxNL3XfF5xPzyGTKUKB2tLyrvnr4RUYSMiKYXQuETkMhkNy/638jt1JuyXhf8E8TQ3M6N9ZXGRWnXpejHP5uWgmTZUcPjuwzy5qYsS9bUt0c4/DC3SUk86hTIwLJLkQmiXWrucVrl7HktUovF+6FIoV8oFK3MFqZnZPHxuuurpr60BeiPINNXav5y44N0MMt6atGol8foE3DXcWtXLwwEnRxtUKg23DdTi9PcXj/PwYSSpEvmi5uZmVNSakK5fDdbb7uBoja+2reJVib7qnqWdcfN0QKVSc+XsA73t5ap44eBsQ0ZaNtfPSSuOjbSE8vzBWyTGGf7udCajPStO8PShaS8YMpmMT5e+ha9/KWLDExjffDpXDt00aV9TkByfyoxBC9mx5BAAn/02ljJVTCvuLAgCv03dhEYjUKt5ZXwrGc7/O7D+AgB1W/pjYamvumk0Gs4dvg1AbYkwu1qt4ZJWUZaq36lWabiuzQ+tWlNfScrOUhGoDeP7S9T3BAi4KRJb/8qGSeWNW6JaVcWI+eh6oDgmN7UzPTObByG6nM3cieTtJ2INUkc7KzxdTHeCX3+oNVyVzZ+6eO5eCAC1yxeOang+SLzOhVUP1BBO3hfTY+qW9ZZUwIsD+wODAKhZ2gt7q5en/hUXroQ9405kNGYyGe0rSedtFxX+E8QTYGyTBpjJZBx98JjLr0j4uSjRzr8CbnY2RCSlcOC2/qJdHKjvVxovRzsS0zM5cqfo8m3LebhQ0cuVbJWagzeCCnw8Tyc76pb3RiMI7Lts2CFtCOZmZjSr4QfA4aumfxct6ogPg4uBIaQZaP34IprXFcefvGI87FvT3xsnB2sSkzO4dks6jC2TyWihVb+OnronOcantDNly7iiVKo5K0EMAVq0Fg0wJ45IK45tOonE8NCeAMk5tO9RB4B9my/rbZfL5bTrXQ+A3WvPSR6/RuMKVKzpQ2Z6Nlt/NZyL2bhzLeq1qUpWhpJ5764wyeEOYGNvzeydn1Oprh/Jcal81e8HVk7fXODuRvcuP2J8s2lcOhCAuaU5Hy1+k5Z9G5m8/5EN5zm39wZmCjlvzxxocFzU0zgOb7oIQN+xbSTH3Dj3iGdPYrC2taR55xp626+df0jk8wTsHKxp1LKy3varFx8RH5uKo5MNdRqW09t+6eIj0lKzcPewl+xYpFKpOXVaJLYtmusfH0Rl/nFwDAqFnKYN9cmxDkfPivdyq0bGjRQXAoLJVqoo7elE2Vx6uQMcuyze/y1qlzc5DSImKZXrD8W1qFWtvC/8yemZnLglPkc715O+LnlBplLFAe3zskvt3I1r+YVSrWbbFfFFplfdqkV2nrwgW6Xiz4uiKDW4Qc1ink3RQyMIfHfkFACD6tbExdbmpZ7/P0M8K7q7MqiueEN9d/QUmpfkRC0uWJorGNG4LgB/nLv60py3xqAwk9O/ofgdbL5kvE1hQSCTyejTSCQ0O6/knShKoXcT8Xi7LhkuSG4MHeuLC8ORaw9M3r+Srxu+nk5kq9ScvSltoHkRrRuKi2ngg3Ci4w07+hUKM9o0Eedz8qJhktqhjRimPnU2SDLcLpPJaKcNZR8zULKobYfqyGQQePMpUZFJets79qiDTAYBV4KJ1LqaX0TXAQ2RyWTcuPiYp8H6anHPEc2QyWRcPRVE8H19pVImkzHys24A7P3zDPHR0p2g5HI5n/3yJvbOtjy6FcZf8/dIjpOCZxk3fjw2jZ7vdgDEAvNfdJtLbHi8ycfQQRAEtv9ykM86zib6aRzeFTxZeHwa3d5sa/IxwoOj+XXyRgBGTe5NpdqGa/OtX3QIlVJN3ZaVDYbZt/5xEoCO/RpgI5G/uXeT+FLQsXddrCSK0x/aHQBA+261JMPkx7VqatsONSTVr2vXQ0hKysDJyYZ6EooqwDHty1Hj+uWxlzAnAYQ8iyPkWTwKhZyWjY2HFk9oiWTbRpVyJZJqjYYT18TfUfuGphPAY9cfIQhiKo+3a97bQx+6HkSWUk0lbzeqFUI+5vHAR6RkZlHK2Z5GFYsux/HU/WBiUtJwtbOhXbWXG+I1hD2BQUSnpOFhb0uPmlWKezpFjt2373E7MhpbCws+bNX0pZ//P0M8AT5s1RRbCwvuREaz+7a0ivM6YWjDWthYmPMgKpazj6WVrZeN/g1qYCaXcS3kOY+iis7d3qNeVRRyOYFhkTyOjCvw8TrWrYyVhYLQ6IR8mYxa1iyHlbmCZzFJJrvbZTIZHRqJysOxK7krpe7OdtSqLCpGp3JRPVs3EUnqmcsPJetwAtSq7oO7qx2paVlclgiVA7TTktMbAaHES9TbdPdwoLY2V/P4kdt62z1LOVG7gaiCHdkboLfdq7Rzjoq2f8sVve3eZd1o2VV8mdmwVFrRbNiuGv51y5KVqWTzkiOSY0B0q3/000gANi86SOB509VpC0tzJvw4iilrxmNjb8Xtc0GMazqNDQt2s2XhfpP/ZgxayLIv16NWqWnZtyGLz3xDxTp+Js9DrVKzYNxKMtKyqNm0EgOMuN/Dg2M4ulW8piMndpccc/d6CAHnH2GmkNPvLf3i85HP4rlyRrxO3QfqK7KJCWlcPC2qaJ161tHbnpKcwaXzomrXvpO0sen4CfGlpnWrKpKmIUEQOHpSHNOhjWEF7eRFcZ4NapY12nc9M1vJ+Rvi/d62Ue5E8ubDcGIT07CztqRxDdMLcB/RRj86NsifWrnroviZezWubrLKavR42pf0Xg2qFWn4e5NWdOhbvzoWhZCXWlBoNAKrzl0FYHSTeq/EnIoSGUolP54QI0TvNW+I60tWO+E/RjxdbW14r3lDAH48cY4MpXH3778djtZWDKwnhsZWnL1azLMR4eloR5sqYlhpy+XAIjuPq70NLar6AbDrimFjiamwtbKgYx1xgdh1Me/Hs7GyoGUtkWAdNoFE6qBTUM7fDDYp3N5Gu1CeuGyceNat7ou9nRWJyRncvCedemJmJqddK3EhP3pKWtEsXdqZKlVKodEInDQQkm/fSbwHjx66Lan2du4lKvOH9wRImq96DG4sbt91ncx0/WswZFx7AM4eDCRUwugkk8kY/blIrPb9dY6YiETJeQK06FGPTsOaIQgCC8atJC1ZugyTIbTu35jFZ2dSvmYZkmJTWD1jK39M3Wjy38X9N1CYmzHuh5FMXTsBW8e8LQobfzrAvStPsLG3YuLSMQbd3QDrFh5Eo9bQsF01qhpQEjcsEcl8h7718Syt72bfv/UKgiBQr2kFfPz021geP3ALtVpD5arelJMo6n7m1H2USjXlKnhQXmJ7ZqaSs9r83faGOioFRRARlYS1lTnNGhlW0E5p1X2d2m8Il26GkJGlxMvNnqrlc1cSdWH21vUqmGzwiU5IJeCxmN/ZoV7e6yc+jojjdmgkCrmc7g0LrtBFJqRw4aEoTvRuKH2dCwNhcYmcfxiKTAYDGumnbRQHTj0M5lFMPHaWFgz6D4TZV1+6TmRKKt4O9rzRqF6xzOE/RTwB3mhUj9KODkSmpP4njEajm9bDTC7jYvBTbocbrtv4MjGocS0Adl2/R0Z20ZH/Pg1FBWXPtXsGTTR5Qa8m4gP5aMBDlPnI4XsZ4fa22ty1gPvPiE8y3LddoTCjpTYX7rSRUkA6Bencpcekp0t36WmnbTt5/IQ08WzZpgrmFmaEhcTyWIIYNm9TBVs7S6Ijkwi4oq+s1m9eEa/SzqSlZHLyoH6KRjn/UjTrWANBENhoII+zTovK1GhcAVW2mo0/H5b+sFq8N3sIpfzciH4Wz5JJG4yOlULpil4sPDGd0dP7035o8zz9dX2zDQtPTKf3ex3zrGLdv/aEdd/vBWDC/OF4+hquCxn2MJITO8R+87pUhH8i6GYYV08HITeTM+jddnrbs7OUHNIeQ/dy8CIEQcgJs3fqVUfyHMcOiSp4u47SaufFS4/JyMjG09OB6gZaaerUzhZNK2FloGTU88hEHgZHI5fLaNHIcA4o/P3S1qZh5aILs994iCBAnQreeOXBjKTDgWvanNfqfrjYF1yx2n3tLoIADSr44OvqVODjGYJObGheyQ9fl6I7T16wQqt2Dm5Q87U3FcWkprHsvBjlmNiuBZaKl9Op6J/4zxFPS4WCz9o2B+D381eISTW8OL8O8HZyoFsNMVy78tyroXo2q1gWH2cHUjKzOHir6IxPraqVw8XOmtiUdM4FhRT4ePUqlMbdwZaUjCwu3M976kLzmuWwtjQnPC6Zu6GmvQTkNdzu5eZAtQpeCAKcvGLcwNWqsbhQnrr40GCZp0oVPCnj40J2toozF6WP17Z1VWQyuHv3ORESaqKtnRVNtO0rjx3WD7dbWpnTtrOoNByWMBnJ5XK6DxLDuHs2XpIk7UPHi6rn6X03eSaRCyqTyRg1USRYhzddJOqp4fQLG3srPv/1LeRyGce3XOLkdn1jU26wtLZg2KTefPHHu3n6+3jxGCrV1Tfg5IaM1Ezmv7cSjVpDm36NaDdQnwi+iHU/HkQQBJp2rkkliYLv8HfqQttedfEuq09izxy5Q1JCOm6eDjRupW9GeXgvgpDH0VhYKnK+3xcRHZnELW39z3YdpdWvYyfE6EK7ttKF7VVqDSfOiCRMl5MshVPal6s61XxwcjBM1LKVKs5eFx34bRvnrkTmN8x++KqYfpCfMLsgCBy6Lj4LutQvuNopCEJOmL1PEaqd2SoVO6+J3+egRq+GshjwNIKroc8xN5MzqknxqH8vE4tOnSddqaSWtxfdqxWdgSw3/OeIJ0D3av7U8vYiXalk0anzxT2dIsdbzRsAcPDOQ57GJxbvZBC73gzUqp6bitBkZG5mRvd6omK383LBw+1mcjkd6oqL0cFreXfLW1uY57hX8+Ju1ykpF24Fk56Ze7hdp3qevGz8HA1rl8XaypyY+NScNoL/hEwmo31rbbj9pHS43dXVjrp1xEX3xEnj4fbjR+5I5pTqwu1nT9wlJVm/U1OnPvUwt1Dw+H4EQRItNitWL03jdlXRaAQ2/Xpccg41m1SkbsvKqJRqNuSielZrWIGhn4rh+V8mriP6WcHzhIsSy77aTHhwNO6lnRk/f6jRscH3nnN67w0ARn7WVXLMozvPuXT8HnK5jCHvSRub9m66BEC3gQ0xkwgxH9otnqN5m6qS3YiOHxV/k7XqlMHDU99ck5qayWVtf3hDYfYbN0NJSEzH0cGaBnUMEz+dqt86lzD7ldthpGVk4+ZsR42KuZcoyk+YPTI+hZuPI5DJoH3dvIfZ7z2N5mlMIlbmClrVyPtLyj9xIzicsNhErC3M6Vir6NomHr3zmPi0DDwcbGld5eWW7zEEndrZs1ZVPB3sink2RYug6Fi23hR/c5M7tCqUvOD84j9JPGUyGZM7iInyW2/eISi66EwurwKqeLnTomJZNILA6gs3ins6gJhYrjCTE/gsknvhpreSzCt0+Uon7z4hwYTWk7mhS33xLfFk4GMyTSy58yJywu1X8x5uz1KqORuQe7hdl+d5/e5TEo3kKFpaKGhWXww7nrpomKR20BLPazdCSEiUjhDowu2G3O0Nm1TA3t6K+LhUbkp0IqpUtRTlKnqgzFZzUkIVdXS2pbW2lI+O8PwTQ8eLrvLju28QESZNFHUmmiNbLhMuoYz+3/E+64Z/PT/SkjP4fvwqgyas4sa5fTc4+NdZZDIZE5eMwT6XvuJrfzgAQKsedSlXVTp8vVGrdrbqVhsfid7tj+6Fc+/mU8wUcrr0a6C3PStTyYlDYli1s6Ewu/Z7bi9RogngzNkglEo1fn5ulC8nXUvzqPZFp23LKigMEL+YuBRuB4kF6lvnomKeuCT+Dto0rJirwUajEfIVZj96Xau+ViiNh3Peyc6h6+JLb8sa5QqlyPtObQ5859qVi7RovK6SyYCGNVEYyT1+WQiJS+Cotk/5mGaG28m+Lph37DQaQaBLlUrU9zWtHnBRofi//WJCfd/SdKlSCY0gMO+Y6d1K/q3QqZ7bbtwmIa3gBKygcLWzoWN10QhQlKWV/L3dqerjgUqtYd/1glcyqOVXilLO9qRnKTl3V9rpbQzNqvthY2lOZEIKgcGmdVaSyWQ5C9tRE8LtPp5OVCrrjlojcPqadDtJHXTu9pMXDRNhn9IuVKnshVojcPKstNLbsoU/5uZmBIfE8CRY/0XCwkJBq7YigZUKt8tkMjr11JqMtHmB/4Quj/DUoduSnY78a/nSoJU/GrWGzctOSB6jaj0/GrarhkatYfm3O42Sf4W5gi9+fQsrW0tunXvA9qWGHfHFhbjIRBZ9sgaAARM6UbuF8fBZwLkHXDgUiFwuY/inXSTHBAdFcO7wbWQyGUPG6ed2wt91VVt0qI6Lm36O4rmT90lLzcKzlGNO1YIX8eRRFCFPYjA3N6OVASe6Lme4fVtptTMrS8lpbeUBY252ndpZw98bNxfDRE+lUuf8Xkxzsz8vkJu9Uz7D7Ie1YfbO9QoeKk3PUnLopni8Po2KLsz+ODqOK8HPkMtk9G/4apiKVp2/hiBAm8rlqOhhOB/6dcDpxyGcfRKKuVzOxHYtins6/13iCWJyrblcztknoZx+HFLc0ylSNCnnS7VSHmQqVay7HFDc0wH+NhntDbhPWlbuIeT8Qmcy2lUINT1lMhkdtcWaD17Pe7jdykJB69qiyqjL8zIFujxPU8Pt7bT5mydzcbc3qVsOCwsF4VFJPDLSmrNDa62xykAo3d7eikYNxfDZ8eMGwu1aZevsyftkSfSTb9e1JgqFnAf3wnkiYULyr+lDxareKLNVHN4pbQzU5Xoe3XGNKIk2nQBvftkThbkZF4/c5shmafVUh9IVPHl31iAA/pyzk+NbjI9/mYgJT2DmqKUkx6dRoaYvI7/sZXR8WnIGP366DoAuw5pRxkCHn43aVIXmnWtQVmJMWkomx/eJHZp6DJIuan9olxhZ6dSzjqRyqHv5aNysomQYPj4+lRsBojLe1gDxvHDlCekZ2Xh5OFDdSEenUzlhduNq57W7T0lJy8TZwZraJnSIOpqPMHt4XDKBwdowez7c7LdCIohISMHG0pwW1QoeZj8a+JD0LCW+ro7UK1d0KtjmS6L63bpKObwc826mKmzEpqaxI0BcD3SizOsKlUaTI66NaFiHMs5OxTsh/uPEs4yzEyMbiirL98fPvtZF5WUyGWNbiD+wdZdvFqmb3FQ0LOdDOXdn0rOV7Au4X2Tn6V6vCuZmZtwPj+H+84KH9btolYYzt4NJzwdh1ikdR68ZNvX8E3kOt2uLyV+5HUpyquG2oTbWFjSu7QfAqQuG1dR2raogk8Hte8+JjNYvBA9/5+EdPyldZL96TV88PB1IT8/m4ll9QuzkbEsTrUnlwM5rettlMhk9BotEZ9/mS2g0+qHvavX8qNOsIiqlmi2/n5ScZ7mq3ozUGo1+m7HdqNEIoMuIFrTq3QCVUs3891fw00dryDTg8H9ZuHI0kPFtZhJ0PQQbeyu++O0tLCylHd06/Pr1NmLCEylV1o2xX/WWHBP2KIoz+8UIxFBtmap/4uieG2RlKilbwYMa9f30tj9/Gk/A1WBkMujQvY7edo1G4Li2k1W7TtLq18lT99FoBKpV9ca7lJPkmOOnxRecdq2qGgyLJySlE3BXzAnONcyufUlr1aASZnLjS2O+w+zXxN9Y/Uo+uDkaT4mQgs5U1KZmBawsCu5I1uW+925YOLVApZCpVLH7ukjyBmvFhuLGuks3yVapqe3jRYOyxRt2LmrsDrzHw5g4nKytGNfcuOnwZeE/TTwB3m3WEDtLC+5Hx7DjVuF0uXlV0alqJUo7OZCQnsHGq0UX3jYVMpmMQY3EB9H6CwGoJYhEYcDRxoq2NUQ1buvFgtcOrerrga+bI5lKFSduGQ9lS6FptbLYWVkQnfh3y7zc8GK4/fCl3JVSv9KulPdxRaXWcOqqacXkj583HG53dbGjVnUfcdxp6ZeEJo0rYm1tQWRkEoG39T+XXC7LIRqHDkjff936irlWx/bfIjVFnzC36VoLW3srIp4lcOmU9HXQEaZDWy7zPERaxe3/bjuqNSxHRmoW3437kzSJc+kgk8mY9PtYhn/eA5lMxqF1ZxnX+ltObr8sSX6LEuHB0cx7bwXThvySo3QuPvYVZf2NG2EOrD/Psa1XkMtlTFw4Amtb/bIxgiCw5qdDotu9Q3XKS/RNVynV7N4gqr49BjeWJCt7tUXp6zepiJe3k97261eDiY1Jwc7OisYS3ZIEQeDIUW2ZJQNqZ2paFhevii9gbVsaDjmfuiS+3FUu54G3p/5cdMhWqjit/Z20M8HNfj3oaZ7D7IIgcPBy/t3sSpWaIzlh9oK3yAyJSeDK42fIZNC7QdGF2ffcuEdyZhalnR1oVsn0lISiQkJ6BuuvBAAwpnmDYjXZFDVSMrP4+fQFAN5p2hBHa8ONE14m/vPE09nGmnebiUXlZx46zsOY19dopDCT825LUTH6+fh5QuMSi3dCQO/61bC3suRhVFyRFpQf3Kw2ANsu3SY0RjoEaypkMhk9G4sP6hWHL+eZMFuYK+jUUFwsVxwwvVRP12ZiHtvpG4959NS4MQagcwtx/Lq9V43OsUXDilhbmRMWHs+5q4aJdOd2ImnctvsaWRLGKisrc9q2Fsu7rP1Lund65661kMtlXLn4mNu3nuptr9uoPGXKuZGWmsVfy0/qn8Pagu4Dxd/r7wv2k5Wpr9zXalyBus0roVKqmfvxerKz9OdqZibnsx9HYOdoTVBAKF+N+JU0CTf9i+NHTurFnG0f4+zhQHhwNHPf+YMJ7WZx6fCtIm9JGxeRyC8T1/F20+mc2CoSv55vteGnA1/iLWH+eRH7153n50mbABj8QSeqSeRcAhzeepVzh28jN5Mz/IOOkmN2rb/A89BYHJxsaNejtt72Z6Fx7Nbmf/YZrB+GFwSB1drvtX3nGlhY6qt2l688IehBJBYWCtoYyN3csec6WVkq/Mq4UqmCdJF3tVrDxt2ia7ljK+M9wfeeukNCcgZuznbUq5p7u8jVe7VtQhtXNjnMfu52CPefRmNlrshX0fhdF+8Qk5yGq70NTasUnMAt3HcWgFZVy+HlXDTh76SMTH45IlaOGda0Tq5K8svAt/tOkJSRRSUPVzpUMV7T9d8MQRD4cu9hwpNTKO3owPAG+r/X4kLx3wWvAMY2aUDzcmXIUKqYsG0vqUWYb1jcGFCvBo3L+ZKhVDF556EiUxlNhaO1FR90bAbAokPniEvNW6cYU9Gooi8tqvihUmv4ce+ZAh9vSKs6ONhY8iQyPl+llcZ0bYTCTM6le2FcDdInYFIo5+1KB63p4Y9dF3Md379DHextrQgNj+fYRcNztLO1pH9XMeVk9ZYLBklUx3bVcHezJzYulQNHpF8Shg9rhkIh59r1EG7eDNPb7lPGlS7dxQfg8qXH9M4ll8t4T2t62bX5MiGP9FMjhrzdBjcPByKeJbB5hbQx8JPvBuLgbMOjO89Z9f1+yTHefm7MWT8eO0cb7l8PYerwX0lNMn7/1W1VlZWXZzF6Sm9s7K14cvsZXw9bzGfd53PrXN7vg9yQHJ/KHzO28mbDqexbfQq1SkOD9tX55dhUxs8bhoWBguk67F1zll++FEln77daGyyfFPowil9n7gRg9CedqVBNX+2MiUxk7VIx//OtTzpj+4++7YIg8NuPB1GpNDRsVpEGzfTVzNPH7xF0LwIra3OGj26ut10QBFauFr/T3r3q4eKsH45OT89i805RVR0xuKlBxerY+SCeRSTgaG9N746GF91spYo1u0QyP6pXI4PueB1uBD3j8p0wzMzkjO4uneMq9bmW7hIJ2KC2tXHOY9H3LKWK5YfEOY7t3AgL84KF2a88fsaxwEeYyWV80r1lgY5lDD8fPk9cajrlPVwY1rROkZ3HVBy884D9t4Mwk8uY06fTK0GEiworL13nSNAjzM3MWNSvO9bmxp8VLxOv71XPA8zkcn7o3RVPezuC4xKYuu9IkSsYxQW5XMbs3h2xsTDnelg4f74C5ZWGNKlFVW8PkjOz+OFAwUmhIUzs2QozuYzjtx9z+ZFpZM8QHGysGNVOzJn97cDFPHdG8nZ1oG8LsYjy0t3nTb7fxvZuikwGx68+5EGo8XxVWxtLhnYTQ9crt180+pIxuGcDrCwV3H8cxcUb0m59C3MFwwaIOULrtlxEqdTv3lSqlBPduoqL/IpVpyU/18i3WmFlZc7d2885e1qfrNVvUoFmbaqgUQss/eGA3jFsbC159wsxR3PzytM8D9WPUriXcuLTuaIpaOfqs1w8Jp1GU6mWL99tHI+9kw1BAaFMGbaUFAnH/IuwtrNi6Kfd+fP6dwz6sAuW1ubcvfyYL3r/wNRBi3gYkPfmAv9Eekom677fyxv1p7B18WGyM5VUa1yBBbsnMmvTR1SqnbvitWf1GZZM3QJAv7fb8O7XfSVJWlamkrkfryMrU0ndZpUY8HZryeMtm3+AzIxsqtUtQ8fedfW2XzrzgCvnH6FQyHnv0y5651Iq1azU5t0OGtoEZwmH+ZmzQTx8GIW1tQXDhjSRnMfO/QEkp2Ti4+1Mu5bSBdTVag1rtoohxsE962NjbbhM0N5Td4iKS8HN2Y5ebXMvbP77TvG4vVrWwNvdMdfxAMdvPOL+02hsLM0Z3bmhSfu8iG3nAolKTMXTyY5+zQpWfF2jEViw+xQAA5rUooJX0Ti67zyPYtMl0YT2Va92xd4DPTY1jRl7xTJh77RoRM3S0ua61wFXnz7n++PiWjq1Y2tqeb9an7WEeGrhYmvDz/26o5DLOXDvAWuvBhT3lIoMPs6OfNlZXFwWHj/H45jiLY5tJpczrbdYtmXX9btcC3leJOep4OXKgCZiTumCXacKrPYOa1MHZztrnsYksvdy3vODx3ZrhKW5GQGPwrlw1zSyUr60Kx0bi2F63QJoDAM71zVJ9XR2tKFv5zqAcdWze+dauLrYEh2TwqHj+mWRAEYMa4aFhYLbd55x5ao+iXVzs6f/EJHArvj1BCqJ9qPvftwJC0sFN6+GcEaCNLboWJ36zSqhVKpZMmeP5Hwbt6tGnzfE0iE/Td5ssEd7xRo+zN00AQdnWx7eesqUYUtISci9o5m9sy1jpvdj5eXZ9HizNWYKOdeO3+GDDrOZ9eZvhD2IyPUY/0R2ppIdvx3lzYZTWTt3N+kpmZSv4cPMDR/ww94vqNnMtNy+XStPsXTaVgAGvN+esdP6GFQGf5+zh5AHkTi52jHx+yHIJVSgK2cecPboHeRmcj6Y2ktvTHaWit9+PARAv2FN8ZHodLRv13XCnyfg7GLLAAlSqVZrWPWnGP4d0K8BjhJ96jMzlWzaLqqdIwc3NdiL/uTFB4Q8i/8/NV8K/692NsQyF8PO1XthXLv3FIWZnDd7mqZ2qjUaftsj/laHd6iHs521SfvpkJGtZMVhMbT/dufGWBZQ7dx99S73nkVjb2XJuM7S5L6g0GgEvt15HEGAbrX9aVwh9/SFooQgCMzYc4zE9EyqeLnzfutXw2RTFIhNTePj7ftQCwI9q1dhaL1Xw9D1IkqI5wuo6+PNpPZi2GHe0dMEPM/7wvFvwcD6NWhZ0Y9slZopOw8Xe8i9dplSOfXdZu06Xii91aUwrnMT7K0suR8ew+6rBavraWNpwZsdRPVi2cFLee7f7u5kx8DWojq4dFceVM9eTZHLZJy+8Zh7IcZbb9q9oHqu2mFc9RzSqyEWFgruPIjg6i39MDmIReeH9BMX3L82XZQkjW5u9vTuJbafW2lA9Rw0rAlOTjY8fxbPAYk2mV6lnRk4UkzB+H3hYTIz/j/9RSaTMX5KD8wtFFy/8JjTh6RJ8JsTu1GxemmSE9KZ/9kGg0Xgy1crzdxNE3B0teNR4DO+HLqEZBPIJ4BrKScmLBjOHxe/pd1A0XBzds913msxg/Ftv2VCu1km/42uN5llX20mKTaF0uU9mLz8bRYf/4pGHWuabILYsfwEv329HYDB4zswZnJPg/ueOXCL/RvEtI2JC4bg4q6f65eVqWTpd3sA6Du8KeUq66snW9edJ+J5Aq7u9gx7q5Xe9rTUTP5aLZLKUWNaYW2jr0AeP3GX0NBY7O2tGDhAmtTtPhBAYlI6pTwdDdbu1GgE/twqfqZBPepja2O4//a+01q108mWXm2NL9CCIPD7DpFA9mldEy9X/W5LUjh89QGPw+Owt7FkRIe8t2XcfOYmcSnplHZ1oHcT6Z72piI9S8nPB8T863c6NMLFruB93qWw9Woggc8isbW04Itu+vfDy8a+20Ecvf8YczM5c/t2Lnb1taig0mj4dOcBolPTqOjmwsxu7V9J81QJ8fwHRjWsS9eqlVBqNHy4bS/x6cVfbL0oIJPJ+LZXB2wtLbj5LJKNV4rf5f5p5xY42VjxIDKWdRcCiuQcLnY2vNNBa7DafzZf5ZBexMCWtXBzsCEiPpmdF6XJjzG80bkh1pbm3A2N4tTN3MskAfh5u9C5qRhiXL7DdNUz5Hk8x410KHJ1tqV3R3HxXbXFMBHu1bUOzk42REQlGazrOXRwE6ytLXjwMJIzZ/XPaWNjycgx4kvempVnSJcoTzRodAs8SzkSE5XMJi1peRHeZVwZrCU5v83fR5IEUbSwVDB50XCsbS24fSU4pyOPFMpV9Wbepgk4udnx5M5zvhy8mMS4VIPj/4lSfu588etb/Hp6Os261UGjEXgc+JRHt8JM/kuITsbN25mPfhrJsnMzaN23oaQCaQhbfzvO79pczaEfdmL0pB4GF56oZ/EsmiqqooPebUv9ltJq6l+/HifiWQJung6MkCgoHx2ZxMaVYlhv7IcdJUnlpnUXSEpMx7eMK1171NHbrlKp+XOt+B0PHtgYOzt9921WlpIN20Tlb8TgJgZzMc9cecSTsFhsbSwY0M0w0VOq1Py5U6t29m6Uq9p55d5TAh48x0JhxugepqmdKvXfaueojg2wt8mbqzgtM5tVR0SF950uTUw2MhnCqhNXiElOw8fVkWEt6xToWIaQkJbBwoMiuZ3QoSnuxdyKMjE9k+8OiKkF77VqTBUv92KdT1Hi51MXuBj6FBtzc37u3wNbi6LrRFUQlBDPf0AmkzG7e0fKuTgTmZLKxF0Hil0NLCp4OdrzaXsxwf/HY+eISjZ9kS0KONla80kXMTS6+Mh5opKKZj7DWtbBx9WR2JR0Vp64WqBjWVuYM7aTGLZZfugyWcq8tdF0cbBhaDsxFLh093mT63q+1asJZnIZZ28+4c4T48q8nY0lQ7pqFcgdF4zez8P7NMLC3Ixb955z4450HqyVlTmD+opK79pNFyRVRCcnG/prWymu+vOM5JhuvepS2seFxIQ0tmzQN0tZWZnzzsedAdiy9jzhz+L1xgwa05Iy5d1JiE3lp693SJJl77JufDCzPwDrFx/l1iXDzv2y/qWYt/kDnD0cCL4XzuTBi0mMTTE4Xgp+VUszfc04fjs7g283fpCnv++2f8LKy7PoOrIlijyGVDcvOcKK2bsAGP5JF0ZO7GaQdKqUauZ+sp60lEyq1CnDyI86SY67cfExW7Wkf/yUnlhLqIe/LzpMVpaKGnXL0Fai/WVMdDLbNomEcez7bTFT6C87Bw8FEh6eiLOTDX37SLcv3Hc4kPiENDzdHXIqLPwTgiCweotI9AZ0q4eDBIHVYe+p23lTO7eL5qA+bWri6WKaC3zvxbs8jU7Eyc6aoe3qmLTPi1h/6gaJaZmU9XCme0PjzvzcEJmYwuqTYn3cT3u0xEJR8DqgUvjp4FmSMjKp7OX2ShiKvj9yhri0dCq4u/B2i7zn1/5bcOLhE347L/7OZnXvSEW3V7cbUwnxlICdpSU/9++BlULB2SehLD376nQrKWwMaViL2j6lSMvKZtZ+6TaDLxP96tegtm8p0rOVfH+gaFqZWigUfNpDVNtWn7hKdAEJbr9mNfBytic6MZWt5/KuHI/qWB87a0sePY/l6PXcW2IClPFypmszsaTT7yaonoO61MPexjJX1dPNxY7u7UXzgi5cKYU+3ergYG/Fs/AEjp+Rrus5aEAj7OwsCQ2N5fgJ/TxNhcKMt95vC8DWDZeIkyB4zdtWoW6j8iiVapZpcwhfhIWlOZPmDsLc3IyLJ+/ntHL8J9r2qkvH/g3QaATmf7aBpHjDYfQylbyYv3kCLh4OhARFMGnwYhJikg2ONwS/Kt407FAzT391W1XN1akuhQ0/H2bV3L0AjPisKyM+7Wo0xLZm4SHuB4Rha2/Flz8NQ2Gur6QlJaTx/VdbEQSBbgMb0rStPvEJuBLMmaN3kctljJsofc41K06Tna2iRi1fmrbQV1Wzs1WsXScqZMOGNsVawgiUrVSxXltGauiAxphLzBfg3NUnPAyOxtrKnEE9DPffVqrUObmdI3vlrnZevB3KrUfhWJqbmexkV6rULN8nnmNMl4bYWOVNfUpOz2TNMZEovte1SYH7my8+eJ5MpYp65UvToaZ+xYHCwK2wCLZdFSM/03q3K/ae7FdCnrH1ujifmT07vLYh9qeJSXy++yAAIxrUoUf1grdTLUqUEE8D8PdwY2Y3sRD14jMXOfOattQ0k8uZ2asDCrmcI/cecfx+3guiFybkchlf9W6LTAb7bwZxK6xo8mw71KxIHb9SZKnU/H7U9FqaUrAwV/B2Z1H1XHn4ChkS7SCNwcHWipEdRUXyt90XTM5vfatXY8zkMi4EhnDrUbjRsXY2lgwxMddzeJ9GYkmkwDBu3pMucG9jY8mgPlrVc+MFSaXWzs6KIYNF88LqNWcl80FbtPKnWo3SZGYqWbtSv6KBTCZj3MQumJnJuXjmAVfO6RfDr1ClFGO0yujv3x8g5JF03uu46X3wKe9OXFQyP07aZDSn1qeCJ/O2fICrpyNhDyKZNHgx8dF5J58vA+sWHmTNgn0AjPq8O8M/lu7BrsO1Mw9yujp9NHsAnj4uemMEQWDhjJ3ERafgW86ddybql2FSqdQs/f4AAN37N6CCRO5n8JNoDmubBbw9TjrfbPfeG8TEpODh7kDPHtJGoINHbxMTm4Kbqx3dOkm7ukW1U1Ql+3Wpi6O9YRPPvlN3iIzVqp3tjLvEBUFg+Q7tcdvVxt3ZtNDxznO3iYhLxs3RlgGt815D8a8T10nJyKJCKdcC92V/EhXPHm1O+8SerYok708QBObtE0PavepWpZ5f8XYEylapmL7nKACD6tek/mvaoShLpeKjbXtJzsyitrcXX3Yo/pza3FBCPI2gT81qDK5bEwH4bNcBIpLzFnL7t8Df0403m4mkZOa+48Vex7RaaU961xPVvHn7TxVJaSuZTMaHXcU0g22XAnkeL90G0lT0alINHzdH4lLS2XgmIM/7D21XFydbK0KiEjh42bT2oaU9nOjeQjQbmJLrqVM9g5/HceKSYdXTy92Bbm3FUOafWwyrnn171sPO1pLQp3GcMlDDsm/v+jg72xIRkciBg/pqsEwm421tp6EDewMIDdEvjVSmnDt9tC74X388SLZE8frew5vQoHklsrNUzPtyC9kS5N/KxoLJC4djbqHg8sn7bFhiON8TwKe8B/O3fIBbKSeePozii4G/EHDONEX6ZSAxLpUlU7fw1w8i+Xvzyx4M/VA6ZK5DeGgs33++EYBuQ5vQsqt0iHn/litcOHEPc3MzJs0diJWECrlnyxVCn8Tg4GjNqHfbSh5nxW8n0GgEWrapQrUa+gt/RkY26zeI9+7IEc2xkFAeVSo167Zo1c7+htXJSwEh3H8chZWlgiG9jKudf2rr4I7s1QgrC+MK8/lbwdx+EomlhYJR3UwL1WZmq/hjv7buZrfGeW5vmZCawbqTYqm797s1NdgS1FT8evgCGkGgbfUK1CxTNKV1Dt9+SEBYBNbmCj7u3KJIzpEX/H7mCsGxCbjZ2fBZx+KfT1FhzpFT3I6MxsnaikX9umNh9uqruiXEMxd81akN1b08SMzI5MNte8lW5825/G/B+DZN8HV2JDI5lZ+Pny/u6fBhx+ZYmysICI3g8G3jLR/zi4YVfWlcyReVWsOyIwVLpzA3M+PdLqK69+fRq6Rl5o2821lb5tT3W7b3AkoT77MxPRtjZibn0p1QbgQZb79pZ2PJYBPreo7o2wgzuYzLN0O4Y6AskJ2tJQN6i8dbu0la9bS2tmD40KbimHXnyZIghDVq+dK8lT8ajcCK345Lnmv42Na4uNrxPCyeHRL5oHK5nM9m9cfR2ZbgB5GsXHhY8jjlq3rz7tSe4nwWHWbjr9Ln08G7nDvzNn+Au7cTz59EM3nIEiYN+oXbl4svMpCSkMbquXt4s9k37F0j5l++NbUXg8ZLdxvS4XlIDF8MX0ZiXCrlqpTinSk9JceFPo5m2QKx6P6bH3eiokTrzIS4VNYsOymOGdceB0d9dTHgegiXzj/CzEzOGAPEdNv2qyQmplPa25nOBvq2Hz5xl8ioJFycbOnRWVo5FASBVZvF51bvTrVxNtIHXad2upqodi7TvtQNbFcbVxP7q289fYuYxDRKuTrQp3neneh/HhOfIVV8PGhXq2Bh8aDwGA4GiC9M47s0LdCxDCFbpeKng+K9+EbL+ng6Fq+h6ElMPMvOiKasKV3bvDKtIgsbuwLvseH6LWTA97274u1oWqWF4kYJ8cwFlgoFP/fvgYOVJTfDI5l7tGjyDosbVuYKZvQUlae/LgUQ+DyyWOfj6WjHGy1FUvPTwbNkq/Jm2jEVH2hVz91X7xJSwFaa3RpWwc/DmcS0TNafzHth/kFta+PqYMPz2GR2n7tj0j7e7o70aiku2Ct2597NaFDnuiapnt6eTnRuIy6Yf241rKYO6NUAG2sLHgfHcO7SI8kxPbrXwcPdgdjYFHbvkb4ub73XFrmZjAtnH3IrQL+Uk62dJWM+6ADA+hWniYnSD3s7u9rx2ax+AOxcd4HLZ6RV2O7DmjJa2x3pzx8P8tfPh42q6t5+biza+xk932iJwsKMWxce8Xn/n5kybCkB5x68tH7tcZFJrPl+P6ObfcOmJUfJTM+mYk0fZq55lwHvtTe6b9ijKL4Y/htxUUmUreTJ7FVjsZTIJc3OUjJ30mays1TUa1aRPsOlicrKJcdIT8uiYpVSdJYoJq/RCCzXdjnq0acePr764fyUlEw2aZXMN0a3kHSpq9Qa/tqkLQTfryFWBvJfr94K486DCCwsFAztbViV/D+1s2fuaue5m8HcD4nC2tKckSaqnemZ2aw6qK272a1xnrsMxSWnsfF0AADjuhdc7VxyULx+XepUxt+7aBzd6y/c5Gl8Em72NrzZqkGRnMNUCILA13uPoVSraVXJj67VC97X/lXEg+hYph8QUwnGtWhMqwp+xTuhPKCEeJoAXydH5vcUc8j+uhrAwpOm11z8N6F5hbL0rFUFjSDw8eZ9xKaaVsewqPBmqwa42dvwND6JHw/ql9MpDNQuW4rW1cqh1gh8u/VYgSoYmMnlvNdNXKhXHrlCaHTeiKy1hTljuorGhaW7z5OQaloprzd6NMLMTM7lO2GcvyXddUgHe1urHNVzyYYzpGUYVmZH9WuMXC7j/LUnXLguXerJ3t6Kfj3F/NRfV54gQ0LptbBQMGqkSPDXrD1HhEQhd98yrnTvKRKYhQv2k5Guf5z2XWtRtaYPmRlK5k/fLpkz2qilP72Hid/B3C82c++mtDN/yPvteFObt7jul6N8O36N0Y5Fzu4OjPt2ACtOf0W3Ec0wU8i5cSaIyUOWMLbVbDYtPkJ8VMHSNaSgVqm5eOQ234xZzqgmM9iw6BAZqVmUq+rN9D/G8vO+iTRsW83oMY7vus5H/X8hPjoFP38v5q59F2c3fVd2draKWZ9tJPhBJI7Otkyc1V+ylNPpo3c4rK29Ov7zrpJF3HdsucyD+xHY2Fgw/A3pMOfSX4+SlpZF+XLutG0j/Rm27rzK84hEHB2s6dWtjuSYbKWKxatFY2SvDrVwM5KDuW7v1Ry1s3d742qnUqVm6VbxudO/bS2cHUyrebls70USUjLw9XCie9O8OdEFQWDBtlNkZquo6edFy+rl8rT/P3H89mNO3HmMXCbj/U5Fo3Y+iopj8VGR3H7YsTm2lsVbwmfhsfNcCXmGtbmC6d3bvZJ1LAuKhzGxvLl+GxlKFc3LlWFCy6JpBFBUKCGeJqJd5Qo5xeWXnrvE3GPShbH/7ZjStQ1lXZx4npjM++t3kZ6dN6NMYcLW0oKv+4gq19pzNzhwq/B7YQNM7NUaawsFlx89ZVUByyt1qluZBpV8yMhWMnXNQZND5jr0b1mTit6uJKRk8P2mkybtU8rNgUEd6gAw98+jRskkwJCu9fFycyAyNpnfNhluUepTyjmnDuK8Xw+TnJopfbz+jXB3s+d5eCJLV0jPuXOnmlSvVpq09Cy+nbNLkjSOHtsaVzc7nobG8cPcvZJ93D+d1gsbWwtuXQ/l1+8PSp7rrU86UbthOdLTspj6/mqCbkunIAx6ty3jZ/RFYW7GhSN3mNB7IXevhxi4GiI8SrvwwXeD+ePUV3Qf2RxrO0siQmNZPW8vIxvPYOZbf3D52B3UeWwm8E9EhsXx5/x9jG7yDd+MWc7FI7fRqDVUb1ieKb++weKDn9O0s/Gi8hlpWfzwxSYWTNxIZno2tRqX57s/38HJVZ+YKZUqvvt8E5dPB2FhqWDK94NxkSCnj4Ii+H7GTgD6D29KtVr6HWluBYTlqJ1j32+Hs0S/9cNHbnPoyG3kchkfftBJUtV7HBzNH2vE+/PdN1obbHu5estFHofF4uRgzegBhjvSBD+LY+V2kSCNH9oqV7Vzzf4rPHoWi6OdlclqZ2BwBOuOXgdg4qDWmOcx327flXscvC72Ev+if5sCkabIxBSmbxJTTka2qkd5T33VuaBIzczi43V7yMhW0riCL33qG38JKmpsvhrIsjOi2jytezt8nE1rafpvwt3IaIav3UJMWjr+Hm780Kfbv67n/L9rtsWMt5o0YFonMVdp1aXrzD5y8rUjn8421vw+oi9ONlYEPo9i4tb9xVrHtF21CoxtLT70p207wqOowm/v6efuzOQ+4ve6+OB5boXm30kvl8uYNbIL9taW3A6NZPnBvOWOWpgrmD6qE3KZjAOX73Mm0LSi8u/1a04pNwci41L4dZtxddjW2oLJb4smlG1HArgZZLhF6TvDWuBTypnY+FQWrZTOh7S3s2LyJ2Lv9N37A7h4VX/OZmZypk7phZ2dJffvR7BylX7KiqOTDV/N7IdCIefU8XtsWqcf4i9Tzp1JM/shk8HebVfZu03/RcHC0pxvfhlJzfp+pKdmMfXd1Ty8K+367zG8KT9tGY93WVeiwxP5fNhvbPrteK7hc68yrkyYM4j1177l0x+GUa1hOTRqDRcOB/L1G78zuuk3rPl+P9dO3uPaqfsm/x3ffoUpw5byZvOZbPzlMHFRSTi42NLvnbYsOz6Z77d/RMsedXMtKh98P4IP+/3M0R3XkMtljPiwI3MMkE6VUs3cLzaLZiILBTN+HkHthuX1xiXGpzHjs41kZamo36QCb03ooDcmOiqZb7/ahlqtoW2H6vToo1/A/emzeBb+LJbGGj2yBbVq6pPXbKWK2T/sQ6lS07xxRYNO9nuPIli3Q/yNffZOB4O5nWqNhjnLD6FUqWlWtzxdWhhXIoPD41i5W3vc4W1NUjuVKjUz1xxBIwh0a1yFljX1r6ExPI9N4rstonL7Xtem1PQrlaf9X4Rao2HK+oMkpWdS1ceDj7o1z/exDEEQBKZuPUxwTAJejnZ8P6R4CdCZhyF8s080DI5r3Zh+dQvW5elVxO2IKEat20piRiY1SnmydsRAXGzy1oL1VYBMeIWZU3JyMo6OjiQlJeHg8OokzW66Eci0/WJuxcgGdfiqU8HeTF9FXA8L540/t5KtUjO8UR2+6lZ8n1Gl1vDuqu1cfPyUcu7ObBw3FDsrw23w8gNBEPjir/0cDHhAaRcHtn42okDnOHQ9iEmr9iOXyVjx0UDqVshbKY+ftp5m7ZFreDrbseXrUdhZ5z6Xy3dCmbBgGzIZLJ86hFoV9U0hL2LWsoPsO3WHst4u/DlnpEG38O2gcMZ9tQGNRuC7SX1o2Uja7PDL78fYuusaLs62rFryJk4SvbZPnwlixswdAMybM4iGEgRnz85r/Pz9QWQymL1gCA2bVNAbs3HVGVYtPY6ZmZy5S0ZSq76f3piM9Cymvv8nd2+EYedgzbw/xlChivRinpaSyeKvt3NSG0Ku27wSn38/RDIkbQihDyI5tOECx7ZdMbndpjHUbelPl6FNadKpJhaWpuUJCoLA/g0XWTZ7D8psFa6eDnzxw1BqNda/hiCG8udN3sLpQ7cxNzfj60XDaSBRa1OpVDNp3BruBIThU8aVRavHYmf//4aNrCwln4xbw8OgSCpU8mThr6P1cjKzs1VM+Ggtjx5FUad2GRbMGyIZqv9t5Uk2bLuMk6MNq5e+ibOTPqHMylbx1udrCHkWT/vmVfjm0x4Gr8uWQ9f58c8T2FhbsH7+aDyNtLvUaATembOJW4/CaV6rHD9+YrjP/YtYtucCy/ZexNnemq0zRuepJ7tKreGtRZu5GRxBnfLerPhoYIFI3PKjl/n5wDmsLczZ/Olw/Nyd830sQ1h1+irfHziDwkzO2ncGUatM/olyQXEvIprhKzeTnq2kd+2qzO3b+bVbkwPDI3lzw3aSM7OoW7oUfwzpi30hr4MFQV74WonimQ8MrluT2d07IgPWXg1g5qETr53yWa+MN/P7dUEmg3WXA1h94XqxzUVhJmfBkG54OdoRHJPA1K3GzSD5gUwmY9qA9ng7O/A8PplZ24y7nXND53r+9GhUFY0gMHXNQVIy9FtCGsN7vZri4+ZIVEIqv+wwLb+1UfWy9GhRHUGA2SsPk51LF6UPh7fBxdGG0PB4Vu80rMzW8PdmSC/RMDD/t8MkpUjnnr4zuhVlfV2JT0jjxyXS31Grlv700uZyzp2/l/h4/eL9PXrXo2vPOggCzJmxk+cSHYsGv9GCNp1qoFZrmPXlFiLDE/XGWNtYMmvpaKrW9iU1OYPJ76wi+IG0ac7W3oovfhjKx3MGYmllzo1zDxnf8yduSNQNNYSylb145+u+rL0yky+XjKZB26qUr1Y6T3+Vavky9MNOrDo3nTnrx9GqZ12TSWdqcgZzPvyLxV/vQJmtomHrKize9bFh0qnWsGDqNk4fuo1CYcZXPw2TJJ2CILBk/n7uBIRhY2vJjB+G6JFOQRBYtOAAD4MicXC0ZsacAZJGoN//OMmjR1E4OFgz+cuekqTz5u2nbNwuhks//6CzJOkEWLHxHCHP4nF1suXTtw2bqyJikvl1o7b70pCWRkknwNbjAdx6FI6NlTmTRpvW6/rR81hWHBDn/MXgtnkinQArj1zmZnAEdlYWzB7VpUCk82ZoBEsOiQ7/KX3bFgnpvPzkaU7e/eQebYqVdEYkpfDuup2kZytpUs6Xb3t1fO1I560XSGc9n1KsGPpqkc68okTxLAC2Btxm6r4jCMCw+rX5unPb1+6GX3nuGvMPn0Ymg4WDetC5WqVim8utsAhG/r4ZlVrDxK4ti8Q9GRAczhtLN6PWCMwZ2pmeDfKfs5SakcXgeX/xPC6Zvk1r8PUw4+Vu/okrQU9590exl/byzwZSv7JPrvskpWYweMqfxCen81avxrzbz3iI7cTlB0xZuAczMzmrZ4+gYhlp16uoLq0l5FkcHVtW5euPu0uOe/Aokvc+/Qu1WsOUz7rTuZ1+uCsrS8n4D9bwJDiG+vX8mPfdYL0cv+xsFRM/+It7d57jV86dn5e9odcDPDNTyWdvr+LR/QjKlnfn+2Vv4OCkr7KmpWQy5V0x19PR2ZZ5K8bgV9HT4DUJfRjF3I/XEfIgEplMxuD32jLiw46YvcJdT+4HhDH3k3VEPUvATCFnzOfd6PNGC4MhebVaw4/TtnNsbwBmCjlf/TBUsjMRwJa15/jj56PIZPDtT8No2Fz/GbBjyxWWLjqM3EzGdz8MpV4DfVPM+QsP+Wr6NgDmzBpAk8b6ynlaehZjJqwmMiqJbh1rMulj/cL1ALcfhDNuqqjCz/2yDy0aSqvwgiDwybztXLoVQp0qpVnylf699iIi45IZMvVP0jOVfD6iHQO1udPGoNZoeHPeJm6HRNK6dnl+fL9XntaB+0+jGfH9BlQaDbNHdSlQa8yUjCwG/vgXz+OT6VrHn3kjjHewyg+iklIZuHgdcanp9KpblTkDi09dTMnMYvjKzTyIiqWShyvrxgzC4TUrnXTzeSRjNmwnJSuLej7e/DGkL3bFbOCSQoni+ZIwoE4N5vTohAxYf+0m3xw8jubV5fH5wpvN6jGsUW0EAb7YdoAbT413yClK1CpTisk92gDw48GzXHos7VguCOqU8+a9TqJDcNb244TFJub7WHbWlnw7ojMyGey4cJvz90LztH9Df1/6tRRz22auPUKmROH0f8LRzpqJI9oBsHrfFR49jTE6vm2jyrRpWAm1WsOc3w8Z7JpkaaFg6gddkMtlHDlzj9OXpJXAyhW9eHOYSHYX/XqEKIluP5aW5kyb2hsrK3OuXQ9h42b9MlAWFgqmz+6Pi6stIcExLJizR09BtbIy5+sFg3FxtSP0SQyTJ6wlJVlfjbW1t2L2b6OpVM2bpIQ0vhy7krAn0QavSdlKnvy0dQJdBzdGEAQ2/nqcL0YsI0bCjV/c0Gg0bF1+kolDlxL1LAEvHxd+2DiOfmNaGSSdGo2GhTN2cGxvAHIzOZPnDzZIOndsuMgfP4tpRWM/7ChJOm9eD+W3xUcAeGdce0nSGROTzPzvxdqgA/o3lCSdAL/8fpzIqCS8PB2Z8E47yTFZWUrm/HIQjUagc+tqBkknwIEzd7l0KwQLczMmvy1tYtJBEATm/nmU9EwltSt507+dad2GNhy7we2QSOysLZkyzDSFVAelSs30dYdRaTR0qFOJbg2qmLzvPyEIAt9uO8bz+GRKuzgwbUDe5mIKslVqPl2/l7jUdCp7uTG9T+Gfw1Qo1Wo+2ryXB1GxuNvZ8NvwPq8l6XxzwzZSsrKo7/vqks68ooR4FhD9a1fnu55a8nn91mtHPmUyGVO7tqGtf3myVGreX7+LkLiC1bssCAY3rkWvumII+/ON+4kqYJ91KbzdvhH1y5cmPUvJF3/tR1kAh3K9ij4MaVUHgJkbjpCax5D7R/1b4u5ky9PoRJbtyb07EUD7hpVoVbcCarWG2auO5GoO++yNdtjbWHLvSRSbDlwzOK5qxVIM09ZI/P73IwZD7kMHNqZ6FW/S0rOZ8+M+ycLyZcu68cF40ZyyctVp7tzVNzi5udkzfdYAFAo5Z07eZ8Na/cYGHl6OzF06CicXWx4FRTJlwl+kpui77+0crJmz7A0qVClFYnwak8auJPSxYfJpZW3Bh7P68+XCYVjbWnL3WgjvdfuRpd/s5EHg02JPrUlJTGfvuvN81O8XVszfj1qloWXXWize/RH+tcsY3E+lVLPom10c2XUDuZmcL+cNokUHaRPG7s2X+e1H0QQ07K1WDBjRTG9MVGQS307fjkYt0L5zDfoN0u9jrlZrmP3dHpKTM6hcyYuxY1pLnu/MhYccOBKITAZTPu2GrY10KHHFpvOEhYsh9o/elC5MDxCflMaiv04C8Fb/ppQpZdzVfejCfc7fCsFcYcbUN42TVB2exiSydJd4X34yoCXuTnkrnL7i8GUePI/BydaKKYMKVvpn99W7HLghOuLnDu+KvQl54XnFgv2nCQiLwN7KkkUjemKdS2WAooIgCHy95xjnH4dhY2HOb8P7UNrp1YuKFgQ3n0fw5oZtpGZl08C39GtDOqGEeBYK+tWqztyenZEBG67f4usDx14r8mkml/PDgG7U8PYkMT2Td//aSUKaaTUmCxsymYzpfdpT2cuNuNR0Pl2/l+wClq75J8zkcuYO64qDtSV3nkbxy8GCdXL6sGcLfNwciUxI4addhssXScFeq6IArD1yjbshuRf2l8lkfDGqHbbWFtx5EsmmI8aL2bs52/HBCJEMLN96nmdRiQbHjhncDD8fV+IT01m4QjoPVmEmZ8pn3bG2Micg8Clbdl6RHNelcy3ata2GRiMwa84uUiQIY/WaPkz4RKyhu3r5SS5d0C9SX7a8O/OWjMLRyYYH98KZ+uFfpKXqE3x7Rxu++/1NylX2IiE2lQ+H/srB7VeNksjW3euweNfHVKrpQ3pqJnv+Esne+91/ZOvyky+1f7tapebyiXvM+fAvhjX7liUzdvLoznMsLBV8MLMfkxcNx9ZIf/LIZ/FMfPMPDmmd7p/P7k8rA92C9m+/xpIFYivOwaObM+rdNnpjsrKUfDNlK0mJ6VSq7MUnX3STJE5/rT/PrcCnWFtbMG1qb8m2mHHxqSz4WSyPNaRfI2rX0He6gxhi37hHrGTw+XudcDDyeX/88zjJqZlUKuvOsG7G03ISktP5Yb3oKH+rVxP8vHMvPSQIAt+uPUqmUkUDf1/6NJe+loYQ9CyGPw6JeaGTB7bDxd60OqFSCI1JYPZ2cf7jOjeljp9xY2F+sPfGPdZfCABg7qAulHF1KvRzmIpfT11i+407yGUyfhrYnerehlNn/o0IeB7BG+u3k5qVTcMypVk+pA+2Fq8H6YQS4llo6FurGvN6ieRz041Apu8/+lqRTxsLc34d1htvJwdC4xP5YNOeQid8psLawpxFI3pib2VJQFgEC/YXfjcpL2d7Zg4WSw6tOnGVCw/yFiZ/EdaW5swYpi1fdC6Qi/fzdqzWtSvQuaE/GkHgm7VHTKoN6uFsz0eDWwHw27ZzPI8xXty8R+saNKhehqxsFXOXGzZvWZgrmDIh95C7j7cz48aKatTyP8/w8HGU3hiZTMYnH3WmVCknoqKS+eGnA5Ln7d67Ht1710UQ4LtvdvLsqb7ZyK+iB98tGYm9ozX3bz9n2sfrJIvQOziJ5LNO4/JkZSpZOGMncz7fRKpEiF4H77Ku/LRlArNWjqVNjzpYWCoIfRjFivn7GdlyNtPHruTMgVuS/eELAyEPIvlj7l5GtprD1++s4syBW6iUam3byx78eWoK3YY2MaqWndh/k3GDlnD/1lNs7a2Y8v0Q2naTDiUf2n2DRd/tBcRanW+O1w+nCoLAT/P38/BBJI5ONsyYMwBLS3316+bNMNb+dQ6ATz/uTOnS+kYXQRCYu/AASckZVCjnzpiR0gXns7JVfLdYG2JvVY0WDaVNUwCnrz7i2MUHmMllTH2ns2RXpBfxw/oTJKVmUtHXjVG5kFQddp67zdWgp1iZK5g2skPeQuxqNV9rQ+zta1ekU738d9dRqtR88dd+MrKVNKzgw1vtTKs5mhc8iIzl6x1iysW7bRvTpmreSkUVJg7cDuLnE2L0Z3r3trSuXLAi+68abjwL583120nLzqZRGR+WD+77WpFOKCGehYo+Nasxv1cX5DIZmwNuM+01I5/u9rb8PrwPtpYWXA19zrf7jxdbyLGMqxNzB4ltD9dfCGDvjXuFfo72NSsysKmYYzll/UHiUw13tskNDSr5MLiluNB/s/5Innu5fz6oDU62Vjx8FsufB00rct+7dU3qV/EhM1vFd6uPGP2uZDIZX47tiKWFgmt3n7LnZKDBsdUqmRZy79mlNs0bV0SpUvPtgr1kZuoTM1tbS6ZP7Y1CIef0mSD27A2QPNb4jztTraYPaalZzJi8hfR0fUWzQmUvvls8Ejt7K+7cfMq0j9eTKVFM38nFljnL3mDMx50wU8g5c/g24wYu5raR4vFmZnLqt6zMpJ+Gsf7CND6c1Z9q9cqi0QhcOXWfOR/+xfDms1j89Q6CboYV+HeRnJDG7rXn+LDfz7zf/Ue2rThNQkwKDs629B7dgsW7Pmbpnk/o+2YrydqcOqSnZfH9V9uY9+UW0lOzqFanDEs2jzcYXj+y7yY/zdoNQJ8hjXn7I2mH8I4tVzh26DZyMxnTZvbDw0u/UHdScgaz5+5BoxHo0rkm7SWMZgDb91zn8rVgLCwUTPu8p8EWkys2nSP0uTbEPsZwiD01PYsFq8R6jsO6N8C/nHE17GzAEw5fDEIuk/HVmE65klSA6IRUftoivvCO690MX3enXPd5EauPXOX+s2gcbQoeYv/lwDnuao81Z1jBHPFSSMnM4qO/9pCpVNGsUlnGdyi+Ljm3w6OYvFMsiv9G03oMaWhaHu6/BdefhTNmww7SsrNpXNaH3wf3waaY0hmKEiWu9iLA7tv3+GL3ITSCwIDaNZjVvQPy18jtfupBMO+t34kgwNSubRjZRL9X88vCz4fPs+zEJazMFWwYN5TKXm6FevyMbCVDF67ncVQ8raqWY/FbvfO9SKRnZTNgzlrC45MZ2KIWUwcb76/9T+y/dI+vVh7EXGHGhqnDKe/tmus+T6MSGPbVGrKUaqa/1ZkeLY0XVd6w7yo/rzuFnY0l6xe8gbuB9oMvutw7tarK9I+kXe6JSem8OX4V8Qlp9O1Rl4/fl3b2b9l6mV+XHcfCQsHSxaMoX85Db0xcbArjx64kLjaV5q38mT6rv2QeXtCd53w5fi3paVnUaVCOb34aarDHd1DgM+Z+uZmIp/HI5TKGvduWoW+3NtnB/iw4hqM7rnFsxzViI/9WlX0reNChb32q1CmTp/slOSGNE3sCuHT8LiqlqGybKeQ0aluVjv0a0KCVP+YG6q3+Ew/uPGfupM2Eh8Uhl8sY+nYbhr3bxuBnO34wkAVf70CjEeg5oAHjDYTOb1wL4ctP16NRC4z7qBN9B+orbIIg8NX0bVy4+AhfXxd+W/IG1hKdh56ExPDux2vIVqr56L0OOe1X/wlTXewAc/84wq7jt/D1cmbN3JFGOxSlZmQxZOqfRMenMqJLfT4cIp1/+s/P9snS3Zy+9YQafl6smjQ4T2TvYXgsQ+evQ6XWMGd01wIZis4HhfLu79sBWPhGT9rXNHxd8gONRuCjdXs4fvcxpZzs2TJhOM62xVOwPDollYG/byAqOZVWlfz4dVjvf13HHmO49vQ5YzfuIC1bSZOyvvw2qPe/inTmha+VEM8iwp7b9/l890E0gkCP6v7M7t4Ra/N/z02UG1acu8qCw2cwk8tYNKgHHaoW7gPPVKg1Gt5bvZPzD0PxdXHkr/cG42YvXfcvvwgKj2HYog1kq9R80r0FYwoQyrocFMY7i8WSMr+81ydPvZgFQeCjJbs4GxhMDT8vlk8ciKUBdehFrNl/hcWbz2BvY8lfM0dSys3wb0mt0fDO1xu4+ziSZnXLs+CzPgZNFncfRvDelPVoNALffNqD9s2lF9Ar14OZOG0LAN9M7k2bFv56YzQaganTtnLp8mPK+LqyaOEIHB30F7i7t58z8YO1KJVqhoxoxph3pRsb3At8xpQP1pKelk3tBn589d1AyVJLIKqCS2bv4ZhWba1Sy5f3v+yOf43cy1fpoFZruHXxMUe2X+X84dtkSai7eUWFat507NeA1j3qGFU1/4mM9Cw2rTjN1lVnUanUuHk6MOm7gdSUcJvrcGDndX6euxeNWqBb33p88GUPye895EkMn32wluSkDDp2qcnnU3tKXv/1Gy/wx4pTmJubsfSXUVSooK86pqZlMeHzdQSHxtKkQXnmzugveazUtCze+XIdYeHxdG5VjWkfdTP4OS7eDOGTeeLva8m0QdSrKp0rCuLvadbKw+w5cwcfD0fWfzsKK4l0gX9i74W7TF99CIWZnPVTh1OxtOkvuxnZSsYs3My9p9G0qVmBn96Wvn6mICIhmWGLNhCbks6gprWYNiBvL7KmYPGR8/x6/BLmZmasfXcQNX29Cv0cpiApI5Oxa7cT+DyKCu4ubBw75F9dx/KfOPHwCZ/u3C+STj9flg3q/a/jCyXE8xXB3jtBfL7rAGpBwN/DjZ/79aCca+EX8y0OCILAlJ2H2RFwFzO5jG97dSy2FmUJaRkMWrye8MRkKnm6surtgYX+Vr7hbABzdojJ+zMHdaRv47wZCV7EnM3H2XzmJtYW5qz4aCDVypieGB8Zn8KgmWtJzciic0N/Zo/pmqv7VqXW8NasDdwLjqJ8aVeWTxmMva3hsiOPwmIY89U6lCo1Q7vV58MRbQyOXbbuDGu3X8LC3Iyfpg+kdjVpsrbkj+Ns3nEVc4UZ333dj4b19ElQYmI6b7+3kri4VPz83FgwdwiuEoTrwN4Afpy7D4B+gxrx3gfS+XV3boYx9UMx19OzlCPT5g+mkoHORQDH9wWweNYe0tPEMH6HXnV588OOuHrk7dmTlpLJ2YO3OLH7BvHRKXna10whp16LSnTo24ByRuYqBY1Gw7E9Aaz6+QjxMeJ5m7evxscz+mAv0UUKxHqpSxcc4MBOsUFE5151+HhqL8l76sH9CCZ/toHkpAwqVynFj0tG6uV1CoLAX+vPs2q1aKL76INO9O6lr2JmZGbz+bQtBN59jouTLSsWv4GLRE/3bKWKibO2cf32U1ydbVn70xsGDUWPwmJ475uNpGVk069DbT4fo9/S80WsO3CVRZvEGsWLPx9Aw2qGqwHocO3BM8Yt2o5Spea9nk15p4fpYWeVWsPEFXs4GfgERxsrtk4Zibtj3lzwOsSlpPPGks2ExCRQ0cuV9R8NLXSH+R+nrvCTtkj8jL4dGNhIum1pUSM6JZWxa3fwICoWR2srNr89lLLFaGwqTKg1GhaeOs+y86IBs6mfqHT+20gnlBDPVwqXQ5/x8Y59xKalY2dpwfyeXejgbzgp/t8ElVrDtN1H2BFwF4AvOrViTPP6xTKXsLhERv++mejkNKqUcmfF2AE42RReTTdBEPhhzxn+PHUNuUzGwjd60rZG/r5HpUrNhF93cOnBU1zsbVjz6WB83JxM3v/SvTA++HkHKo2GNzo34MN+LXPdJyouhTHfricmMY0GVX1Z9Fk/zI2Ek4+cv8/0xSK5+3R0WwZ2lg6BqtUavvp+N2cuP8LO1pKls4ZSvoy+AqRWa5g5fw8nzwZhaalgwcyBks7l0NBYJk7aSFxcKt7eTnw/bwheXk5643Zuu8KSn8Rcr6496/DRxK6SXXCCH0Ux84vNhD+NF93fk7rTqWcdg587NiqZVYsO56ifVtYWDH6rFf1GNcfSQLj+VcCdG6H8Nn8/D++IZalK+bow9tMuNGtX1aCiFh2ZxLeTNvPgbjgyGYx+vx2DR7eQJJ2BN8P46ovNpKdl4V+1FHN+GIrDPxRpQRBYtvwEm7eITu3Ro1owakRzvfNnZauY/M02rgWEYmdryaK5Q6lYXj+1QqMR+GbhXo6dC8LG2oLFMwdTubz0S1p0fApvT19PdHwqdar4sGhyf4O5ogDHrjxg8hLRQPXRkNYM75L7c+tJRBxvzt9ESnoW7etVZN7b0qqwFARBYNamY2w7F4iluRm/TehP3fJ5a6OrQ1pmNmN+3cLdZ9F4Ozvw5/hBeDmb3trVFPx59jrz950C4OPOzXm7jX6ZrJeBsPhExqzZxrOEZNztbfljZD/8PQs3naq4EJ+ewSc79nEhRKxHPbJBHSZ1aIWF2avbqMIYSojnK4bolFQ+3rGfq0/FReH95o34sFXT1yI/RRAEFhw+w8rzYv3Ht1s05NMO+ovNy0BwTDyjf99CXGo61Ut7smJs/0INxwiCwPRNR9h55Q6WCjOWv9efuuXyt3ikZmQxZtEWHjyPoYy7E6s/GZynciq6cB/A5GHtGNg69yT7B6HRvPPdJtIzlXRvXo3pY413HFmz+xK/bjyLTAZzP+lNqwbS6RRZWUo+nrmVwPvP8XC159c5Q/GUCOcrlWqmzd7BhStPsLG24KfvBlOlkr6qFxGRyMQvNhARmYSbmz3fzxtCmTL6+ayH9t/kx7lindCWbarw+ZSeet2NAFJTMpk/fQeXzj4AoEf/Brz3WRfMzQ0/4IMCn/Hb/H3cuykuCh7eTrz5QUeata/2yhBQQRAIfRzNht9PcuqgaAazsbVk6Dtt6D28qWTZIh0CrgYzZ8pWkhLSsXe05stv+9GgqfT3e+50EN99s5OsLBW16pTh2/mDsPlHjU21WsPPvxxmz74AAN5/rx0D++uTFZVKzfQ5uzh36RHWVub8MHsw1avol/4RBIFfVp9k895rKBRyFkzpT8PaZSXnl5aRzfszN/IwNIYypZz5/ZuhOBppWXnrUTjj520hS6lmYPvaTByRu7knNimN0fM2EhGXTK3ypfjtkwFYmZhrC/D7wYss3XcBmQx+eKsn7WrnLzVJqVIz7o+dXHwYhoudNWsmDKZsIbfE3HDhJrN2i6XSxrdvwrgOTQv1+KbifmQMY9duJzY1nTIujqwc1R8fZ30T278RgeGRfLBtL+HJKVibK5jdvRM9quunIP2bUEI8X0Eo1WrmHzvDn1fEmooty5fl+95dcbYpnkTtwsYfZ6/w/RExLDOwXg1m9GxfLMT6YWQsbyzfQmJ6JnXKlOL3Mf2wLcSiuyq1ho9W7eb0vWAcrC35c8IgKubT0BSTlMqoHzcREZ9MjbJeLP9gANYm5JjpsHzfRX7dfQG5TMaP43rRqlbuJU7O3wrms4U7UWsE3unblLG9DS8qgiAwf+VRdh67haWFgiVfDaJ6Renwb3JKBuO+2kDIs3j8fFxZOnsoDnb6inNWlpJJM7Zx41YYDvZWLJo7lPJ++m06Y2JT+GLSRkLD4nBysmHed4OoVFE/v+z0iXt8981OVCoNZf3cmDarP2X99L8PjUZg/YrT/LX8JIIAVWv68OWs/nh5Oxn9/CcP3GLFT4eIjRLrdVpYKqjTuAKNWlamUSt/PEoZ3r8okJ2l5NaVYC6fecClU/eJ0vaol8lkdOlXn1ETOuBsJB9UrdKwbf0FVi05hkYjUNHfi2nzBuElUeZIrdKwYtkJtmwQO0s1alqB6bP664XXVSo18xbs49jxu8hk8OnHXekuUapJrdYw+4d9HDt1DwsLBfNm9KeeATK5ftcVlq4RFbfpH3enU0vpzkoqlZqJ3+/k0q0QnB1s+GPmMLw9DJOTZ9GJjPl2A4kpGbSsU575H/bK9TmVnpnN2B+2cD8sGl8PJ1ZPGpKnXuw7L9xmxnqxs9Pkge0Y3Cp/TmyNRuDL9Qc4cCMIawtzVo0bQPVCzrnceiWQr7drO1W1bsjHnYtHRLga+oz31+8mJTOLKl7u/DGyL252hZu7X1zYGnCbGQePk61W4+fixOL+Pans8e9XcUuI5yuMPbfvM3XfETJVKnwcHfilfw+ql3o9it9uuRbI13vE4vkdq1bk+/5dTTK/FDbuhUcz5o+tJGdk0aBcaX57o2+h5j9lZCsZ+9s2boVG4Olox18fDMl3qCs4Mp43ftpEUnomrWqU48exvVBIhIylIAgCM9ceYde5O1hZKFj+2UCq++W+EO04eYvvVouLy4y3u9CtueF+9Cq1hkk/7OR8QDDODtYs/2YYpT2dJMdGxiTz/pT1xMSnUruqDz9OH4ClhCqUnp7FZ19t5m5QBC5Otvwyfyg+pfULdiclpTNp8mYePIzE1taS72YPpEZ1/RzS2zef8u307cTHpWJlbc5nX/agTXvpz3Tl3EPmTttOakom5uZm9B7ciCFvtsRewsikQ2ZGNtvWnOPQ9qtER/x/PVS/Sp40auVP41b+VKnlKxnuLyhio5K5ciaIS6eDuHHx8f8Zl8wtFNRvVpGR49pTwUhOqCAIXDrzgBWLjxIWHAtAx+61+eDL7pIKblxsCrO/3kGgVvHtP7gRY99vp1dqKDtbxcxZOzl/4RFmZnKmfNmTtm30SaIgCCz45RD7Dt1CoZAz+6u+NDFQh/Pw6bvMXCS21xw/ujVDe0mb+QRBYO4fR9h9IhArSwVLvhpMtQqG7/+k1AzemrWRsMgEqvh5smzyoFxf9FRqDZ/+upuzgcE42VmzetIQyng4Gd3nRZy5E8zHv+9CrRF4q1MjPujZ3OR9X4QgCMzfdYq/ztxAIZezZGwfmvlLk/b8Yvf1u0zZeghBgFHN6/FF91bFQjpPBD3h4817yVKpaVC2NL8O6/1aGImyVSq+PXySTTfE6ET7SuWZ36vLa/HZoIR4vvK4HxXDhG17CEtIwlJhxjdd29OvVvEYcwobh+8+5LOtB1Cq1TQp58viob2Kpc3X7WeRvPWH2G6sacUyLBnVu1BJcGJaBqOXbOZJVDzlPV1YM2EwjvnMKQ14Es67i7eSpVTTt2kNpg81vRi1Uq3mo8W7uHg3FFcHG9ZMHkopl9x/K4s3n2HN/isozOQs/mIA9fwNO7jTM7N5f+YmHoREi6HMGUNxNGDweBwaw/ivNpKankXrxpWY+VlPSTKWkpLJx5M38ig4Gg93e36ZPwwvCaUqNS2TqV9tJfD2M6yszJk5ox8N6usbkxLiU5kzYycB18Xi/H0GNOCd8R0kw+kRzxJYOHsPAVeDAbBzsGL4W63oMaCh0fC0IAiEPorm0un7XD79gHs3w/6vHai9ozUNWlSmUcvKNGheyaChJzeo1Roe3H7O5TNBXD4dxOP7Ef+33dXDXkt2q1CnUXmsJNILXsSDu+EsX3SYW9pr4+BozZgJHejSu67kfXbzeiizZ+wgIT4NGxsLPpvcg1YSvdwzMrL5avo2bgSEYmGhYMb0PpI92AVBYMny42zZJXZM+npSL8nKBgBXboYwcfZ21GoNg3vU5wMjLTFX77zEss1nkctkzP20Fy3rGw5fK1VqPliwjetBz/BytWfltKG4mdDecu6G42w+eRNLczOWfTqQWuVNN3wFhkTy9i9byMxW0bNRNWaO6JRvIrfi+BUW7hMjSt8N60KP+tIKcH5x4FYQX2w8gEYQGNKkNl/1alsspHNnwF2m7jqMWiPQ1r88Pw3sjlUxiBeFjYjkFD7Ytpdb4ZHIgI9bN+Pd5o1eqzKLJcTzX4CkjEwm7jrIqcfi4jesfm2mdGz9r00sfhEXn4QxbsNu0rOV1PD25PcRfXCxzX87uPwiIDScsSu3k5GtpGVlP34e2RMLReE9xCITUhj+y0aik1KpXbYUy9/rn29l9cStx3z2xx40gsB7XZvwXjfT86pSM7IYs2Azj57HUrG0Gys/H4RdLn2aNRqBKUv3cvzqQxxsrVg5bShlvAznisUmpPL21+uJjE2hZmVvfp4ywGB9xOu3w/js220oVWr6dqnDp2P1O98AJCSm8eGkDYQ9i6e0txO/zBuGq4s+GcjMVPL1N9u5cjUYc3Mzpk3tTYvm+p1e1CoNq1ecYqO2p3uVat5M+7Y/Hp76zw5BELhy/hF//HyE0CcxAHh5O/Hm+Pa07ljdpEU3OTGda+cecul0EFfPPfy/7kdyMznVavviW949Twt4emoWNy4+JikhLed/MpkM/5o+NGpVmcatqlDe38ukY0aGJ7Jq6TFOHroNiGkCfYc0ZvAbLbCVSIPQaAQ2r7/Aqt9PotEI+JV35+tZ/fGRyK9NSclk8tTN3L0XjrW1BbO/7U8dA2HzFWvPsGaj2Glm8ifd6NJBuiJE0JMoJkzbSEamkvbNq/D1x90Nmnf2n77Dt7+JLTY/Hd2OgZ0N1xIWBIFv/jjE/nN3sbWy4I+vhlDBJ/fQ5sbjN5i/6SQAC97tQft6lXLdR4ewmERG/7iRhNQMmlUty6J3e2Oez2f7zst3mLZJNNJ93qs1o1pLG/3yi6N3HvHp+r2oNQL9G9ZgRp8OJpumChN/XrjOdwfF9Io+daoxq1dHk6M/rzIuhjzl4x37iE/PwNHKkh/6dKNVBb/inlaho4R4/kugEQSWnLnI4jMXEYC6pUuxqF8PvBzyV2LjVULg80je+WsnCekZlHNzZsXIfng7vfzv8GrwM95dtYNMpYp21Srw47Du+V4ApPAoMpZRizeTkpFFq6rlWPhmz3wff+vZW8zaJHZcmTakA/2bm16+JCI+mVHfbSAuOZ3mNfz4aVzvXB/amdlKxs3dwu0nkfh6OrFi2lCcjOSuBT+L490ZG0hJz6J1w4rM/qinwfy44+eD+PrHPQgCjB3SnDcGShPp6NgUPvhiPZFRSZQr68aiuUMl63dmZ6uYM3cPp88EIZfLmPR5dzoaIDAXzz1k3re7SU3NxMHRmslf96FBI+n8V7VKw+G9AaxZdoL42FQA/KuX5u2POlKzrumhTLVKzb2bT7l8JohLp4IIfRxt8r5SsLGzpH6zSjRu5U+DFpVxcjE9vy0lOYMNK8+we/NllEo1Mhm071ab0e+1lewwpNtnwZw9XDgrtkDt0LkmH07sIln0PSEhjS++3MTjJ9HY21sxd84gqkoYhADWb73EslVad/T7HejbQ5o0PYtIYNxXG4hPTKdeDV++/8qwK/1yYAifzt+BWq1heI8GTBhmvOj7il0XWbbjPGZyGT9+0pemNf2Mjgc4E/iET5bsRiMIfNivBW90Nr12b1xyGqN/2sSz2CSq+Xrwx0cDscln1Of03Sd8uGo3ao3Am23q82nPVvk6jiGcvPeEj9btQaXW0KtuVWYN6PTSc/MFQWDR8fP8dlqshvBG03p80alVsZDfwoQgCKy8dI3vj59FLQhU9XRn8YCe+Dq9Hgapf6KEeP7LcOLhEz7ffZDkzCzcbG1Y1K87DcuYXrz6VcWT2HjeWrOdiKQUvBzsWDm6P+Xd9HP5ihoXHoUy7s9dZKvUdKpRiQVDuhXqm/SN4Oe8/ds2slRq+jSszszB0m0GTcGSvedZfugSZnIZP7/bh+bV/Eze905IJG9/v4VMpYohbevwxRDDYUod4pLSGPPtBiJik6lduTRLPjdehubGvWd89N1WlCo1AzvX5ZNRhkNyW/dfZ+EK0R370Zh2DOwuTTrCIxL5YNJ6YuNS8a/oyY9zhmBnq6/YqtUavv9hP4eO3EYmg48+6EyvntJKV0R4It9+tY2HDyKRyWDkmy0Z/kZLg4tZZkY229ZdYPOac2RmiDmUTVv789aEDvhKmJVyQ+TzBK6de0hifGqe9jMzk1O1dhmq1y2LwojrXgrZ2Sr2bLnC+pWnSU3OBKBuo/KM/bADFf0Nh4gfPYjkm6nbiIxIxNzcjPEfd6JbL+kwfHR0MhMnbeTZs3icnW1ZMG+wZJcpgB17b7DwV9FU8+4brRk2sLHkuKjYZMZ/tZHImGQq+rmzeKb09w/wICSa97/dRHpGNh2bVWHGuG5GCcqhi/eZ9puYL/rl6A70a1vL4NicczyNYcyCTaRnKenTvEae+rBnZCt5++et3A6NxMfNkT8/GYyrQ/5MMQEh4bz92zYylSp6NajKt4M7FyoZO/cglPFrdqFUq+lSszLzBnd96QqjRiMwc99xNl69BcCnHZrzdouGxRLmL0ykZWczZe9hDtwTX+T61KzKN13b/yvrc5qKEuL5L0RYQiLjt+4hKDoWM5mMSR1aMbqh9MP/34TIpBTeWrudxzHxuNrasHJUP/y99F3MRY0zQcFMWLsblVpD99pV+G5Q50J9sz9x+zEfrxZD5WPbN+Sjbi3ydRxBEPh63WF2XxLDgqs+HkTl0qZfr2PXH/L5MrE+4ReD2zCkXe7tTJ88j+OtWRtIy8ima9OqzHini9H77ujFIKb9LJ7jg+GtGda9gcGxKzefZ+UmMfQ9ZXwXurWTVilDwuL4cNJ6kpIzqFndh/nfDMBGQm3TaAQWLz3Czl1iwfN3xrZhyGDpIt7ZWSqWLjrMvt1iJYkGjcrz5fTeOBroYAQQH5vK2uUnObjrOhq1gNxMRrc+9RnxdmujbvHihCAInDpyh1VLjhGpdbn7VfBg7IcdadC0gsHvUhAEDuwNYPFPh1Bmq/Eq5ci0b/tT2YBJ6fnzBD77YgPR0cl4eDjw/bwh+PhIv0gePHqb734SCd/IwU0ZO0q61mxCUhrjv9pEWHg8PqWcWTprCC5O0kQtMjaZt6evJzYxjXrVfPlpUj+jL0kBD54zfr74kjS8S30+MqEdZkxSKqO+20BUQioN/X1Z/GFfo/VuX4RGI/D5yr0cu/kIJ1sr/vx0CGU98lfq6ElUHKMWbyYpPZMWVfz4eUyvQo3UXH7ylPdW7SBLpaZD9Yp8P7RboR7fFKjUGqbuOsyum/eQyWBGj/YMbpD7i8GrjuC4BMZv3c2j2HgUcjlTO7ZhWP1a//q1PDeUEM9/KdKzlUzbf5Q9d+4D0KO6P7O6dfxX9WuVQnxaOmPX7uBuRDSO1pYsH9GPWj4vv/Xa8buP+WTdXlQaDX3rV2dmv46FqiBsv3SbrzeLCs+k3q0Z0Sp/uVhKlZpxS3dw5eFTPJ3sWDtxKB556HCy+uAVft4hmi4Wju9Ni5q5t+W8fCeUj34Uw5dv92nK232M55jqeroDzJzQnY7NpNtlCoLA4tUn2bRXNJZ882kP2jaVNpY8eBzFJ5M3kpqWRfUq3sybMQB7e/1cREEQWLn6DOvWi4R2+LBmjHmjpcEH+5EDt1j0/QGyslS4ezgw7dt+VK1uvP5qWHAMK345ysUzYu1PaxsL+g1rQuuONShTzu2VWERSUzK5cfkJm9ec48HdcABc3OwY/V5bOvaoY9Rhn5mp5JcfD3J4v6g0NW5WkS++6qVXFF6H4OAYPv9yI/Hxafj4uPD9vCF4GOjotGv/DX5aegRBgP696vPBO9J1MlPSMvnw6808DI7Gw82epbOG4uUufczk1Eze+2Yjwc/jKO/jym9fDzHafetpVAJjvt1AUmombepXZO74nrn+1jOylIz9fjP3wqLx83Rm9aQhOBg5xz/x487TrDl2DXOFGb9P6E/dCvmr8RuZmMLIXzYRmZhCrTJeLH9vADZ5KLOWG66HPOedVTvIyFbS2r8cC0f0xMJEcl1YyFap+GzrAY7ce4RCLmdevy50r/nvrmMJcDToMV/sOUhqVjYedrb83L8H9Xyk01BeN5QQz38xBEFg7dUA5h49jUqjoYqHO0sG/vvzQpIzMnl33U5uPI3AxsKcX4f1pnE5w32UiwqHAh8wccN+NILAoEY1mda7faGSz+VHL/PzgXMAzBvelW71pAlZbkhOz2T0j5sIjoqnio8HKz82PU/sxTJLNpbmrPxiMJV9cldNd54KZM4qkTh/804XujYzXGZJEAQWrj3J5oPXMVeYsfDL/tSrJv19CoLA/N8Os+doIAqFnLlf9qVJXWkyfC8ogs+nbyElNZOK5TxY8O1AyVaKABs3XeT3P04C0KtnXca/L+1iBwh+HM03U7fx/Fk8CoWcdyd0oHf/BrkSyFvXQli+6AgP7oXn/M/d04EGTStSv2kFatYpm6f8y4JApVLz6H4EN64Ec+X8I+4FPkWjFh/f1jYWDBrVnH7DmmAloRS/iGdhcXw7bTtPHkcjl8t44+3WDB7ezODv4FbgU6Z/vY3klEwqlPdg3tzBkt+JIAhs2HqZZavFF5K+Pery4bvSRpWMzGw+nbmVwKBwXJxsWPLtUHy9pdXBrGwVH8/dRsD9Z7g52/HHzKF4uhpeD14sm1S1nCfLvhyUaw92jUbg82V7ORGgVSsnD8XX3cnoPi9iy9mb/I+9sw5v6z7b8C2ZmZkZEydx7MRhhqZJmzLDoNvK3Tfo1nVbu3Ydd4WV221dmQINM4MpiZmZGWSxdL4/jizbiUF2HHCq+7p0HZFlWT465/k9L734uZhW8tL9a1k7e2Lf+265kgf+9QVlTe2Eernx4aO3j6tn6Fjk1DTygw++oU+lZl5kMK9PcrcPU1CoNTz++TaOllVhZWHBK7etY1ns1J7mp9PreePYaV47Kva8nR0UwCs3rcPrGuk9agpm4XkNkFFTx+PfbKe9T46DtRU/W7qQO5OnT+n2C30qNY98upVTlbVYWUj5+cqF3Dv38qcTbDtbxNNf7EQQYGViJC/esnrSmswLgsBLmw7x6fGzWEglPHPTMm5Nm1j4qK6ti3v/LlbGTg/z49Uf3YCriTPoNVodj7y6icziWtyc7HjtsY3Eh4zdL7a/zZKFhZRnv7dq1B6fOr2eZ1/dxsH0UuxsrHjh8euZN3OEIh6dnudf2c7+48VYWVrw8x+tHDHsXl7Zws9+8yUdXX14ejjy21+sH3a8JsCWb7N59bU9CAKEBHvw5OOrSUoafuZ2X5+Kv7+0jaOHxIhCaLgXN98+h2UrE0Zto6TXCxzZl8/ebefIya5CrdIOeTwwxIPEGcEkzgxh2oxgfPxdJ2WfVirUFObWkXe2hrwzNRTm1Q3p4QkQFOrJ3IXR3Hx32pipAOWlzXz9+WkO7stHq9Xj6ubAr39/IzOTQ4d9vkym5L0PDvPttjMIAsTH+fPSi7cN60LL5Sr+/q897DtUCMA9t83lB/cN70K3tPfyqz9vpri8GSdHW1577nYihxkiAKIr+quXt5JVUIu9nTVv/+4OIoNHXkS1dcl44u+bKK1tNbRNugvPEUL3/Wi0On73n93syhD3zbeeupmZkaa7lZ8fOcefvjqAIMAj6+bxwzXD57KORUNHD4+8v5mypna8nR3432N34G9CazRT2ZtXyq++3I1CrSElPJA3779x0ue7j0VFawdPfrmdkuY27Kws+dedG5gXMbn9SC83ZW3tPLNtL2fqxbZn96XM4JfLF1321IUrjVl4XiM09vTy1KYdZNeJbktykD8vrltJuMflL9CZLFQaLb/ctItd+Ybq2dgIXrxxFS52kzdX3RS+PVPIb77eg1anJ8bPi9fv3YC/2+TsY3q9wG+/2MOWDHGG/QNLknlq3ciFLaORW9XEw298Q69CRZiPO/96eKPJJ6OePiU/fvlrimpbsLex4m8/Xs/c+NEP8nq9wPPvi61nAB69bSH3rh3ZGVSqNfzy71tIz63GQirhZw+u4MblwwttrVbHc//czsGTYvj6jg2z+ck9i4YNCdc1dPKr576mpq4DqVTCg3fP5+5b5w773OMnSvjHy7vo7JIDsHplIj96aBmuw+RyCoLA5q8y+eCdg8YiIncPBzbcNJv1N87CeYzemyqlhpzsajJPlnE2o5KqYarXPb2dSJwRQmK/CB31FYeiVGqMYrOsqBGdTj/kcScXOxJnBDM7LZLZaZGjTl7q/3szTpXz9efpZBv6lgLMSgnj58+sx9PzwsEHgiBw4GABb7x1gE5DW6fVq6bx+KMrh61yLy1v5vd/2kpdQycWUgk//t4Sbts4fBV4fkkjv/7zZtq7+nB1tuMvv76J+GHGpoKY0/l/f/mGirp27G2t+Mv/3UhywvCLCoDqpk4e/9vXNLb14O5sz79+ccuYbZP6lGp+/ta3nCqswVIq5YXvr2HVbNNCvoIg8K/tJ3hvt1iNfdvCJH5168T6X+bVNPHoB1to75Xj5ezA2w/dRJTf5EyzEQSBtw6c5vV9YkurtMhgXrln/aROdDOFb3MK+d23+5GrNXg62vPq7dczK3hi6QhXAxqdjndPZvKvY6fR6HQ4WFvx7Oql10xP7vFiFp7XEHpB4JOsc/z94DH61BqsLSx4bNFcvjcnecquqARB4JP0c/xp9xE0Oh0Brs68fOu6y573eaa6gSc++pZ2mRwPR3v+eff1zAqdnAOhIAi8ve80/9olHuxXTo/kj3etnVAz5LLGNh55YxPNXTK8XBz41082mlxwJFOo+Nlb35JeVIulhZQ/PLiG1Smjn1j1eoHXvzjCR7uyALhj1SyevGPxiMJZq9Xxp/f2sv1IPgD33zCHH902/Kg9vV7g31+e4N9fiJ/L3Jlh/P6p64etYpYr1Pzzzb3s3i++bnJSCM/8bN2wvT57e5W89/4h46xwZydbHvrhUtasnj7s+5b1Ktm+9Qybv8qgrbUXABsbS1atnc5Nt88hMMi0xV1Pt4KCczWiK3m2hpKChgvE4sXg5eMsitiZwSTOCCY4zMukBYxapWX/3jy+/uw01VXipCKpVMLCJbHccsccYuOH38/r6jp45bU9ZGVXARAU5M6Tj69m5owLFyyCILB5+1neeO8Aao0OL08nfvfL9UyLH74jx+7DBfz5zd2oNToigj3506824jfCeMuSqhb+7y/f0NbVh6erA3//xU1Ehw5fPQ+QX9HIUy9vpqtXQZCPK6/8300EjjFhqKNHzmOvbaKwpgU7Gyv++qPrmZcQOurP9KPR6Xjxs/1sPiXumw+vS+OHq+dMSHTuzy3j6Y93otRoifbz5F/fv3HCk9DOR6HW8Juv9rArV1zs3TNvJj+/btFlrV5XarS8uOMgX2aL/WTnhAXxt5vX4uU0dcPQuQ1N/Hr7XopbxO/W4ogwnr9uOX7Ok/N/m4qYhec1SH13D7/dsZ+jFVUAxPl48dL1q4j3HflgfLWT19DMU19sp7azGysLKT9buZD7LnPovaGrh8c+3EpRYyuWFlJ+v3EFG5Mnb8W6PbuIZz/bg0anY3qwL69+7wY8nMbfTL+5s5dH3txEWWM7jrbWvPzQBlKiTMuRVWu0PPvv3ezNEk8+P7ttMXctH7vw6eNdWbzymZivt2pODL/9weoRq4gFQeD9r0/y/jeioFw1L5ZnfjTy8w+cKObF13aiUmsJCXDnT09vHDHHb9e+PF5+Yy9KlQY3V3t+87PrmT0zdNjn5hfU8/Iru6gwNIVPTAjkqSdWExY2vFDXaHQcOVjIV5+doqykGQCJBObOj+KWO+YyLSloXPujUqGmKK+evLPV5J+rpXdQU3lTkEqlhEf5kDgzmGkzQ/AZ5yz4rs4+vt2czdZvsugyuJX29tasXT+DG29JwXeE11OrtXz6+Sk++fQkGo0Oa2tL7r4rjdtvnTNsGkKvTMlfX93F4ePiPjUvNYKnn7pu2B6sOp2edz45xsebRWdwYUokzz5x3bBdCwBOnavimVe2IldqCA/04O+/uAlfz5GP/ydyKnn69W9RqrXEhvrwz59uxN159O9YXWsXj7y6idqWLlwd7XjtsRtNGjcLYhHSzz/YzrGCSqQSCb+5Yzk3zTO9524/giDw0dEz/HXrYQQB5seG8vd71+FgOzlOZFN3L499uJWChhYsLaT89obl3JwyfHrLpWJwaF0igYcXz+XhxXMue6/QyUKh0fDakZN8cDobvSDgamfLs6uWcn1CzFVRcHglMQvPaxRBENiSV8gf9x6mS6HEQiLh+2mzeXTB3Ck7VqxXqeI3W/ayu+DKhd7lag2//nIXe/PKAHhgYTI/XbNg0g6OmeV1PPmfb+mWKwlwd+aNH2wk3Gf86RI9ciVPvrOV7PJ6rCwteOHe1ayeZVpYUK8X+NsXh/js4FkAHlyTwqM3Du9KDmbXyUKef283Wp2elPhg/vzY+lGnIm07lMef3t+LTqdnVnwQf3pqw4jVx8UVzfzqT5tpae/FydGWP/zfemZPHz4VoKqmnef+vJWKqlYkErjntjQeuHv+sM6NTqfnm02Z/Pu/R1EqNVhYSLn15hTuvWf+sKFiEL9b585U8/Vnpzl1osx4f3SsHzffPodFS2MvmFN+NVFT3cY3n6ezd1cuarWYg+rl7czGW1O4bv2MYScV9ZOVXcUrr+6mrr4TgNnJYTzx+CoCRlgIFBY38vs/b6WpuRtLSyk/fnAJt9yQPOy+1CdX8dw/t3MiqwKA+26eyw/umD+ia7v1YC5/eX8vOr1AcnwQL42y/wBsO5bPix+IIxbnJIbw50fXYz+GcCuubeHRVzfR3iPH38OZfz1xEyE+prU96uiV8/jbW8irbsLWypI/P3gdi6eNvzBGp9fz582H+fT4WQBuS5vOrzYunTQnMqemkcc+2kpbrxw3Bzteuft6ksMub2/o80Prf715LWnhI6dKXO2crq7lN9v3Ud3ZBYhdZ36zcskVmcp3NWIWntc4bbI+/rDnEDsLRbchzN2NF9atmLJN588Pvfu7OvPPyxx61+sF3th/kjcPnAZgYXQof73zOpxsRx89aSpVrZ08/O4matu7cbKz4ZUHN5ASMf7/l0qj5ZkPd7LvbBkSCfz8piXctWTsXp0gfs7/3pXB65vFqvv1afH85t4VY6ZsnM6r5peviw5UdLAX/3jqRrxHCQWezqni1698i1yhJixAdKz8RmiT097Zx6//spn8kkYspBIe/94yblozY1gRo1JpeP3dA2zdeQ6A6QmBPPuL9XgPk6cIYrPz19/YxzGDK+fj48zjj64ibe7IM73BIOK+SGfvzqEibsPGZGYkhxAR5Tti9fzlpKW5h8K8Ovbtzr1ALN9yxxwWLhldLHd09vHmW/vZf0DM5/Vwd+SRh5ezeFHssJ+/IAh8sTmTt/99GJ1Oj5+PC797egNx0SP0/Wzq4uk/baKyth1ra0uefng1qxYOP2NcEATe/eoE/94kVgWvWRDHrx9aPWIPTUEQ+M+2dN78WtyX16bF8Zvvrxqz52ZGcS0/fWMrfUo1UYGevP74RrxMbFVW39bNT974hprWLlzsbXn1xzeQFDb+VjlylZpffLSDwwVizu1Pr1/IA0uGF+4T4dszhfz2m72otTqifT15/b4NBLhdvq4o54fW54YF8dcpHFrvVar464GjfHYmFwAfJ0eeW7OMZdFTuxJ/sjELz+8Ie4vLeG7XAVpkYkjtruQkfrZ0Po42kyOWLjf5Dc08eYVD77tzS/j1l7tRarSEe7nz+n0bCPGcWBPo8+mQyXn8g62cq27E0kLK87evYn3y8Cfi0dDp9fzlq0N8flQUYA+smM3j6xeYXLy0+XgeL360D51eYH5iKH/+4boxXaKiqmae/McmOnrkeLs58o+nNhI9SnVxaXULP/3LJto6Zbg52/HCE+uZFTd8aoBKreUvb+1h92FRAN2wKoknvrd0xDD9/sOF/O213cgValyc7fjVU9eRljrySeDEyVJefX0vLS09ACyYH82jD68YsQ9lP8OFrQGsrS2JjvUjPjGAuIQA4hMDcb/EzeU1Gh1lJU0U5NVRkFdPYX4drS29xsclEkhbEM3Nt88ZMz1ArxfYtv0s775/iL4+FRIJ3HhDMg8+sBDHEdzF7h4FL728g5Pp5QAsnh/DL55YM+KEoezcGp79+7d09yrwcHPgT0/fSFzk8AJVrlTzwlu7OJguRj0euHEuD906b8S/QaPV8af/7uPbo2J+5T1rknn0trFHLO7OKOa3/9mNRqsjOTqQfzy8AadR3PvBFNW28Mibm2jvlePn7swbP9lImO/4oxatPTIeeX8LhXUt2Fha8Me71rAqKXrcrzMcOr2eV/Yc5/3DmQAsi4/gT7etuaxFROeH1h9ZPJefTOHQ+oGScn636wDNveIEsjtmTuPnyxZOmiFxLWEWnt8hepRK/rL/KF+cFVeXfs5O/H7NMpZGDd/W5mqnV6ni2a17r2jVe0F9M49+uJXmHhnOdja8fNf1zI2cnBCRUqPlmU93seec+Pc9vGouP141d9ziWhAE3t+TwevbRMfn+pQ4fnf3SpMLzo7kVPD0O9tRarTEBXvz6mM3jjnar6G1m6de3kRlQwf2tla8+JN1zE8aeT9rbu/hF3/fQklVCxYWUp64Zwm3rBrezRQEgU+3ZPDmR0cQBIgK8+b3T64jJNBj2Neua+jkuT9tpaRczMu85YZkvnfPAhzshz8hKBRq/vfRcb78OgOdTo+trRU3bpjF+utn4jdGHmV/oc7xI8UU5NUPm7fp6+cqCtHEAOITAgmP9L6o0Hx7Wy8F+fUU5NZRkF9PaXEjGrVuyHOkFhIiInyYPjOY62+YRWDw8J+V8e9Qazl6rIQvv0qnpLQJgKgoH376xBpiRhmpeSqzgr+9tpvWtl6srSx45IfLuOG64f+PWq2O9z8/wUebTiMIEBfpy0u/vBHPYQrCAOqau/jlP7ZQUduGpYWUn39vBRuWjpwv2dun5Jevf0tmYS1SiYSf3bOUW5bPGPXvFgSBj/Zl8/JXRwBYPiuSF7631uT+laeKqvnpe98iV2mI9vfk9Yc3jmugQz8ljW088t5mmrp6cXe049Xv3UBSyMif+3joU6n5xec7OVQopjQ8tCSVx1aO3Jv1UvBtThG/+3bfNRFab++T88KeQ2wvKAYgxM2VF9atYE7I5e89PVUwC8/vICeravjN9n3UdnUDsC4+ht+sWoLHFMw/Ga7q/Z+3rWNawOULvbf2yHj8o2/JqW3CQirhV9cv5c60pEl5bb1e4JUdx/jgoOhMbJgdx+9vXWnyaL7BbDmVz/OfijlxabEh/O3715tcnJBb2cgTr2+hS6YgwNOZ1x8fO9ftghP/vcu4ZdnIn4tSpeGP7+5h7wnDNK4lifzsgeXYjNA382RWBS+8tpPuXgU21pY89uBSblg5/Lg5tUbLWx8c5uutYvW9u6sDDz2wiNXLE0c84VZUtvDPV/aQl18HiE7hnNQINqyfRcrssFGn/YC4b9bXdpCfV0dhXj0F+fVUVbRw/lHU1taK6Fg/vHyckYyjoZJKpaGkqJHmpu4LHnNxtTc4rKK4jY7zGzFndTBNzd1s336WHTvPGVtO2dtb870HF3HD+lkj/s119R28/u4BTmaIYiYowI3f/XIDURHD94Otb+ri9//cRqFB1F63LJH/+8FybEZo3n46p4pnX9tOb58SD1cH/vjkeqZHj9xVor61m5+OY+ED4nft718e5tMD4tjUO5fN4Ke3LjbZgdueUcjvPhbbrqVEBfGPH6432SUdzIniav7vw23IlGpCvdx444c3EuThOu7XGY7aji4e/XArZc3tWFta8IebV3H9jIk1sJ8ISo2WF3ce5MusqV+1LggCm3IL+NO+I3QplEglEr43ZxaPLUq7puesTwZm4fkdZbiKu6dXLGLjtPgpWXGX19DMk19so66zR2w4v2oR984Z3mm5FKg0Wn6/aR9bz4hNsW+fM51frV8yaW2svjyZw4vfHECnF0iNDOIf91+Pi/34nd2j+ZX8/INtKNVa4oK8ee3HN+I5hnvZT01zJ4++uom6tm5cHWz556M3Mj18dBfm/FDnXauTeez2hSOezAVB4NMdWfzrkyPoBYGESD9eemoDXm7Du0ZtnTJefG0nGeeqAbEK+pcPr8J1hErl05kVvPr2fuoaxOKYuGg/nvjxCuJGcPH0eoGTp0rZsvUMmVkDvS39fF1Yf/1M1q6ZjssY/TwH0ydTUlTYIIbA80SHsk+mMvnnh0MqlRAa5kX8tEDiEwKISwwkINDN5H1frxfIyqpky7fZnDpdjl4vHuY9PBxZv24G118/c8SJUHK5iv99foovNmeg1eqxsJByy4ZkHrhrHvbDOMqCILDrUD7/eG8/CqUGRwcbfvHjVSybN3zhmyAIfLI9kzc+PWrS/gCQV97Iz17ZMpDq8eSNRIeM3tFDpdHy7Ae72JctRheeumUR96yYZdJnKAgC/92fxT+3HAVg1cxoXrh35C4No/HN6Tz+8NV+tHo9yeEBvPLghgl9z4cjo6KOJz/+li65Ei8nB169dwPTgy7fAr2yrZMnv9hGsSG0/pNFc3hkydwpGVqv6ezitzv3c6KyBhA7x7y4biWJfmMP3jBjFp7fefIam3lm+14Km8WWMvPCgnl+7XKC3Vyv7BubAD0KJb/Zupc9BWLxxMq4SF68YSXOlyn0LggCHxzJ5OXdxxAESAkP5J93XW/yBKGxOFZUxf99uA25SkOYtztv/OBGAj3GXwiQW9XE429vplOmIMDDmTcevokQb9NyU9t7+nji9S0UVDdja2XJSz+8jsVJoyfOn1/csXhWBM//6DrsRhlNeL7D9dKTG5gWPXxxhl4v8MW2LN76+AharR4PNweeffy6EaveNRodX23N5L+fnkBhaAy/ZkUiD92/aNi+n/3U1XXw7bYz7Nydg8wgFq2sLFiyOJYb1s8iLs5/3AsdvV6gtrqNwvx6enuU4/pZqYWEsAhvYuP9hxV5Y9Hdo2D37hy2bjtDQ0OX8f5ZM0PYsH4W89IiR0wB0OsF9h4q4O1/H6K9Q8xpTU0O49EfLiMkaPgwfm+fkr+9vZf9x8WQ5Iz4QH7z+HUjzlxXqjS89O4e9gxywH/+4PJRBd2BjBJ+985OVBod0cFe/P3JG/FxH71fYnefkp++sZUzZfViPvUDq1mTapoLqNcL/G3TYT45JLqkdy+Zyf9tHLmP7Wiv8/quE7y7X2wjtW5WLM/fvhJry8npQPJFeg4vbjmIVq8nIcCH1+7dgM8EUgAmyvbcYp7duhe5WoOHgz1/vXnNlJxCpNXr+W/6GV45fAKlVouNpQWPLUzjwTmzpmyv7CuBWXiaQaPT8Z/0bF49chKVVoetpSVPLE7j/tRZWE6x1aggCHx0+ix/2XMEjU5PkJsLL9+2jkT/y7cSPVhYzi8+24lcrSHI3YU37r+RcO/JmSBV3NDKw+9tpqVbhrujPa9//wamBY/ftahp7eLhN76hrq0bN0c7Xv3RDUwLNS2HTK5U8/S7OziWJ/YmfPrOpdyyeOzUgr2ni3nu3V2otTriwnz4+xM3jjqi8MKcvuVsWDrySNGSimae++d2qus7kEjgzg0p/PDOBSNWlbd3yHjnP0fYtV8M+9nbWXP/nfO4eUPyqJXoSqWGg4cK2bI125j/CBAZ6cMN62exbGmcSWHtK0VRcSNbtmZz8FChsRLfwcGG1aumseH6mQSPkf9ZVNrIq2/tJ79InJIW4OfKow8tIy0lYkThfa6gjudf2U5zWy8WUgnfv2M+d9+YOmLovrG1h6dfHsj5ffLeJdy8cuQIhiAIfLQzk9e/PCr2uUwK44Ufr8NhjP9DY0cPj766icrGDhxtrfn7wxtIiTEtN0+l0fKb/+1i7xnRJf3pjYu4b3myST97/us8+9kedp4VBfmPVs7hkdVpkxKt0er0/HXHET46IQrjtdNjeOGWVZetpZ5Ko+WlXYf5LDMHgNTQQP52y1q8nS6f6J0sCppa+M32veQ1iRPI5oQE8ofrVhDqPjkFpd8lzMLTjJGqjk6e3bGP09ViTluCrze/XL6IuaFTL0k6t76JJ7/YTn1XD1YWFjyxLI370y7fqrS0qY1HP9xCXWcPTrY2vHDLKlYkjN6ax1Sau2U8+t5mihpasbWy5Le3ruD6WcO3tRmN9p4+Hnt7CwU1onv53D2rTO71qdXp+ePH+9l8XBRt965M5tEb54+Ze3qutJ6fvbKFbpkSXw8n/vjw9SRGjCx45Uo1f3hzF4cyxJP7hqXTePzuxSMWBilVGl77zyG27BGr+KPDvPnZj1aOOGoRoKCogVfe3kdRiSgigwLcePDuBSxZEDNmHudIIm7xolhiY/2ICPcmLNQLW9srk/MlCAJNTd2UV7RQVt7MqVPlQ8VyhDc3bEg2SSzX1Xfwv89PsftAHoIAdrZW3Ht7GrdunD2iCylXqPnPlyf57NtM9HqBAF9XfvfkulH/H8eyy3nx7d109SpwdbLjxSdH7nIAYpP2v310wJjOcevyJJ66a+w+l+fKG/j529to6+7D29WR1x7fSFSAaaMnW7tlPP2fHWQZXNI/3LOatbPHnyvZ1NnLLz7awZmqBiylUn536wpuTJ2coRStvX088+VujpeKaSiPrZzHj5amXrb0o/yGZp7ZspeiJrGf7o8XiqH1yzkJaTLokCt4+0Q6H6afQScIONnY8PSKRdySlDAl09KuBszC08wQBEHgq3P5/Hn/EXqUYjhxflgwP10yn2n+l3dM5cXSrVDyzOY97CsS27pEeXvwu+uXMTvk8vQw7ZDJeeLjb8muEp2h62fE8qv1S3GdhJytPqWan3+0g6OFYt7h6qRonr1l+bjzweQqNT9/fzvHC6sAWJ8az9O3LjWp6EgQBN7Zdoq3t4n9FOOCvXnh+2vHbB1T29zJUy9vpqapE6lEwv3Xp/KDG+aO2ofxv1tO886XxxEE8HZ35JffX8m8mSMXixw5Xcqf39xDd69YVX7d0gR+dPciPEbIVdTrBXbtz+Od/xw2FtUE+rtx921zWbU0fsyq85HC1iDmYAYFuhMR4UNEuDeREd5ERPqMmDc5UdRqLVXVbZSXt1Be3kxZeQvlFS309Q3NIR1vekBFVSsffXGKg0eLjPmfK5fG8+MHF+PpMXwYW68X2H2kgLc+OkK7ob3UdUsTePL7y0ecQtTZI+flDw8ai8uiQ735809vGHUSUU5pA79/dyd1Ld1IJPDUnUu4feXobdU0Oh3vbT/N+zvS0QsCEf4evPbYRnzHCMn3c+BcGc9/upeuPiUOtta8/IP1pMaMvyJ7e3YRL359gF6lCidbG/5x//XMjb74ym5BENhxrpgXtx6kW6EUU2JuW8OqxKiLfm1T6FEoeeXAST7NOIdeEHCzt+OvN69lQeTUCq3LVGr+k57N+6ey6FOrAVgTG8VvVi2Zko7t1YRZeJoZllZZH/86dpovzuSi1YvzpFfHRvLk4vlEeE5O2PhyIAgC35zJ5697j9IlF3PobpwRz89XLsTD8dJX8au1Wt7cf5r3DmegFwQ8nez5/cYVLI27+IbCWp2e9w6k8/ae02j1erxdHHnhjlWkRY/vAK/R6Xh75yne35OOIECgpwsv3rfG5IbX+7NLeeGjfXT3iSe5n966iJsXDV9d3k9Pn5K/fXSAXScNIiPYi+ceWktE4MiOU1Z+DS+9u4f6FrGKe/X8OJ68d8mIhURtnTLe+ugIuw6JPT/t7ax54Ja53Lpu5FC6rE/FN99m8eXmTHp6xf3F19uZdauns2xhLIEBo+/7er1A9pkqMrMqRQFY0UKXQciej7u7AxHh3kRE+BAZ4Y27+/iEqFqto6qqlfKKFsrLW6iuaR929rulpZTQEE8iInyIjfFj8aJYXF1H3/eVSg0nM8rZcyCfE4Z+nABpqRHcf8e8EYuxAApKG/nn+wcoKG0EIMDXlcceWMqClOH3eUEQ2HO8iJc/PEC3TKwOvnNdMj+4ZR621sM7xWqNlnc3n+R/OzLRCwLe7o789vurSU0Yfd+vae7kNx/sIq9KdH3XzY3jF3csNan6XKHS8NdvDvHNCdHljw305o/3ryHcd/TUhPPplit58esDxtD69GBf/njXGkK8Lj5k2y6T8/zm/ezLF/Pc4/29+eOtq4nyNc3JvRgEQWBbbhF/3n2ENpm4z6+bFsMvVy+aUkJNpdXyaXYObx5Pp1MuLlxjvb34v6XzWRwZdoXf3bWBWXiaGZWazi5eO3qKrbmFCIBUImHjtHgeWzQXf5ep8zl3yhX8Y98xYxsPZ1sbnlo+n9tmT7ssVZU5tU0889VuKlo6ANgwM46n1y+ZlJ6jeTVN/OqTXVS1ipXa9yycyRPrFow7jyu7rI5n/rebxo4eLKQSfrh6Dj9YPcek0FhLp4zf/Xc3pwvFKs+F08L43X2rxpyDvS+9hD9/uI9umRIrSwt+cMNc7lk7e0T3U6nS8M5Xx/l8h6Ebg5MdP7x1PhuWThvxfeaVNPDKBweMrXv8vJ25Y/1s1i2fhu0IBU5yhZqtO87y+TcZdHQNNISPjvBh2eJYli6Mxdd77MIuQRDo6OijrLyZ8nIx3F1e0UJdXccFbZUmAycnWyIivIk0iNmICB+CgzxMmp6k1mhJz6zkwNEiTpwuQ6EUC68kElg0L4Z7bp9L9AjtkUD8nD/elM7RdFH02Nlacf8tc7nt+uQRQ/HltW289tEhTueK4eDIYC9+/dAq4sJHjq7kVzTy4r/3UlbbBsDaeXH87O6lo47LFASBzcfz+NsXh1GoNDjZ2/Dru5azOsW01JL8miZ+/d9dVLd0IpHA/ctn88i6eeNua3a6tIZnPt1Nc7cMC6mEH62Yww9XmPYdG4vduSX8YcsBOvsUWEql/HjZHH6wJOWypBeVtbTz/PYDpFeJaVphnm78dt2yKdWbU6vXszmngNeOnqKxRxy4EOruyhOL57E2LhqpOaw+aZiFpxmTKGlp4+XDJ9hfIrofVhYW3J08nR/PS51S82fP1jby3Lb9FDaJVfyJ/j787vpll6Xvp0qj5fV9J/nP0SzRpXF24LmNK1kUe/GraLlKwz+2HeHzE2ISf6SvB3+6ey0x/iNPDBqOXoWKl744wI5M0YmcHubHH+9bQ6Cn65g/q9cLfHrgDK9tOoZaq8PV0Y5f3rGUVbOjR3U/27pkvPjvvRw/J6YNhPl78KsHVjBjlD6NBeVN/PGd3ZQbxEdYgAeP3rWItBlhw/4uvV5g9+F83vroKO0GIenqbMct62Zx0+oZODsN33lApdJw4EgRB44UkXW2Cp1+4BCYEOvPskWxLFkQM2LIeSQUCjWVla2G3EsxNH5+SHwspFIpQUHuBtdUFJteXk7jyjvTanVkna3mwJEijp0qRTboPfj6uLB8USyrlyeOWKkuCAKnz1bx0abTnDX0OwVYsySeH9+9aMRm8O1dfbz75XG+PZSHXhCwsrTgezfN5Z7rU0ZMa5DJVbzx1TG+PngOQQBXJzt+df8Kls4ePYTc1NHLHz/ez7E8cf+aHRPE8w+sNim0rtPr+c++TN7cfhKtXo+PqyMv3LuGlOjx5b2rNFpe2XGc/x3JBiDY05WX7lrD9EloCt8hk/PC1gPszhXzoGP8vHjxllXE+Y/eQmoy6FOpefPwaf5zMhutXo+tlSU/WTSHB+fNmrSK/EuNIAjsKS7j5UMnqGgXjQEfJ0ceXTiXm6bHm6vVLwFm4WlmXJytb+TvB48ZC5AcrK14IHUW35+bPGXGb2p1ej7NOMcrB04gU6mRSOD25Ok8uXz+pORfjsXZ6gae+WoPVW2iQ3nT7AR+sW7xpIxWO1JYyW8/30N7rxxLCymPrZnH/UuSx+3q7sgs4o+f70emVONga80vb1nC+lTTeryW1rfxmw92UlonisLFSeH8+q7leLmOHG4TBIFdJwv556eH6TTkZW5YlMijty3E1XF4UajV6ti0P4f3vz5Bt0wMi6ckBvP4PUuIHGFEp0qlYfvBPD7dkkmjIWRvZ2vFhhXTuX3DbLxHEZBd3XKOnChh/+FCzuXVGh1LiQSSEoNYtiiWxfNjcB1HX88rgU6n51xeLQeOFHHkRAndg6YreXk4smRhLMsXxREb7Tvi/1ur03PwRDEfb06nrEpcxFlYSFm9KI67bkwldIQpUkqVhs92ZvG/renIDY7q0tQoHr5zEYE+rsP+jCAI7Esv4R+fHKK9W1w0rJ0XxxN3LB7VUdfrBb45lssrXx+lT6nGytKCR26Yxz0rkk1qd9TQ0cOz/9tFVlk9ACtnRvHsHStwHucxorihlac/3klZUzsAt6ZN42frF2M/SjsxU9mbV8rzm/fT0afAQirhoSWpPLR0DtYXMQnLFARBYE9hGS/tPERTjzgicllMOL9eu4TAyzjr/WIQBIETlTX8/dBx8hrFqWaudrb8aF4qdycnXbbK/+8iZuFpZtwIgsDxyhr+cfCYsbWEq50tP54vfmFtpshKt7W3j7/uOcLWHNHdc7O342erFrIxKf6Sj49TarS8uuc4Hx7PRhDA18WR529axfxx5mcOR4dMzu+/2MfBfNGdTg4P4I93rsHffXzfi4aOHn7z4S6yy8UT79LpETxz+3KTGs5rtDo+2JnO+zvT0er0ONrZ8NNbFnHD/NErQbtlCl7/4ihbjogpEa5Odjxxx2Kumxc34s/19in5z+bTfLn7DBqtDokErl+cyEO3zsdzhEbjwwknS0spqxaOLpz6aWvv5dDxEg4cLjS2FQKwkEqYNSOERfOiiQr3ISTYY8RimsuFXi/Q1NxNZXUrmWerOXSsmI5B8+TdXO1ZPD+GZYtimRYfOOq+r1Rp2H4gj8+2Xijcb1ufjM8IhUB6vcDu44W89flRWjpEoRIf4cvjdy8mKXbkYr/6li7+8r8DnMytAiDY141f3reclPjRQ7i1rV384cO9ZJaIC+Tp4X789r6VhPuNnY8pCAKbTubx901H6FOqsbex4ulbl5q88OpHrxf48HAWr+48gUanw93RnuduW8GShIvP7+7qU/DitwfZcU7ME43y8eDFW1eTEHDp28ZVt3fxwo6DHC2rAiDA1ZnfXLeUpTFTZ/TyufpG/n7wOKeqawGwt7LiwTlTy0CZypiFp5kJIwgCu4tKefnQCSo7RPfOtz9EkZQwZXqAplfV8fy2/ZS1imGWWcH+/G7dMmJ8xxemnghZVfU88+VuajvEk/gtKYn8/LpFOF6k+ykIApvS8/nT5kMo1Bocba359calXJ88soAbDp1ez7/3ZvLWzpNodXqc7W345S1LuW62ae2byurbeO7DPeRXiY7CnLhgfnPPCgI8R3dFzpbU86f/7qOiXnSJkuOCePq+5YT4jVzcU9/cxZufH2X/qRIA7GysuHt9CnddNxu7EdoZ9YeKP96Uzpn8WuP9C1MiuXtjKokxYxdYNbV0c/BoEQePFFFc1nzB434+LoSFeBIW6kVYsAdhIV4EB7lPaLLNaAiCQFu7jMrqNiqrW6mobqOyuo3qmnaUKs2Q5zo52rJofjTLFsYyY3rwmDmGPTIl3+w6w1fbs+kyOKSmpCoAZBfW8tpHhymqFD8bX08nfnLHQlbMjR1R5Gq0Oj7elcX7W06i0uiwsrTggetTue+6lBFHqIK4v366/wxvbDmBUqPF1tqSR2+cz+1LZ5jk+jd29PD8p/s4WSTmnCaF+fHCvWsI8nId82eHvE5nD898upuMclH4LokP5/e3rcTD6eLd8AMF5fx+0z7aZXKkEgk/WJzCT5bPueShbaVGyztH03nveCZqrQ4rCwt+uGA2Dy1MnTLuYGlrGy8fOsG+QSljd82azo/np07JkdFTFbPwNHPRaPV6NuUU8NqRkzT1im5GmIcbTy6ex+rYqCmRlK3R6fjw1Bn+degUcrUGC6mEe1Jn8NjStIsWgWMhV2t4ZfcxPjpxFhDdz9/euJzFsRfvINS2dfGrT3ZxrlqsMF6VFMVvb1kx7rZLJfWt/O7jPRTWig73kmkRPHP7MrxMmH6i1en5ZH82b249gUqjw87GisdunM9tS2aM6q5ptDo+2ZXFe1tPoVJrsbK04P51Kdy/LnVU8ZFTUs9rHx0mr0z8mz3dHPnx7QtYu2B0Jzu/pJGPN6dzNL3UGEZPigvkno2pzJkZZpILXtfQycEjRZzJqaGypm2IszgYC6mEAH83wkK8CAsRxajbGJXm56PW6Kit66CyutUgNtuG5GgOxtrKguAgD6IifFgyP5rkGaEmFRy1tPfy+beZbN2bYyw28vN25o4NKaxbljhicRZATWMH//r0KEcyxWIjeztr7r8hldvWzBqxWh0mtugAKG9o47kP95JXKRaRpcQE8ey9Kwg0QTQKgsA3J/L4x2bR5bSxsuDR6+dz15KZ40pTEQSB7dlF/PGbg/QqVdhZW/GLGxZz85zEi+752C6T89K3B9mZIy6swr3defGW1Zdl7OWhkgpe2HGQus4eAOZHhPDsuqWEekyN5ul1Xd28euQkW84rkn100VwCplCR7LWCWXiamTRUWi0fZ53jrePpdCnEnLtEX2+eWjKfBeEhU6LZblN3Ly/tOszuAjFR38vJgV+uXsS6xJhL/v7TK2r57dd7je7nuqRYnr5+Me4X2fZJq9Pz/oEM3tpzSmy75OzAH+5YzbyY8bdd+s++TN7eeWpC7mdNcyfPfbiXM4acuRkR/vz2vpWEjtH3s761m7/+bz8ncqoACPJx5en7V4wabhUEgf2ninnjs6M0toony+hQbx6/ezHJCaOHaavr2vlkSwa7jxSg1YrtiTzcHFiQEsmClAiSpwWb7FZ2dcupqhFFYUV1G1XVbVRUtY4oEC8WC6mEwAB30WEN8SQ8xIvQEA/8/dxMrpyuaejgaHoZx9LLyCtpMIrwiBAv7t6YyrJ5MaO+Vnevgg++OcnX+86h0+mxkEq4Ydl0vn/zPNxHyX8dLs3iyTsWs3aUNAsw7Je7Mnh3+2kxrcPWmqduXcSN800Te40dPTz36V5OFYkdGZLC/Hj+ntUmj5Htp6tPwYvfHGDXWVEYTg/25aW71xJsQmHeaAiCwLazRbz07SG6FWK7qQcWJvPoijRsLrHTWN/Vwx93HmK/oReyj7Mjv1qzmNXxUVPieN4q6+Ot4+l8lp2DxtAWcFVMJE8unkek1/jaYJmZPNo6O/Fyd792hOeGd97jztQU1sXG4GTO1bgiyFQqPjidzb9PZ9GnFl2S2UEBPLVkHinBl6d5+8VyrKyaP+w4QHV7FwBzw4J4dt1SIi7xwUqh1vD6vpN8eExsF+TmYMev1y9l7fTRK8NNIb+2iac/Hmi7dPPcRH62fvxh/dKGNn770e5B7mc4z9y+3CT3U68X+OpIDq9+cxS5SoO1pQU/Wj+Xe1fOHlXMCILA/oxS/vHJQdoMVelr0+J44s7RC0xUai1f7j7Dfzafok8hNoGemxTKndfNJiUxeNTPtLW9l8+3ZfHtvhz65Grj/Xa2VsydGcbCOVGkzQobtY3PSH9Le4fM6FL2X/rk4xOjFhZS/H1djSIzLNSLoAC3cYfw9XqBwrImjqaXciyjjKq6jiGPz0gI5O4bU5k7c/iOAf20dcr4eu85vt5zhl7D3zJvRhiP3rWYsFFyZgVBYOeJQl75bKCw7AZDYZnLCIVl/RRUN/Pch3uMhWwLp4Xx67uX4+M2dsW6IAh8fSKXlzcfHeRyLuCuJaaF5QdzIK+c57/aR3uvXGyTtHIuP1yeetFtkhq7enl+836OFIsV+dG+nvzh5pUkBl5al1Ot1fHvE1m8eeQ0So0WS6mU+9Nm8vDiuTjYXL3jYPvpUih571Qm/8s4g0IjThRLCw3ip0sWkHQZOpiYuRBBEDjT0MiXuXlsz8kh95c/v3aEZ9jzLyK1tcXW0pK1MdHcMi2B1MDAKbE6u9Zo75Pz9okMPsk6h1qnA2BheChPLZlHot/lm50+UdRaLe8fz+KtI6dRaXVYWUh5cF4yP1k0B7tRQoWTQW5tE7/9Zi8lTeIJdUlcOM/esAxfl/G17TkfhVrDP7cf45NjZwHwc3PiudtWTqjp/MW4nw3tPbz40T5OFoi5dLHB3vz+vlVEB42eVyuTq3jz6+N8deAsggBO9jY8uH4Oty6fMWr4vbNHzvtfn2Tz/nPGlkjhgR7cvnYWq+bHjRr6VWu0nMmr5WhGGccyymkzFMeAKP5mJgSxMFV0Q0cqrrnaUGu0ZOfVis5mRplxuhCIf9OsxP6/KXLUSn+A4spmPtuZzb6TRWgNDewjg7147O7FpE4bfb/KLWvg9S+PcqZYdMHDAzx4+v7RW2kBKNVa3tl2kv/tzUKnF3B1sOXndyxlTYppkYmGjh6e+2Qvp4tFl3NGuD/P3b1q3C5nt1zJnzYfZFuWWKAY5u3Oi3euZlrwxYkbvV7gi/Qc/r7zKHK1BisLC36yfA7fWzT7krf3OVVRw/PbD1LRJi5AUkID+e26pUR5X/om9BeLTKXmw4wzvHcqE5lKXCwm+fvy1JL5zAubOj1FryVaZX1syi/gq7w8Kgy1IHqlksrfPnPtCM9X9x9kW2UV5R0Dq/ZgVxdumZbIxoR4/Jwu7sRtZvw09fTyxrHTfHUuf8gUpCcWTY1wR11nNy/sOMihEtF18Hd15pm1S1gWE35JFzRqrY73Dmfw9kFDCNHGmv+7biG3zJ520VX3GWW1PPv5Huo7xDD0rWnT+L/rF5k0KnMwZQb3s8Dgfi5ODOeZO5bjbYL7KQgC204V8LcvDtMrV2EplXLvqmR+cN0c7MZoNVNQ0cRL/91HcbX4e33cnXhoYxpr58WP6jLVNnXy5e4zbDuUh8JQcOPqZMeNy5O4aWUSXiNUwfej1wsUVzRx5HQZR9PLqKprH/J4TIQPC1MimTsrjNBAj1FzIC8ner1AS3svuUX1HE0v49SZSuSKARfX3s6atFlhLDC897FcXK1Oz7Gscj7flcXZonrj/dOj/bl9bTKLUyJHdQ0rG9p58+vjHMoS8z9trCz4/g1p3L0mecym7OlFNbz0yQGqm8WT2KrZ0fzi9qVjDiuAC11OWytLHl0/nzsXj9/lPJRfznNf7qOtVyzyuX9JMo+svvjwd1VrJ7/9Zi9ZVYaUlGA/nr95JRHel/ZY2dIr4y+7j7ItVxTRno72/HL1Iq6fZtpi8kqi1IjTht46MTBtKMbbk6cWz2Np1KU9Tpu5ELVOx+GKSr7Ky+dQeQU6g3S0s7JkbXQ0a0JDWJ4Qf+0Iz+7ubpycnDjT0MhXeXlsLyymTzNQ0TnL3581MVGsiY7C35wLelmp6ezi1SMn+TaviP4daXFEGA+kzmRe2Ohhz6uBA0XlvLDjIA3d4lSL5GB/Hl2axtywoEv63sua23j2673k1IpFE/H+3jyxej7zoy4ub1auUvPy9mN8dvwcAJ5O9vxkdRo3pSaOK0So1en57/5M3tp5Co1Wh72NFfcvn829y2Zhb0JYrq27jz99eoADZ0QR4uFsz/2rZnPz4umjOss6vZ6dxwt5a9NxY4seP09n7lw1i/ULE3EYpY1Rb5+Sbw/l8eXuMzS1ieJbKpEwZ3oo1y1OYOGsiFEd1H5qGzo5miGK0Lzi+gumEXm5OxLo50agn6th60aQnysBPq7YTLIo1esF2jpl1DV0UtvYSV1jJ3VNXdQ1dlLf3I1arR3yfA83BxamRLIgNZJZiUEmhejLalrZcTSfPceLjI34LSykrJgbw21rZhEfMbLTJwgCZ0vq+XhXFkfPliMI4me+bkE8D904D58xnNWzZfW8s+0UpwzTsTxdHPjVXctYOiNyzPcNkFFayz83HyW/Rqyunxnuz+8n4HKWNrbx8vZjHC0UF6KhXm68cOdqki6yGXxbbx/vHEzn8/QctDo9dtZWPLV6PnfMTbqk09XaZXI+OJHJJ+nnUGi04sjSlCSeWJaG8yRMVruUNPfK+DQrh0/P5BgFZ6i7K48vmsd18eZpQ5cTlVbL8eoadhWXsK+snB7VQOrQTH8/bpmWyHUx0TjZ2Fz7xUVytYZdJSV8mZtHRl39kJ9J8vNlTXQUa6KjCXKdGk1vrwVKW9t45fBJ9haXGQVopKc796XM5IZpcdhZXR0u0XDI1RrePHya/57KRq0V0wdmhwTw6JK5zLmEAlSn1/PRibO8vvcEckPebEp4IE+tXkBS8MWd8NLLavn9F3upbReLmsK83XniuvksS4wY199T1tDG7z/ZS161KJDdnez5/qoUbpk/3SQX6NDZcv7x5WHq2rqNP3/fqmRuXZw0qgOqVGv4av85/rs93dhI3tHOhhuXTOP2FTNHFTRanZ4jmWV8sSubc8UDxwcnexuWp8WwblECCZF+Jn0OHV19nMis4Eh6KXnFDfQY3stIeHs4DRGkzo7jO8lrtToaWrqpa+yi3iAyVeeJy8FYWEgJDXBn3uwIFqZGEhvha5Jz3tUjZ8+JInYcyae4qsV4v6uTHTcsm85NK5PwHmUKkFan50BGCR/vzqKwcqDd1KKZEfzklvlEBIwewj1jEJz941gtpVJuXjSdh29Iw8mE7gx51U28sf0EJwrFtA57GyseXjePuxbPHFfkoKmrl3/tOsnWzAL0goClVMo9i2byyJp5F9VOqFep4t9HsvjweDYKw3d7QXQov71xGQGXsBl7R5+c948PCE6ApEBfnl23jET/qzsVKqehiQ8zzrCjoMQYRQtwcebhBXPYOD1+yrTym+qotFqOVFaxq6SU/WXlyNQDURRPe3tuTIjj5sREojyHuvXXvPAcTFNvL3tKy9hZXEJmXT2D/5hpvj6iBRwTRbCr62V5z991qju6+F/mGb4+l28sQnK1s+XWGYncM3sGfs5Xb1pEc4+Md49l8EVW7lABujSNOaGXLqe4Qybn3UMZfHrqHBpD3uyy+AgeWzmPaN+J52BptDq+OJnDW3tO0SUXBdPMUH9+un4hM0LH7mXZj14vsPdMCa9vO06tQUD6uDry0Jq5bJg79vg5jU7H9lOFfLAj3ShA3ZzsuG/lbG5bMoYAVWnYcaKQT3dnUd0khmEtLKSsSInm7jXJxIaOfjKtbuhg59ECdh0roLm913h/sJ8bC2ZFMDshmKTYAOxNTEfo6VVQ2yg6joPdx9rGzktX1W4hxc/bRXRVfUV3tV/Y+ng5m+RkC4JAZX07mXk1pOdWczqnypi7aWEhZf7McNYtSiBtRtioYXGZXMWWw7l8vu8MTYbP08bKguvmx3PHqlmE+Y8eOs4ureOdbadILxL7q1pKpayfF8/316biP0YfWBCL4N7YfoKDOeXGn795/jQeWjMHDxOGIPTTq1Dx/oEMPjqSjcrwXV85PZLHr1tAqNfE2wkp1Bo+PXWO9w5l0N3fBSTQh6dWL2Bu5KXLR+zok/PB8Sw+Tj9rFJyJ/j48tjSNRVGhV23kSaPTsbe4jA8zzpBd12i8f3ZQAPenzmR5dIRZcF4GlBoNRyqr2FlSwoGyiiERZR9HB1YbzLzkAP8RnfrvlPAcTKusj92lpewqKSW9tg79oD8twcebtTHRrImOItRtavQpm8r0KlV8nZPPhxlnqOsSw54WEgmrYiO5N2UmyYH+V+3BcCQB+tjSNOaEjW+e83ho6OrhjX2n2JItui8SCaydHsMjy9Mu6mTYq1Dx74OZ/O9INkrDSWnFtEieWDe+k6xGp2PLqXze3XWa5i4xDB7k6cKPr0tjTXLMmKFDjU7HjlNFvL/j9BABeu/KZG5bnDSq+NPrBY7nVPLJ7iyyCgeaws+KCeSuNcksSAof1enS6wWyCmrYcaSAQxklKFUDLqKFhZSECF+SE4KZnRBMYpTfuKvIBUGgu1chCtGGToMY7UKhVI/9w4OQSiX4ejkT6OdGgK8rQX5u+Ho5jzjrfLT309DSTWZ+DVn5NWTm19LZIx/ynJhQb9YtTmBlWiyuY+RSNrb18NnebLYezqPP8De5Odlx64oZ3Lw0Cbcxfj6rRBScGcUDgnPD/AS+tybFJMFZ3dLJmztOsju7eCCcnxLHj9bOIXAcrY3UWi1fnMjh7b2njYuxWWH+PHX9+BZjw73ul+l5vHPoNG294ucc7uXO46vmsSIh8pId6zr7FHxwIpOP088ZoyaJ/j48unQui6NG71hwJemQK/j8TC6fZJ2j2dAn2koqZV1CDPenzCRhChSqTnUUGg2HKyrZWVLKwfIK5IPEpq+TI2uio1gbHc3MAH+T0hu+s8JzMG19fewpLWNXcSmnamuHiNA4by/WxkSzNjqaMHezCL2U6PR6DpRW8L+Ms8ZRZgAJvt7cmzKDdfExV+04zuYeGe8cFQVovxOZEhrII4vnXNIQfHlLO//ad5LduWLfUQuphBtmxfOTZXPxd5t4DnNzt4w3dp1kc0Y+ekHAQiphfXI8D61MJcjD1eTXUWm0fHU8h/f3ZNDRf5L1defhdfNYNj1yzFCnRqdj5+ki3ttxmrpWUYC6Otpx38pkblsyugAFKKpq5tPd2exJL0ZncO38PJ1ZNSeGlXNiiQryHPV/06dQcyy73CDIaow9QfuxtrIkKcbfKERjwnwuuoXO5aC1U0Z2fi0Z+dVk5dca81z7sbG2JCkmgNkJwcybGU5E0Ohuem+fksPZ5ew5XURGQY2xc0CYvwd3rZ7FmrS4MXNms0rqePvbk8Yxl5YWUm6Yn8CDa1Lx9xh7X65v7+bdXaf5Nr3A+PtXzYzmx9fNJdzX9MIcjU7H1owC3tmXToOhYXqYtztPrVvAkoSJF6podXq2ZBfw5oFTNHaJDnCAmzM/WTaX9TPjLtl+0y6T85+TWVNOcBY2t/K/jDNszSsydkXxcLDnjpnTuCs5CS9H011rM+NHrtZwqLKSncUlHKqoMLrjAP7OTqyJFs25Gf5+486lNQvP82iXy9lrCMefqqk1VmMBxHp5sjYmmlVRUUR6uF+1X9hrgeKWNj7MOMPWvEJjeMvd3o47Z03njlnT8XEau2r6StDU3cs7xzL4MivPKECnB/jy0MIUlsVEXLIZ8IUNLby29wSHi8SCB0sLKbemTOOhpal4O0/8syptbOOf249xxFBIYSmVsmG2KEAD3E3PP1OoNHx6+Az/2Z9Jj6HHY2ygNw+vS2NhwtgnP61Oz870Qt7bkU5tSxcArg623L0imZsWTcNtjH6PzR29fLnvLJsO5Rh7TAKE+rmzaq4oQkN8x15YDnYGswpqjQU2/TjYWTMzLpCkmEACfV3x93LB39sFR/sr01NYq9PT0t5LQ2s3DS3dFFc1k5VfS3XD0F6dlhZSEqP8SI4PJjkhmIRI3zGdXIVKw7GzFew+VcTJ3Co0hu8pQGpCMHevTmbutNFDt3q9wOmiGv69M32I4LxxfiIPrk3Bz33sY3lLt4z3dqfzzYlcY0rA4sRwfrIujdhA7zF/vh+NTse3mYW8s++0sduDp5M9D69OY+M4C+4Go9cL7Mgp5l/7TlJj6Avs7ezAj5bO4abZiViP06E2lfquHj44nsVX2bnGY2iCvzePLkljSfTVKTh1ej37Syr4MOMM6TV1xvsTfb25L3Um18VFX/LRoN9lelUqo7N5uKISpXZAbAY4O7M2RgyjJ/n5XtT+Yxaeo9AhV7C3TBShJ6trhojQMDc3VkZFsioqkul+vubquUtEp1zBl2fz+DjrHI09oktgKZWyNi6a+1JmkBRwcYU1l4qm7l7ePZY55KAf6eXOQwtTuS5x9MkvF8PZ6gZe23uCU+WiY2xjacFdaTP4/uIU3BxGF2ejvm5VA2/uPsWJErFAw1Iq5YbUeB5aPgd/E8RBPz1yJR8dzOajg9nIDe2Mpof58ei6eaTGjJ3XptXp2ZUuOqA1BgFqZWnB8pmR3LxoOrOiAkY9ICpVGo7nVLLnVBHHz1Ua0yMAYkK8WTknhpWpMfiZ0I9TEASq6jtEIVpQQ3Z+7RBROxhnR1v8vVzw83LB39tZvO7tQoC3C76ezhOe2y4IAh3dchpaumlo7aaxtZv6lm4aDbdb2nuN7t9gJBKIDfMhOSGY5PhgkmICRpxnPxi1RsvJ3Cr2ni7myJlylIOKmcL8PVg1N4ZVc2II8hldxLf39LH1RD6bjuYZUyksLaRsXJDIA2tME5ydMgX/3pvB50fPotKI/8c5McE8sm4e08NMPy5odXq+zRIFZ52hwM7DyZ7vLU3h1rRpE+7ZKwgC+wvKeX3vCUqbxbZbbg52/GBxCnfMTbpk883LW9t591gm23KKjIU3SYG+/GhRKkujr87WQt0KJV+dy+OjzHPUdw+kW62Oi+L+lJnMCDCtwM/M+Gnvk7O3rIy9pWWcqKk1GiYAQS4uRrE5zddn0v4HZuFpIp0KBfvKytlVXHLBP8fH0YEVkaIITQ0KvOQNfr+LaPV69hWX8WHGWTJrB6qPk/x9uS9lJqvjorC+Cj/3dpmc/57K5pP0c8aGxgGuznx//mxumplwyU4+p8treXXPcc7WiEn49tZW3LdgFvcvmHVRLVLOVjbwxp6TnCwxVBhbSNmYmsAPl6fiN47QfqdMwX/2ZfD5kXPGXNLU6CB+uHoOs6PGLs7S6vTsyiji0/1nKKwZqLQO9XXn5oXTuD4tHpcxelHKFCoOZ5ez93QRp/NrjKF4gOmR/qycE8PylGg8XU0L6en0ekqrWsnMr6aospmGlh4aW7vpMkzjGQmJRJwn7zROV1Sr09Pc3jtqJTuIwtzPyxk/LxdC/N2ZFRfIzLggk6votTo9mYU17D1dzMHMMmSKAXEd4OXCqrmxrJoTQ0Tg6OF4vV4go7iWb47mcPBsudGddLSzYX1aHPeunI3vKNXx/bT39PHpkbN8cuiMcfGSFObHo+vnkxJlel61Vqdne3Yhb+89bezo4O5oz/eWzua2eaO38hoNQRA4VlLNa3tPkF8vVvE72drw4KJk7pk385JN/smtb+KdoxnsKyoztvWaFx7MQ4tSL2nB48VQ1trO/zLPsjm3wBjKdbWz5Y6Z07kreTq+V3GB6VSmtqubPaWi2MyqH1poHermyqqoKK6LiSbBx/uS7DdXjfB86aWX+OabbygqKsLOzo558+bx5z//mZiYGJN+/nLOau+3o/eUlnG4onJIVZeLrQ1Lw8NZFRXJwrDQq7o10FQlv7GZ/2We5dv8YuMCwNPBnluSErltZiKBV2FrrF6lik/Sz/HfU9l09IlCxNPRnvvnzuLOlOnjHltpCoIgcLS4ilf3nqCwQRRnznY2fG/RbO5Km3FRJ8Dsinre2HOS06WGAhALKTelJvLD5an4mjCusJ/Wbhnv78ngq+M5RiES7e/JXUtmsnZ2rEltmAqqmvj6aC67MoqNTeGtLS1YOTuamxdOIyli7OK0rl4FBzJL2Xu6iOziOuOJWyqRkBwXxJzEEJKi/IkN8TGpv+dg+hRqGodxIxtae2hs6Ta+54kikYC3uxN+XqKD6uftIob4vZzx83bB09VxXCkegiDQ0NZDTmk9Z4rrOZxdZhxlCeDl6sCKOTGsnhNLXNjYLkhnr5ytJwr45liuMU0CIDHMl5sXTmdVSrRJIq+otoWPD51hV3axMawfF+TNI+vmMT/e9GpsrU7PjjNFvL33NDVt4vtxd7TjwSWzuW1eEvYX0V81s7KOV/YcJ7uqAQA7ayvunT+TBxYm43IJemIKgsDpqjreOZLOiYoa4/0r4yJ5aGEK067C8ZBqQ3X6Z9k5nK4eCKfHentxf+pM1sXHXLIF+XcVQRAobm0zis3C1tYhjyf6+LAyKoKVUZFEeXhc8kXKVSM816xZwx133EFKSgparZZf//rX5OXlUVBQgIPD2I7D5RSeg1FptZyormFvWRn7ysrpkA8coG0tLVkYGsKqqCiWRIThZjfxUKeZC2nvkxurHVtkYq6dBFgcGcads6azKCL0kjZenghKjZavs/N4/3imsRG9k60Nd6cmcd/cmbg7jD19ZbwIgsDe/DJe33uC8hYxt8/V3pb7FszizrlJF+WAZpbX8eaeU6SXiQLUysKCm+Yk8oPlKfi6mi5AGzp6+M++TLaezjeGb90c7bh1wXRuXTDdpDnwMoWKnelFfH0kl5K6gQNrhL8HNy+cxrq5cSb1fWztlLEvvYS9p4vIq2ga8piVpQWxod4kRfozPSqApCj/Mau0R0MQBLp6FTS0dKMcpwCVSCV4uTni6+k85rSf0dBqdRTXtHCutIGc0gZyyhpoOy931cXRluUp0ayaG8uMqIAxhawgCGSV1PH10VwOnCkzCkUHW2uumxPLzQunjzkeFUQX+XBuBR8fOkNW2YBImR7qx/3Lk1mWZHoVuE6vZ8eZYt7Ze5qqVrHdlpuDHQ8sSeaO+UkmDTsYDkEQOFlWw9sHT5NZKUZjrC0tuHNuEj9YnIK74+R/p/V6gUMlFbx9NJ1zdeI+aiGVcP20WH64IIXISzzlaCLUdnXzxZlcvjqXT3ufWGgolUhYER3B/akzmR00epqMmfGh0+s529DI3rIy9pSWUdPVbXxMKpGQEhjAqqhIVkZFXvZhOleN8Dyf1tZWvL29OXz4MIsWLRrz+VdKeA5Gp9eT3dDAnpIy9paVUdc9UClqIZGQHBjAisgIVkRGmHuFTiIanY4DpRV8lp3D8cqBVb+/sxO3z5zGLTMSr7oKSI1Ox/bcYt49lkF5qygGba0suXVWIg/OS8bfdfL3YZ1ez45zxbyx/5SxyMHRxpq75s3gvvmzLioHNKO8jjd2nySzXBQH/QL0gSXJBHqY7kD3yJV8cyKPz4+cpbHTkNNrIWXNrBjuWjKT+OCxW6cIgkBeVRNfH8llT0axMZRva2XJytnRbJiXwIzIkXvMDaa+pYtD2eWcK60np7SBjvPaDAEE+biSFBXA9Ch/kqL8CfW7ugsPe/qU5JY1GIVmfmXTBeF6CwspsSHeTI/yZ25CCCnxwSa1aWrtkrE7o5hvjuZSZRhpCRAX7M3Ni6azJiXGpD6ovQoVm0/m89mRM9S3i8dRS6mUFTOjuHvJTKaFmp7DqdHq2HGmiPf2ZxgFp6u9LfcvSeauBTMuSnAeKqrg7QPp5BrEn5WFBTfNTuBHS+fgY8JiabxodXp25hfzztEMSlvEvFEbSwtunpXI9+YlE3gJG85PBJ1ez6GySj7NzuFoeZUxpOvt6MBtM6dx24xEczh9ElFqNByrrmFfWRkHyiuGGGHWFhYsCA1hVVQkyyIicLe/ckbYVSs8y8rKiIqKIjc3l8TExAseV6lUqAaNZOrp6SEoKOiKCs/BCIJAYUsre0rL2FdWRlFr25DHoz09WBEZyYrIiElN2v2uU9XRyedncvn6XD5dhqbMllIpK6IjuDN5OnNDLu14y/Gi1wvsLy7nnaPp5BrywSylUq6fHssP5s++JM6FVqdnV24J7x5Kp8xQ9GBnZcntc5N4YMEsvC6iCj69rJZ/7T5JdoXo/FhIJaxOiuF7y2YT4z+2wzX4PR7MKePjQ2c4W9FgvH9muD93L53FkmkRJhVo9cqVbD9dxDdHcihrGJir7u5kz+KkcJbNjCQlxrRxkYIgUNfSZRRs50obqBz0mv04O9gSH+aDn6cLvh5O+Ho44ePhjI+7E95ujhflTpqCIAh0y5Q0d/TS1N5DU3svzR29NLf3Ul7fRkX9cO/ZhmmR/kYBHR/mg62JOY41zZ0cOFvGgTNl5FUOOMR2NlasSYnh5kXTiQ8xrddiTWsXnx4+w5ZT+cb8TRd7W26eP43bFybhM440DrlKzden8/jvoSyau2XG13pgSTJ3zp+Bg4mDAM5Hp9ezL7+Mtw+mU9woOuu2VpbckjKNBxcl4+sy+UJKodbwzZl8PjiRRb2h17GjjTV3GSIlnlfZwrq5V8ZXZ/P44myesSgUYEF4CHfMnM6y6HBzs/dJol0u52B5BfvKyjlWVT2kEt3Jxoal4WGsjIpkUVgoDtaXJr94vFyVwlOv17Nhwwa6uro4duzYsM/5/e9/z3PPPXfB/VeL8Dyf2q5u9peXs6+snIzauiEV8j6ODiyLiGBBaAhpwUE4217d83GnAiqtll2FpXyafW7IlIswdzduTkpgXUIMAS5Xz34iCAInK2p491gGJysGepguj43grtQk0sKCJ70Vk14vcKCwnLcPnKbAkANqbWnBxuQE7pybRNQEJyEJgkBGeR3v788wVsEDzI8N5a75ScyLCR1XVX9edROfHDrDnuyB8Xh+7s7cOn8a61LiTBIjgiCQU9HIN0dzOXSufEj1uYOtNQumhTE/IZQ58cEmhfX76ZYpyC1rJKdsZPdwMBKJOGPcx10Uo97ujni7OuLl5mjyRKR+dHo97d1yWjt7aemUGcVlU8fYBUeDXdrpkaJLa+r+pdHqyKlo5ER+FUdyKig/T3xPC/NjfVoca1JjcbQbO3dZo9NxvKCKTSfyOJJfYcyvDfd15+4ls7guJXZchT517d18czqPz0+co8dQCOXpZM89i2Zx+7yJ51P3qdTsOFfMh8eyqTBEKeytrbgzLYn75s/C02nyxV9jdy9fZ+fxScY5Y264h4M9986dyd2pSThdgtzwiaLWajlSXs2WvAL2FZcbz3GudrbckpTA7TOnE+LuemXf5DWAXhAoaG7hWHU1hysqyapvGNJ73N/ZyRBZjSQlMOCqLHa+KoXnT37yE3bu3MmxY8cIDAwc9jlXu+M5Gl0KBQcrKtlXVs7RyqohUwCkEgnTfH1YGBrC/NAQZvj5XZU7zlSiqLmVz87ksiW3kL5Bs2RnBfqzITGWNXHRVzTscD7DVaf6uzpzy8wEbpqZMOmOiliFW8VbB09ztnpApM8I8eO21OmsnhY94WT/grpm/n0wkz3nSo0HR29nB25ITWBjasK4mtG3dMv48mgOXx3PoVMmnoQlEjHfb8WMaJbPiDSptZNGpyOruI6DZ8s4eLactu6h+YxRgZ7MjQshLT6EmVEBJhU4GV9bq6OkpoXSmlZRCHbIaGrvMYrCwe2bLjXuzvYGx9UZb3fReQ30diUxwg/3ceSlCoJATUsXJwuqOVVQTWZxrdGNBNGhnx0TyNKZkSxJisDLdWzhrtZoOVVcw94zpRzOKzf2dgVYmBDGXUtmMjcm2OTohEqj5UBeOV+fzjUWvAEEe7ry4NLZrE+OG9f/cTCFDS18cTqHbWeLjA3YnW1tuHf+TO6aNxNXE/KGx4NGp+NQSSVfZeVxtKzK+L0JdHPm+/Nms/ESdsMYLzq9noyaerbmFbG7qJTeQefk5CB/7pw1ndWxUVft4I+pQkNPD8eqqjlWVc3Jmho6DdG8fuK8vVgRGcHKyEjivL2uqqjecFx1wvPRRx9ly5YtHDlyhLCwMJN/7mrI8ZwIKq2WkzU1HK6o4nh1NRUdnUMed7C2Zk5QIAtCQ1gQGkKYm9tVv1NdrchUarYXFLM9v5jT1bXGfCNLqZQF4SGsT4hleXQE9hNsozLZVLR28HH6Ob7NKaRHKR7QpRIJCyNDuSU5kSXRYZO6KBEEgYzKOj4+cZaDheXG/o/OtjZsmBXHTbMTifEzPVw+mNq2Lj49fo5vMwuM4wcB5kQFcVNqIsunRZosDJRqLbuyi9l6Kp/s8vohjyUE+7BiRhTLZ0QR7OU65mvp9WI+6JGcCk4VVFNY08zgo5yNlQUzowKZFuZLbLA3MUHe+Lk7Teg7KAgCnb0KoxBtausV3couGa2dsjFdyvORSiS4Odvj5SY6pj4Gcenr4Yy3m+OEe4PKlWpK6loprm2lqLaFjKJaGtqHTjZyd7JnTlww8xJCWTgtDOcxWleBGC4+UVDFvnNlHMmrMI7TBLFv5nWzY7llwXRCvE2fEFfS2Mam03l8m1VIt2G/kkhgblQwt6ZNZ1lixIQKDGVKFbtyS/gyPZe8umbj/aGebtyaOo1bUhInvRNFZVsnX2fnselsgbH4BiA1NJDbZk9jTXz0VTEVSxAECppa2JpfxPb8YmNhJ4CPkyPXxUdz0/QEYrwnFjExI3bOOV1bx3GD2KzsHFkXLI+IuKqid6Zw1QhPQRB47LHH2LRpE4cOHSIqKmpcPz9Vhef5NPT0cLy6huNV1RyvrqFTMbQHoL+zE/NDRBE6LyTYXCk/QZp7ZWwvKGZbXhF5TQN9IO2sLFkRHcn1CTEsCA+5KtxmpUbL3sJSvszKI71qoLLXw8GejTPiuWlWAuGe7pP6O1t7ZHyTlc9X6Xk0dA2Ijnh/bzbOTuC6pNgJOT1qrZaD+RV8cyqPk6XVRpHnbGfD+tlxrE+OIz7Q9Jzn5s5eDuSUse9sGdnldUNEY3SAFytmRLJiRpTJIxM7ZQrSC2uM7l6LYc78YJztbYgO8iY2yIvoIC9ig7wJ9XW/KkTBeOnokVNU20JxbQtFNa2U1LVS09LJ+Ud6K0sLZkT4kxYfQlpCCFEBXiaF5vuUao7mV7LvbCnHCiqHNJz3dnVkeZL4/5kRblqxF0BXn4I9OaVsTs8nt2Ygp9THxZGNqQncmJowrqla/ej1AplVdWzKzGdPXqmxKM3SQsrKhEhuTZ1Oavjk9sNUqDXsMXy3M6sHFlGejvZsnJHAzbMSCPW4OkY1V3d0sS2/iK35RVS2DwghZ1sb1sRGsSExjtnBAeZhKhNAq9eT29TE8aoajlVVc7ax0ZhWBGJx8nQ/X+aHhLAwNITpfr5Xxblpolw1wvPhhx/mk08+YcuWLUN6d7q4uGBngri6VoTnYPSCQEFLi3HVk1nfMKRxvQRI9PVhfkgI80ODmeXvbw5pTIDytg625RfxbX4RNZ0DLSdc7Wy5Li6a6xNjmRXof1UcUKvaB1yRNtmAK5IU6MsNSfGsTYzGbRLTBnR6PSdKa/g6M5eDhRXGXptWFhYsj49g4+wE0iKDJ+QqNXT0sDkjn03p+TR1DRQgBHq4sGZGNGtmxBDtN/os9cG09/RxMKecfedKySipHTKxJ9zXXXRCk6KIDjDtNQVBoKKxnYyiWgprWiipa6W8od34GQzGxsqCyABPogO9iA32JsLfAy9XRzxdHCbciHyy0On1dPYqaO/po7ali+LaATfz/DSDfrxcHYgJ8iYm0IukCH+SowOxM7G/ZY9cyeHcCvafK+VEYfWQ9AJ/d2dWzIhixYwoEkN8Tc4r7VEoOZBbzq5zJZwuqTGelC2lUpYmhnPTnGmkRU9wP+zsYXN2AVuy8qnrHFhkhXu5s3F2AjfMisdjElsi6fUC6dV1bD1XyK78EmP4XiqRsDAqlFtnJbJ4kqMZE6VV1sfOwhK25hWR0zAg8m0sLVgWFcGGxFgWhoeYx1iOE0EQqO3uNgjNKk7W1NKjGjr1LNjVhQWhoSwICWbuNVb7cdUIz5FOBP/+97954IEHxvz5a1F4no9CoyG9ro5jldUcr66mpG1oUr+NpQWzAwKYGxxMWnAQib4+5srBcSAIAjkNzWzLL2J7QTFtg8JdAS7OXBcfzZrYKBL9rnwXAo1Ox+GSSr7MzuNYWZVRZFlZSFkSHc6GpDgWR4VN6hzozj4F284W8k1mPiVNA10afF0c2TArnhtnJRDi6Tru19Xp9ZwsqWFzej5HCitQDHLFQr3cWD0jmjUzookcR7FTV5+CQ7kV7DtTyqni6iFi0dfNieTIAJIjA0mODCTYy9Xk/6dao6WiscMg3looqm2hpLZ1SM7j+TjYWuPp4oCHsz0ezg54ujjg6TLourMDHi72427rox0kKNu6+2jv7qO1u4/2HvmQ+zp6FUOKDwYjkUCIt5vRuY0JFsXmeHJAZQoV5yobySqrI7OsjoLq5iFuTbCXq1FsxgWZPglFplRxML+C3WeLOX7e/zDW34t1s2JZPzseD6fxi0KlRsv+/DI2ZeVzqrzG6PA62Fizdno0N81OZHrQxc2jPp+K1g62nCvk25xCYw9fEHM3b56ZyMYZ8ZekIn68dMgV7C8pZ2dhCScqa4z7jlQiYX5YMNcnxLIyJhLHSzSB6VqlWSbjZHUNp2pqOVlTS33P0PQVZxsb0kKCWWAwkq7llotXjfC8WL4LwvN8mmUyTlTVcLSqihPVNbTJh/YYdLS2JjUokDSDEI328rwqXLupgFav53RVLVvzi9hTVDakKCnAxZlVsZGsjo1iRoDfFf9MW3v72J5bzNacQgoaB9IGXOxsWZcYww0z4pgeMHknUUEQKGxoYVNWAdvOFhorhwGSQwPYODuBVYlRE5qMJFdpOFJQwe5zJRwpHDpLPdLXwyBCYwj1Mj382KtQcSSvgn1nSzlRWGWc692Pl7MDsyIDmBURSHJUAOE+HuPqIKDXC9S1dRvC1aIzWtXUQXu33BiuvdJIJGJepo+bEzFBXqKbGeRFVIDnuKvpe+RKssvrySqtI6u8nqLalguEbYSfh1FsRvqZPgml//+/61wJRyfp/9+PIAjk1TWzKSufHeeK6VUO7Lep4UHcNDuBFQmRk+pQd/Yp2JFXzJZzheTUDziGTrY2rEmI4sakeGYFjz1Z61LTKutjX3EZu4pKSa8e2nUlyd+X9YmxXBcXfdW1bbqa6VQoOG0Qmadqainv6BjyuKVUygw/P2P9xnfJKDILz2sEQRAoa+/gZI24ojo1jHXvZmfH3OAg0oKDSAsOJtTNdKfnu4xSo+VgWQW7Cks4VFZpnCkMYiPkVbFRrI6NZHZQwBWflFTc3MbWcwVszSmitXcgjBrq4caNSXGsT4ojYBKb06s0Wg4WlrMpq4ATpdVGAWJnbcXKhEhWT4tmXlTwhEJxfUo1B/PL2X22hGPFVRc4XqtnRLM6KZqgcbisCpWGc5UNZJXVk11eR25V0wWV5m6OdswMDyA5KoDkiECiAjwn9H8VBIE+pZr2Hjlt3QYHst+J7L+vp4/2bjmdMvkFeZWmYGttaXBQDc6pwUX1cLHHy0W87eFij5uj/YRzUDt65WSV1RnFZmlj2wXvNdDTheTIQGZFBDA7MpAAT9NzLBVqDceLqth1tmTSHO/BVLR0sDe/lB3nio19awH8XJ3YmCyG0gMnkBM6EmqtlkMllWw9V8jh0ko0hv3WQioWBt6QFMfSmIgrXpne1NPLnuIydhWWklU7dF53vI83q2MjWZcQQ7Cb65V6i1MKmVpNZl0dJ6prOVlTQ1FL65DPtD81Li04iLnBwSQH+F81fTUvN2bheY2i0+spbG3lZHUNJ2tqyayrH9K2CcDH0ZHUoABSA4NIDQok3N1cMT8WCo2Go+XV7CkuZX9JxRAn1MPBnhXREayJjSI1JPCK5mjp9HpOVtSy9VwBewvLhojllNBA1iXGsCIuYlIdjKbuXrZmF7IpK984GQnE8OWS2HBWJkayIDp0Qo7SSDl+ACGersyNDmFeTAipkYHjqjZWabTkVTeRVVZHVmkd5yobL3ApHe1siPb3JNzXnTBfd8J9PQj39cDbxWHSvi9anX5I/rYpSJBgO8658aOhUGmobO6gsrmD8sZ2Kpo6KG9qp7a164Lnhvm4M6s/XSEiYFyN3fV6gZLGVk4UV3OypIbsyvoh4n+iOb79CIJAaXM7e3JL2JtfNkRs2lhasDIxio3JCaSGB01ab1ytTk9mdR278kvZmV9M96AoQLyfNzcmxbFuWuyk5opOhNqubvYUlbK7qIyz9Y1DHpvu78ua2ChWxUaaxaYJ9CiVZNU3kF5bR3pdHXlNzUOcYoAoTw+j0ZMaFIjLNZSneTGYhed3BLVOR25jEydrxNVYdkPjBSc6D3t7UgIDSA0KJDUw0ByaHwO1Vsvxyhp2F5Wyv6Sc7kGhO1c7W5ZHRbAiJoJ5YcHYWV25AhOZSs3ewlK2nC3kdFWt0a2SSiQkhwSwOj6KlXGR+FzExKLBCIJAdnWDeOLPK6O5Z6Ay3M7KkoUxYaxMjGJxbNiEwvFdfQr255ax62wJGeVDi4gspBKmB/uRFhNCWnQwiUG+43L6NFodBbXNZJfVk1lWx9mKhiFtfwbjaGs9SIgatj7u+Lk7T3qz/8mkV6GisqmDiqZ2Kpo7qGgUrzd09Iz4M1H+noac2ABmRQTg4Ty+BUtzt4yTBqF5qrSaDtl53TrcnFmVFMWaGdHj6mrQjyAIFDS0sDevlD25pVQPWvhYWkhJiwhmZWIUKxMjcbabnJO/WqvjdGUtewpK2VdUTueg8YQ+zo6snxbLhqQ4on2uXFshQRAobW1nf2kFe4pKyR/UwUMCzAryZ3VsFKtiIvGfYi15LjcdcgWZdaLITK+tp7ClhfMFUbCrizG1bU5Q0FU3qvlqwSw8v6MoNRrONDaKq7XaOs42NqI6L+ToYmvD7EBRhKYGBRDn7f2dyUEZLxqdjtPVdewuKmVvcdmQGbk2lhbMCw1maVQ4S6PC8XGa/BnOptLY3cu2nCL2FJYaR3T2MzPIj1XxUayKj5q0cLxeL5BT28je/DL25JYOac1kbWnB/KgQViVGsSQufEKCQKZUkV5Wx8mSak4WV1Pd1jXkcSdbG1KjgpgXHUxaTMi4GtaD6GSVNbZR0S/UDCKttq1riOAdjK21JcGerrg52ePmaHfeZeA+Vwc7XBxsJyU9Q6HS0CGT0ylTGC7i9S7jbfHS2NlD6wiV7CCmGQwIaVFMxwR64eowvk4JcpWGrIo6TpRUc6qkhrKmoYWQdtZWpEYGkhYdQlp0CGHe44+26PUCuXVN7MkrZW9eKfWdk7tvDYdKo+V4RTV7Cso4UFRu7K8L4vz3FbGRrE2MZm5Y0BVLu1HrdGTW1HOgtJwDpRXUDfrOSSUSUoMDWR0bxcqYCLyv4LHoaqdFJiOjrt7oaJa2XThqNtTNlZTAQKNZM9X6aV4pzMLTDCA2ss9tajZ+ybLrGy4IzTtYW5Mc4M/swABSAwOZ5utjbt80DDq9nszaenYXlXGwtIL67qFOUqKfD8sNIjTO58pNmajr7GZvYRl7Cko5Uzs07Jbo78NqgwgNGadYG4l+V2pPbil78kqHhOP7Xaml8eEsjAnDf4LCt76jm5MlNZworuZ0ac2QwicQw7gpEYEkBvsyLciXSD+PCaVEaLQ6qls7RUFqCEtXNndQ1dKJZhzTiaQSCS4Otrg52o07508nCHT3KemSKcZdxOTl4mAUmBG+HoT5uhPm4477BCrEQez7mlvTTF5tE2crGzhbNTSiIpFAYpAvc6ODmRcdQlKI34Rm1qs0WjIq6zhSVMm+/Ml304dDodZwtKyK3QWlHCqppE814IB7OtqzMi6SVfFRpIQEXrFerl0KJUfKK9lfUsHRiipkg96jtYUFaaHBrIiJYGV0BO4OVzbcfzUiCAJ13T1k1NWRUVdPRl0dVZ1dFzwv0sOD1KBA5gQFMjswAB9Hs3CfCGbhaWZYNDodBS0tRkc0s75hyDg0EA9oSX6+pAQGkhIYwMwAfxy/o8nSIyEIAsUtbRwsrWB/acWQXngAfs5OLI0KZ1lUOHNDAq9YP7zmHhl7C8vYXVBKVnX9kCrlWF8vVsVFsjI+kkgv0yuUR0MQBEqa2sTQaF4p5S1DKz6jfDxYGBPGopgwZoRMbGysTq+noK7FkEtYzbmqoU2ZQXSj4wK9SQz2JTHIl2nBvgR5uEz4b9Tq9NS3d1PX1m10HYdeBu7rPU8UXyzWlhaikzqMu+rmaIe7oz2ezg6E+rjhfBFjHmVKFfm1zeTWNBm3zd0XNtr3d3MmLSaYtOgQ5kYF4zLB39nQ2cOR4kqOFFeSXl47JF/Z3tqKJXHhrEqMmnD+8HDIlCoOl1axp6CUI6VDCwp9nB1ZFRfJ6oQoZgaZ3vh+sqnq6GR/SQUHSyvIqq0fkl/o4WDP0sgwlkaFMz8s5KqZxna1oBcEytrbyaitJ7NeDJ03y4buwxIg1tvLEPEThaaHvVm0TwZm4WnGJHR6PUWtbYYcl3oy6+ppP699k1QiIdLDnel+viT5+jHNz4cYT8+rohHy1UKrrI9DZZUcKCnneGUNSu3ACc3W0pLZwQGkhQaRFhpMnI/XFTmptcvk7CsSRejpyqE5lP6uzsyPCGZ+RAhzw4InbU51RUsH+/LLOFJcybmaxiHC197aipkh/qSEB5IaHkR8gPeE9qk+pZrMijrOVTUaRdPgljr9ONnaEOXvSay/F7H+XkT7exHi5TrpIxI1Ot2QUPh457hLwOiWujnaY29jNanuuVanp6mrl7KmNooaWimub6W4oZXa9u4LniuRQISPB9OCfUkM9mVOZBDBnhPrmtHY1UtmZR0ZFXVkVNYNccYBvJ0dWBgTxtK4cOZFhkx4BvtgdHo9+Q0tHC+v5nh5NWdrhy5SAlydjRGA6QGmN76fTNpkfZyqruVEZS2nqmuGhNABor08WBYVwbLocKb7+5rz8wfRLJOR09hETlOTYdt8gZFiKZWS6ONDalAAswMDSQ7wNxcDXSLMwtPMhBAEgarOLtLr6sg0hCbqui8sTrCxtCDOy5skP1+m+fqQ5OdHiJur+aCI2KbpZFUNB0pF12LwzGMQc2xTQ4KMQjTc4/J3HeiUKzhQXMGeglJOlNdcGD7192FeeAjzIoKZGeQ/KQ3ru+RKjpdUcbS4kmOl1XT2DS1EsTMI0dTwQJJDA4gP8JlQaxq9XqC6rZO8miZya5rJr22isL51xOpyV3tbAjxcCHR3IcDdmUAPFwLcXQjycMHXzWnKLbAEQaCrT0ldR7fo0nYYLu3d1Hf00NjZM2Ieq7+bM4nBPkwL8iUh2Jf4AG8cxtkPtP891HV2c6aqgQyD2KztGCpspRIJScF+LIoJY1FsGDG+4690H466zm6OlVVzoqKa05W1QyrRQWxBtio+ktXxUcT7md74frKQqVSkV9dxsqqWk1U1lLQOzTG0lEpJDQlkmSFtJ8h18lpCTWV6lEpym5o519RErkFsNssuzGu2s7Jkpp+YOpYSFMAMP78rWgT6XcIsPM1MGi0yGTlNzeQ0NnGusYncpqYLeokCONnYMN3Xh2m+viT5+TLdz/c7nyvTX316sqqGk1W1pNfUDcnTAvBxcmRuqChE54UG4+t8eaec9KnUZFbXc6K8mhMVNZS2nFcwYmVJamgQ8yKCmRcRPClheb1eoKS5jYyKWjIq6sisrKdboRzyHEuplChfTxIDfZgW6EtioA8R3h4TyrfTaHVUtHRQ3CC6e8X1rZQ2tV1QhX0+UokEX1cnoyD1c3XC2d4WZzsb49bJzgZnO1uc7W2xsbS4JEJGp9fTq1DRq1DRo1DSLVfRq1TRI1fSo1DR3is3CEtRZI42cQnE0aih3m7E+nsR4+9FtL8nMf5euE+wLVBbbx95dc3k1TWRa9h2yYf+P6USCXH+3qSGBzI7LJBZof6TUhzUo1ByuqqO4waxWXOewHWytWFuWBDzI4KZFxFCsLvrRf/O8aDSasmua+BkZS2nqmvJbWi6oD1PnI8XaaHBzA0NYnZQwHd+epBKq6WgpcXoYuY0NlHZ2XnB86QSCdGeHgPnHF9fojwnlt9t5uIxC08zl4x+VzSnSRSh5xqbKGhpuaB6HsDH0YHpvr5MMxwUEny8cbObvJnjUw2tXk9eYzMnK0Uhml3XgPo8Jy7M3Y200CDmhAaRHOh/2StUm3tknKio5kR5DSfKa2jvG5p64eVoz6zgAJKD/ZkVEkCsj9dFF1/o9QKlzW1kVtaRXlHHmeoG2mXyC55nZ2VJXIC3QYj6Mi3Ih0C3iedu9inVQ9zAweKtvr172H16NKwsLHC2F4WoKEhtxh0y1un19CnV9BhEZq9ChWyE1k+j4e3sYHRvxe2Am+vt7DjhsHKfSk1+fTO5tU3k1TWTW9dEY1fvBc+ztJAS5+dNSnggKQahORlpDS29MrJrGsiqrierpoGiptYhKRyWUilJgb7MjwhhXkQIif4+l7U4SK3VktfUYnA1a8iua7hgPwpxcxUjHmHBzAkJwt3+u3tM1Oh0lLV3kNfcbAybF7e2XZC3DRDk4sJ0w7lkup8PCd4+5jzXqwiz8DRzWdHodJS0tRuFaE5jE6Xt7cPOkg5wdibBx9t4SfTxwdPhu9kXTakxuCEGRzSvsfmCzyzYzYXkoACSA/2ZHRxA2GUcCKDXC5S0tHG8XBSimdV1F5xE7a2tmBHkR7JBjE4P9Lvok4EgCDR1y8itazIKnPz65iGVx/242tuSGOhLrJ8X4d7uhHm5E+blhtNFihxBEGjr7aO+vYfaDlGQNnfJjGKwRzHgOPYqVCPOTZ9M7KytBlzWfufVzhY3B7shwjLA3fmicyS1Oj31nT1UtnZQ0dpBWXM7eXVNVLR2XDDhSCKBMC93ozM9LdCXGD/Piy6qEwSByvZOsqsbyKypJ7um/gJHEyDM04154SHMjwwmNTTosjqGPUolZ+oayaptILO2npyGpgsWk14O9qSFBZMWGkxaaNB3tremSqulpK2N/OYWw6WZota2Cz4vEPtPT/f1MQrNab6+32mBPhUwC08zVxy5WkN+SzM5jc3kNDWR19RMdVfXsM/1cXQg3seHBG9vEn19SPDxxtfR8Ts3calHqSS9up6TVTVk1NRTfN54NgA3ezujCE0ODCDe1+uyhZZUGi15Dc1kVteTXdNAdm3DBYU8llIp8X7ezAr2JzkkgFlB/pMy2UWvF6hs6xjitBU3to2Yu+nt7GAQoe6Ee7mJW293fJwnf78SBIE+lZoeueoCYTreyUVIJDjaWA8J6TvZiu7pRFoVjUWfSk1VWyeVLR1UtHZS0dpOZWsn1W1dI753P1enQSkQviQEeE+Km6nR6ShsbCWrpt7oaA5u4A6iyI3x8RIXOiH+zAryx9fl8qWnNPX0klnbQFZtPVm1DcN+R93t7UgOCmBOSCDzQoOJ8HT/zh3LFBoNRa2t5DW1kN/STH5zC6Vt7cM6mY7W1iT4eBucTFFo+js7fec+s6mOWXiauSrpVamGrHbzW1qoaO+44MAN4sE7zsubOG9PYr28iPP2Itzd/TuVv9OrVJFd10BWbQNZdfWcq7/QTbG1tGRGgB/JQf4kBfgx3c/nsvX00+vFHFZRJIhioannwhY8Aa7OxPt5k+DnTYK/D/F+3pMiRtVaLcWNbeTVNVPa3EZlayeVrR1D5tmfj721FWEGIern6mgyFzIAAQAASURBVIS3s6Px4uPsiMdFzD+/2hAEAZlKTXO3jJYeGc09Mlp7+mjukVHT1klFaydN3ReGyfuxsbQg1CDcw73ciQvwJjHQFy+ni49QqLVaSlvaKWhsIb+hhYLGFoqbWy9w1G0sLZge6EdysD/JwQHMCPK7aDfbVDQ6HSUtbeQ0NJFVJy60hiu2DHFzJTnIX4xMBPlf1qjElUYQBFr7+ihsbaW4pY2i1lYKW1spb+8YNgrgamtLgo/PkKhXsKu5MPVawCw8zUwZ+tRqilpayW8ZEKSlbe0XJOADWEmlRHp4EOvtZRSjsV5e35kQTH/+WL/bklVbP2SkZz+Brs5iXq2/L9P9fYj39cbhMvVibejqIcuYg1d/QbFSPz7OjsT7eYuC1N+bBD8fvJ0mZ056j0JJVWsnFa0dRjFa0dpBTfvIk4n6kUokeDjai0LUpV+UOuDt7IinowOOttY42FjjaGONvY01DjZWl20xJAgCcrUGuUqNTKWmT6WmT6WhS664QFi2dMto6e1DoR690AjAw9HeKMZFh9idcG83/FwmZ0yoUqOluLmVgoYW8htFkVna0oZGd6H75WJnaxSZs0L8SfDzvix9cPWCQHVHFzkNhqrphmYKmy/MXZdKJMT5eDHbIDJnXYE87CuFWqejvL2DotZWilpajSKzQz58kZ6nvb0xgpXg7U2Cj4/ZybyGMQtPM1MapUZDcVsbhYaDW5FhJS1TD19k4ePoQIyXKEKjPT2I9vQkwsP9mp/ApBcEKto6yKytJ7O2ntzGZirbh6/+jPR0NwhRX6b5+RDtfXl6sfYolBQ2tRpdrYLGFirbL8wTBLFBdryfN3F+XkR4uRPu6U6Yh9uk9dpUa3XUdXRT2dpBZVun0Qnsv7T29o0pTIfDxtICBxtr48XR1hoHa2scbK3H/Rn3i8s+lRqZUk2fWk2fUhSZcrVmQrmkzrY2eJ8nooPcXYxCc7L6tur0euq7eqhoE8P2xc2tFDS2UNHWMezn6mJnQ4Kf6IDH+4uLkGA318vST7O5V0ZOQ5NBaDaT23BhD0gQP7tEPx9mBvgxOyiApAC/a77qXBAEGntllLa1UdrWLh6DDS6mZphQuVQiIczNjRgvT2K9vYjz8iLex/s739Xku4ZZeJq55hAEgfqenkFiVFxt13RdWGwA4sEwxNWVKE8Poj09iPL0JMrTg1A3N6yv4XB9j1JJXmPLkJNqc++F4W8bSwvxJOHjRazhEu3leVlOqjKVmuKmVqP7ld/QTHnr8KE5EN3RcE83wjxFMRru6UaEl8ekOaT96PR6OmRyWnr7hg1Pt8vkyI1Oo3rcVe+ThVQiMYhcKxxsrHGys8HH2REvpwGX1sd5QGhO1uSffuRqDZVtHVS0dRq2HZS3dlDd0TViw3x3BzsS/HxIMAjMeD9vAlydL7n71e9kFrW0UtzcRlFLK3mNzRf01wVxaluCrzfT/H0MCzRfQtyv3TCwIAi09ckpaWujtL1d3La1U9rWPuIi38nGhlgvMf2pX2RGeXqYe2WaMQtPM98d+tRqilvbDKvyNuMqvUupHPb5llIpYe5uRHmIzmikhzsRHh6EuLles4K0uVdGbkOzMYyY29hMzzAheoAgVxdRiHp7GgVpoMulFwgKtYaSljYKGlooam41ipq2Ydoq9WNvbWUUosHurvi5OuPv4oS/izO+zo6TMv1mNDQ6HX0qDX0qlWErupRytcGtVKmHLaYYDQlgb2OFg7UYyu8P7fc7qA421thZWV7S/4cgCLT3yWns7qWhu5fGrl7qurqpNPxPGkbJC7W2tCDMw038v3i5T3oaxWjIVCoxz9AgMIuaWylpbRsyGrMfqURClJeHISVFFJpRXtdmD8h+gVnW0U55ewelbQMic7TjZKibmzGC1C8yzaFyMyNhFp5mvtMMXsmXtLVT2t5GSWs7pe3t9I2wkreQSAh2dSXcw50IdzciPDwId3cjwt0d52tsxJpeEKjp7CKvsVlMY2gWT9LDuUAAjjbWxHh7EuMtCtJILw+iPD1wmYQG4GPRrVBS2dZJRVuH0WWraOugpmPsfE1PR3v8XJwMF1GU+rk44e/qjJ+zE272dldkTOKVRq7W0Nwjo7G7xyguG7rE643dvTT29I456tPdwU5MhfA0iEzDAsDf1fmSj4TV6HRUd3ZR1tpOSWu7uP+2tF4wbrIfG0sLogxh4FgfT+J8vEjwvfZ6QGr1emq7uinv6KC8vZ2Kjk7KDNvh0ghAXOiEuLkSbYgIRXt6EuXhQaj7tR0ZMjP5mIWnGTPDIOYu9RpX+yVt7ZS3iy5An2bkIgwvBwdRhBrEaIirKyFurgS6uFxTB+cOucJ4EhfFaBvlbe3D5nUBeDs6EOnlQaSnOxEe7oS4uxHq7oqvs9MlD0+qtTpqO7tEIdraQV1Xt1E4NXT1DOtynY9UIsHFzhZ3Bzvc7MXL4Ouu9ra42g1sne1ssLOyumQTisaLRqdDpdEiU6npUijpkisGtnIlnXIFHXKFuO1T0GW4rTThs5FIwNvJ0SDWRdEeZkh3CPN0w+0yFPTJVCqqOroMl05KW9spa2unqr1zxH3Sx8mRWG8vYnwGhGaouxuWl1gMXy4EQaBdLqe6s4vqri4qOzupaO+gvKOT6s6RPxepREKgizMR7u7GtKNoT08i3N2wNYfJzUwCZuFpxsw4EASBZplskEPQQXl7BxUdHcPOA+5HKpHg7+wkClFXV4LdXI2iNNjF5Zo4oGt0OiraO42CtKRFPPk39ozehifYTfwcQt1dCXJ1IdBw8XdxuuRFX4Ig0KVQDhGixuvdPTR0947acmksJBKxjZWtlRV2VpbYWFliZ2WFrZUltoOvW1qOuzWTXhBQa3UoNBqUGi0KjRaVRmu8rdRoUGi0KDXacYfxB2NvbWUUlf0usL/RHXbCx9nxkoed+yMTdV3d1Hf3UNvVQ3VHJ1UdXVR3dl0wNWvI+7eyIsLTnSgvD9GJ9xEd+Wuhw4VeEGiRyaju6jIKzP5tTWfXqItkW0tLwt3difBwM2w9iHB3I9TN7ZovtjRzZTELTzNmJolelYqKjg4q2jsp62inqrOLGhNOAAC+To5D3NEgFxcCXZwJdHbB08H+qnDNJopMpaKsrYOyVrEYobK9k+qOLmq7uscURN6ODgYh6kyQqwsBLs7G277OTpfFndLodHTKFXTKlXT2yY2uYIdcQWefgi6F6BoOdhLlJrQmuhJYSCWDnFlbXO3tcLWzFZ1bhwEn193eHjd78X4HG+tLvv8JgkC3UkV9Vzd13T3UdXVT29VjFJr1XT0otaO7rx4O9sYFTKSnh9Fh93dxntJFPwqNRvxMurup7RY/j5oug7js6h71c5EAAS7OBLuIxxZRYLoT4e6O32WINpgxMxxm4WnGzCVmcMir6jxnoqqzc8Sq0H5sLS0JcHEeIkYDDQIsyMUFlymaV6rV62no7qHaECKt7uykziA26rp6kI8h1i0kEnydnfBxcsDb0REfJ0e8Dde9B913JVra9Ie2lVqtwY0ccCWVRkdy0P1qzbD9aEdDAtgY3FJba9FRHeyiiqF+S8P94uPWVyD0r9HpaO+T09zbR4tMRnOv2De0RSajRdZHS6+Mxh7ZiLmF/UglEnydHAlwFff9YFcXY8pGqLsrjjaXp1n8ZKPW6Wjs6TUKy36R2b9tk4/s5oL4PQhwcTYuXENc3Qg1RBECnJ3N7qWZqw6z8DRj5goiCAKdCiXVXZ1GQTr4xNPU2zvstKbBOFpb4+fshL+TE35OTvg7O+Pn5ISfs3jb18lxyp18BEGgU64wul9GQdrd74L1mjxi0sHaaogY9XS0x9XODjeD0+fe7/rZ2eFiZ3tNVitPJoIg0KdWG3JClXQpFHQqDI6wXEGHXE6rrI8WWR/NvTLa++Rj7sP9eDrYGx3twe52kKsLvs5OUy5PWhAEOuQKGnt7aejpMWx7h2xb+/rG7LnqaG09sPB0cSHI1cUgMkVxad5nzUwlzMLTjJmrGLVOR0NPj5jXNsgF6Q+5jeWG9ONpb4+/QYj6OTvj6+iIt6MoxHydHPF2cJxSlbt6QaClt4+Gnh5aemVGkdPvoPW7azLV6G7ycDjZ2IhhZjtD4ZCdLY42NjhYi62LHKwNfTGtrXG0FicTifdZ42Bthb219VVboCIIAgqNVmw2rzb0GVVrjLdl593uU6npVoppBJ2G4qMuhXLEwpSRsJRK8XJ0EPc5J3Hf63eofQzOdICr85Tq8ajT62mTy0UXV2Zwc2UyGgcJyyZZr0k9XMeKajjb2EzpdBszZgZjFp5mzExhFBrNIAdFdFQaz3NUxsqN68fR2hofgyAVt474GMSpj6MjHvb2eDrYX7aRmpNBn1otum+DwrztffIBIaVQ0Gm43q1QmuzMjYW1hQXWlhZYW1hgZWFhvG0lNWwtpBc8ZiEZn1gVENDodKi1OjR6vbjV6VAbLsbHjLf1KLXaCU00Gg47K0vc7OxE99iQDyreth0qLh0dcHewnzL5hFq9ng65nLY+Oa3yvkGLGhnNff0pAjJa++QmfZYSxG4X/RGI/oiEcSHo5DTl87jNmBkPZuFpxsw1TH8o/3xh2iyT0dInnlCbZbIx8ykHY2dliae9A+72dng6OOBpEKSiMB1628nGZsoIDp1eT7dSJYaOzwsji06gOO/c6AaqDe6gSo3M4A6O1wm8UkgAe2vRoe1vOu943u1+h9fFznZAXNr1pybYYXuJm+5PJkqNhg6FknZ5H219ctoMwrJdLr/gvi6FwuQFiIVEgqeDg3GB1h9B8Dekuvg7OePj5DjlUgTMmLmUmIWnGTNmkKnVRkewub/oo08MITb3ymjt66NN3mdSz8vBWBj6X7rZ2eFuN+CIuRmuD1zE+5xtbHG2tblqQ9VjodZqkak1KDSaYR1HtU6HWqsd1qGciBN5voNqbWEpXrcccFMHtlJsLC1xtLHGzspqyiwIBiMIAnKNxrhA6FIq6VSIHQa6lP0OtoIOuXLI7fHut1KJBHc7Ozwd7Ie4/z6OToMiAg542Ntf8ib4Zsxca4xHr02d5a0ZM2bGhaO1NY7u7oS7u4/6vD612ugUtfX1GV2iNrmc9vNu96nV6AzFFR1yBeXjeD8OVlY429ribGODs63NwPX+2waB6mxjY3TnHK2H5mFeCWFlbWmJu6UlMPV7RE42giCg1umQ9TvEajV9moHc0m6lih6Vil6V0ni9R6miR6Ucsh1v9X8/llIpHvZ2eNg74OlgP8iZH3rb094BVztbs6A0Y+YqwCw8zZj5jtMv6kLcXMd8rkqrFR2p/lzKQde7lIY+mINcq065wtjvtE+joU+jobF35ObzY2FvZTUkZOxoeO/2/e2GLA0tiAxbsfWQFXb9Dd8tLY3N360HuYc2FkNzNKe6QBEEQXRgdYMcWK0OjV7cqnSGNlDagYb0Sq0WhdbQCspwv0ortodSaDTI+guUNEPTEi6mkf1gLKVSXG0H8krdbAcVg9ka3HV7W1xt7YzpAY7Wl74fqRkzZiYXs/A0Y8aMydhYWooVy46OJv+MVq83ulu9KtUg50t0u7pVSnoN93UrlfSoVEOrsA0uK4Bco0Gu0dA68cFDJmEhkQwUDhnEqJWFFAuJFAupFAuJBKlUgqVUilQiMdwvMT7Wvx2vKBIAvV6PThDQGbZ6vYBWr0cvDH+/TtAPEZdqvd7ktlSTiZ2VJQ5WhtxSQ06pi40NLra2OBm2zrY2A9eNTrd429bS0iwizZj5DmAWnmbMmLmkWEqluBt6a04EQRBQabX0qTXGcK5skCiVqdUG926wg6dBYXDrlIO2/W6eUqMdUil+fgGRztCiSMH48givVqQSyRABbWspusP9LrG4HTrus981trMUm9YPaTM1KP3B0eA4T3WX2IwZM5cHs/A0Y8bMVY1EIsHWygpbKys8HOwvye/QC8J5BUNaY+GQZpBA1QkCOkEvuo2Greg+6tEatkY3UtAzkdpNC4OLOuCmGhzUfqfVeHvAaTUWIPW3crIYWog0VQu7zJgxc+1hFp5mzJj5ziOVSLCxtJxy06DMmDFjZqoxJY6yco0Gy3H0JDRjxowZM2bMmDFzeRhP3+gpITznvv8WUlvbK/02zJgxY8aMGTNmzJyHXqk0+bnmxB8zZsyYMWPGjBkzl4UpMbmosa3NPLnIjBkzZsyYMWPmKqSnpwc/T89rZ3KRvZUV9lZWV/ptmDFjxowZM2bMmDkP7Tg0mjnUbsaMGTNmzJgxY+ayMCUcTzNmzJgxFb0giI3ktYPGQA7ZiteNDeT1A306VYNvG7ZqnQ61XuzvqRP06AdNDzJOERLOmy5keGwiSA29OodMRRo0DUlq2FpKpOL0JMnAqE9r6aARoMapS1KspZZDenzaWFiIjeMNjeTt+hvGD9paWVhM8n/GjBkzZszC04wZM1cYQRCQazV0q8Rxmb1qFTK1GplhJrhMMzAfXKbRDLo+cL98kKhU6a6NaUNXGkupFFsLS6M4dbAyTCuyssbRMBrTwcoaJ8PWwUqcYtT/uKO1tTgO08YWJ2sbcxN7M2bMAGbhacaMmUlCpdPSpVTSoVTQOejSrRLntPeoVPSoxW23SkmPWmV8THeJahytDc6eXf+ISEvDOEgLsVm8tcEZHOoQGpxB6aApQFJx+o/lebPYpZJBE4QkEiyk4n39buV4J48LiEJ8sJuqM05I0qPr3w5yWXWCfsCZPc+xVQ2avDT4MZVOh0KrMVzEEaNKnSjc9Yb/hVavR6YXBf5k4GBlhYuNLc7WNjjbGGa1G6672NjgbG2Lq60t7rZ2uNra4W5rh5utHU7W1uYZ7mbMXEOYhacZM2YuQBAEZBo1bXI57Qo5bYo+WuVyOpRyOpXKIcKyQ6mgS6mg7yKHPFhKpbhY2+BkYzOsg+ZgbTXITbMxPC7eZ29ldUHo2MbC0jw/fJwIgoBarxuYb28Qo/IRnOb+6zK1mj7Dc8TranrVonvdv1+Ij2tooHdc78lSKsXVxhY3gxB1GyRK3e3s8LCzx8POHk87ezztHXC3tTO7q2bMXMWYhacZM98hlFoNLfI+mmQymuUy2uSiqDRuFXLxIpdPKGRtIZHgZmtncK7scbW1xWWQo9UfehWdLtHxcjG4X3aWVmZn6wojkUiwsRBFu4vN5Azt0Oh09KpVBoe73/kedN3ghHerVHQpFYaFjbjAkWs1aPV6435p0t8AuNva4WnvMESQilt7vO0d8XFwxMfeATdbO/M+Z8bMZcYsPM2YuQbQGU7OzX0ymvpkNPdf5IOu98noUpk+XQLA3tIKT3t7PO0cDM6SHe629rjZnudA2fWHRW2QTqETuVqnE106g3PXq1GJbp5maI6pTCPmkWoMhUZqnRaNXo9KN3B94D6toRhJh57xpxBYSYaG+K3PC/0b75NaYm0h5mEaHWIr6yGucP/t/vvsr4C4t7KwwN3OHnc7+3H/rFKrGeKw97vrHQaB2qHod+TltMn76FAqEIB2pYJ2pWLM17eWWuDt4ICvgxM+Dg74ODjhYy/eHny/vZX1BP5yM2bMDIdZeJoxc5UjCAI9ahX1vT00ynppkPVQL+ulYdDtpj6ZyXmSNhaW+DqIro/XIFHZLzA9jdftp8wJVy8I9KpVdKr6RYmcDqWCDlW/YJHTpRLzTzuUcjpVCmRqNWq97kq/9cuKBHC0ssbFELp2t7EXFw82AyFsd8NtcYEhXre+QhXutpZW+Dla4efoZNLztXo9nUqFUYgO2RquN8v7aOmT0aFUoNbrqOvtoa63Z9TXdbGxxc/RiQBHJ/wdnfF3csLfwbB1dMbHwdEc3jdjxkSmxOQiUzrhmzEzVREEgVaFnNqebup6uqnt7aa+t4d62YCwNCV/0kIiwcveAR8HR7ztHY3isv/Sf9vZ2mbKhBcFQaBLpaRJ3kuz3ODmymW0yGVD7utQyi+qQMnGwnKoW2g1tDrbweAWWltYDi08GuRC2gxxKC2xkkqxkIxPjAiIBUNqnQ6VflBhkF6LRqdHZdiq9VpjsZBCqx3IuzS4szK16oL7LuZA72Rljbe9Yb+yd8TH3lF0BQfd9rZ3vGICdSKodFpa+vrEfalvaGSg/9LUJ0OuHfu7J5VI8LF3NApRf0cnAp1cCHJ2IdjZhQAnZ2wszD6PmWuX8eg1s/A0Y+Yy0KNSUdvbLYrL3m5qerqoNYjMut4elNqx8yk9bO3wcxxwWQIcnfBzHDjRedk7TLliGqVWS51M/Bxqe7uplXVRL+sRUwQMAlOlM92VdLCyws3GfqD4xODkiQ6eHW624mOuNrY4WdngZG2DvZUVVtKpI5gmQn/Lqn5R2q1WieFrlcEB7g9f9zvEhm2nSmGscjcFD1t7fAxC1M/BIL4cXQhyEi8etvZTZtHTT69aZVgAiovAhl7D1nC7UdaLZoyerRLAx8FR/BycXYyiNMhZ/Fx8HRyn3HfXjJnBmIWnGTOXGUEQ6FAqqOruorq7k6ruTvF6TxdV3V10j5FbKZVI8HNwJNBwIgpwcibAydkoKv0dnbC1nHpjY7V6PY19vdT2dlE7WGAaRGaLvM+k13G3tcPXXnTY+nPwfAxum7e9I152YqGI2VWaXPSCQI9aSbtCTouij6a+XuOi4HwH2pS0BXtLK6MIDXJ0HbhuEKiO1jaX4a+aXPSCQJu8b0CYynrF8L1hYVnb0z2ma2ollRLo5EKIiyuhLq6EurgR4ixuA52czc38zVz1mIWnGTOXAEEQaFfIqeruMgrLqp5OqrtFcdmrVo368x62dqKwNIjL/m2wsyt+jk5TKkx5Pt0qJeXdHZR3tVPR3UF5dwdlXe3U9HaN6QY5WFkNESGBjqID5GPvZAjhOlxWQSkIAr0alZgTqpIbXcHu/2fvvMPbqs8v/pFsyfKWZVvee8ZxbGfZ2YsMVpgFApRRCrSlQCmjdP4KdJdVWmgLhULZO0ASIJPsPWwndrz3kG1ZkmVrS/f+/pCj2Il3nJCAz/P4sazlK+nq3vM973nPa7e6My9dJ2KG3DmYfS9bXE731CSXozcTc/TTi3y8eiOhvHpzR716c0i9+kRG9V729fbGz1veq+z6uX/7+KH0UZxTFVcURfQ2i4eItpl7aDEZ+yw0DLSZe4Yt94f7+pMcHEJKcCgpShXJwSpSg0OJCQi6YBXBE4vSxj5EtLG7v61mqO+Il0RyGilNDFaSEBRCfFDwBCmdwHmBCeI5gQmcARwuF/VGA9UGHTUGHdV6HdW9v43DkMvogEC3WhGsJDFISaLSrVzEBQXjf4E06gwGlyDQ3GOkqqvT/d506ajuvay1Dh51I5d6ERMQNKjSFeJz9iNtRFFEazXTYjLSajbSYjL2Nh+ZMdh6S8s29996qwXnGAjj+YZAmU9vo9AJQupuGFLKfd1+RL9Aov2DifQPRHEOiL3N5aS5x+ghom7Vu4uG3stDJS74eHmRGHQqIXX/vhBV0r5wCQIaUw8NvdWRuq4Ti1k9dUbDkDYcb6mU+CAlycoQUpQqUkJU7t9KFUqF7zl8FRP4tmOCeE5gAiNAl81Klb7TQyqrDTpq9DoaurtwDqJASOhLLkNIClaS0KtAxAcFX5Dl8IFgsFko03VwXNfBcV07ZboOyvVarENke0b6BZCiDCU5WEVKsIrU3stR/oFnPWLJ5LDTajbSbHKTylbTycstZiOtpu5Rd7D7esk8Hd4hPr4EyRX4ecv6qZEnLvt6lMkTt7snI8kko1OjRET3VCFXr5raq6IOpa52O9zd/Cd+DDbLqBuJwhR+7i5t/yCi/YJOXu79O8zX/6x/ht12G7W9arlbPXcvbOqM+iF9vrEBwUxShTNJFU6mSs0kVTgJgcoLViHtC1EUaTP1UGc0UN9loLZL32vlcRNTyxCkNFThS0pI6CmkNJTYwAtXPZ7A+YsJ4jmBCfTBCYJZoeukQqelUu/+PZS/0M9bRkqIiuRe9eDE5aRg5TeGXILbg1nTpaNM10GZvoPSznbK9B20mgaeLuPj5UVSkMqjOp0gmElBIWddeXIKAk09XVQbO6nu6qTGqPNc1tmGz2yUAGrfAA+hClX4uVVAH7+T8UE+J4mm7wX6ObsEgS67FV0vCT2h4rpVXTOdVjPtlh6aTW5CbnEN37Utl3qREBhCSrCKlKBQkoNUpAS7fwfJxydofqjXc0Jpr+lDSGu69HRYBv4O+3rLyAgJIzMknEmhaiaFhJOpCh+3UPzzAaIoojH19C6cO6kx6D1VmpaewadD+Xh5kxqiIl0VRlpIKOmqMNJVocQGBl9QGbwTOL8wQTwn8K1Et91Gha6TSp2WCr37d6W+E42pZ9DHRPkHknqCYPaWqZKVKiL9Ay647tvh4BBclOu0FGtbKerQcKyzjUqDdlA16etSkox2q5tUdnVSbdRRY+ykuktHXbduSC9coMznNKUu2j/Q83eE74Xtoz0bEEURg91Ks6mrVynupqVXLT5hSWiz9AzZ2a72DfAQ0ZQgFclBoaQEq4jxP/tExmCzcFzX0avOt3O8V5kfbOpWjH8Qk0LDyQmLIjc8kpywSFSK0Qfbn+8wOezU9hJRTzXHoKPGoB/0vfH19iY1JJS0kFDSVKGkh7gJacwEIZ3ACDBBPCfwjYZLEKjrMrhPNtoOyjrdJeHmIUKgo/wD3QfT3gNqWu9qP0B+YfsuB4MgilR36SjuaKVYq6GoQ0Oprn3Ak46ft4yMkHCyQsPJDHETzAxVOEFnWcEURZFWczclujbPT6m+jWbT4J+jj5c3yUGqPkTHTXbiA5VnRXlzCQKdNhNtlh46babebEwbJqcdk/PEVKMTf9t6MzNt9PTe7hqmsepUSCQSFF7eBMh88PeWe357LsvkBJzyd7DMF7VvABG+gWdFpXUILjSm7pOLAM+ioHNQxRHcVoVJKjWTQyKYrIogWxVBmjLsrDeKuQSBOqO+1ybSQZm+neOdHYPuV7EBwR4SmhsexZTQiAveNzoYXIJAY3dXvwV6hU5LtUGHfZAFqL9MRoYqnMzQcCaFhpEZ6r4c+A19jyYwNkwQzwl8Y2CwWjje2UFZp5bjne2UdXZQruscdNWu9vM/pYTkvhzk8809SIqiSFOP0aNkFmlbOaZto8dhP+2+QXIfpoRFkhsWyZSwSCaHqokLVJ51RUMQRWqNOjfB1LdR0ukmmYOVyE9V0U6UdcdLRTvRua6xdNNu6e793UObpZu2Ppc7rCOfCHU+IFDmQ6RvIGrfQCJ8A4noJaRq3wDP9eGK8Zuy01edPml9GFyd9pZISVOGMVkV4SGkWSo1AbKz//3sslkp03VwrLONYq2GYm0rNV360+4nAVKUoeSGRZITHkVOWCRZKjUK729uVJdTEGgwGqjQad2WJL2WSl0nNYbBqwyxgUG9ZDTc8zsh6JvhrZ3A6DFBPCdwweGEif5oRxtHO9o4pm2jVNs+aJlc4e1NhirMc9DLDA0nUxX2rejkdAgujmnb2KdpZL+micKOVjoH6CpXeHmTHRbR7wSaGBRyTspmOquZA+1NHOxo4khHC6X6tgGzDL0kEtKCw8hSuUnIZFUEk0LUBI+TemlzOakxdlLVraXaqHUTI2MnDT36EXkbwZ2xGubjT5jCn8BeldHfu1eJ7FUgPcqkR6l0q5KjjTQSRBGLy+FWTnsVVLeyavMorCfU1RPqq8Fupc0yMq8muIlVpF8QSYEqUgLDSA0KJSUojNSgMMIU/uNiMXEKAnXdun5q9jFdG132gTvXEwNDyAmNYoY6hpnqODKU4edkP+2yWTna2UZxRytFWg3FHa20DOBv9pZImRQazgx1LPmRsRRExhE6htnzFxocLhd1XXqO91n4l3VqB/WAnzguTw6LYEq4+yddFTZhcfkWYIJ4TuC8xglT/NGONo51aDxkU2sZOJInLjC4X5lnUqia+KDgb83K2up0UtTR6iGah9qbTyNxMqmUTFU4uWFRbkUzPJI0Zdg5mx/dbOriQFsT+9sb2d/eSFVX52n3UXh5kxmiJrsPyUxXho9LlE+3w0a1UUtVl9ZDMquMnTT26Ifs8A6WK1ArAt3h9IqAfspgZK9SGDaOCuHZgiiK9DhstFl7aDN3e5Tbtl7l9oSqO5yCGyRTkBp0koymBIWRFhxGtN+ZK82iKNJsMnpU79JeQtpqPp3EBMsVzAiPYWZEHDPVcUxRRZ4z8tJhMVHcofFUEIq1mgEXdqnKUAp6SWhBZByR/iObJ/9NgMFqoUyn9VidynQdlOu0A0Y/yaVeZISGkR0eQU545AQZ/YZignhO4LxCm6mHonbNsCRTKpGQFhJKdu9KOTssgozQsG+dl8jssHOovaWXaDZS2NF6WgOQ0kdBfkQs+ZFxzIiIYdI5LAWKoki1sZP9bU0c6CWaA/nn0oLDmKmOZXp4DDmhUSQHqcZlsWB22jmm01Cka6Gos5kiXQst5sF9oUEyBSlBoaT1EqmUoFASAlRE+QVdsJ3rY8UJz2qjqYvaPkpwVZeWRpNhUJLu6yVjckgkuapockPdPzF+weOijnZazZTo2jiibeZgexOHOk5fWCm8vJkaFs3MiDjy1XFMC4vG7xzl4oqiSHOPkSMdLezXNLFP00i5Xnva/eIDlf2IaFzg+Lw/FwpcgkC90UCptr1XVHAf6wfKPu5LRqf0EtIMVdhEGP4FjAniOYGvDT12O8UdGoraWylsc/8eqFzudSrJDI8kKzQcX9m3iwiAuyxZrNWwo7mOXS11HG5vOc1XFe7rT0Gkm2jOiowjLSTsnHWaiqJIjVHH9pZa9rY1cLC9iU5b/4WDl0RCtiqSmepYZqrjmKmOHZduYZcgUGnsoKizhUJdC8WdLVQYOwbsslYrAnpLxidLx+NZPra7nHQ5rBjtFowOK10OC0a7FaPDQpfd0nub+2+jw4rRYcUxyuxQCRAg8yFI5kuQTEGQXEGw57Ivwb2/g2S918sVBHj7jMvrszod1HbrqOp2E9ETqnFdT+eAPr9QH39yQ6PIVcWQGxpNjiqKYPmZW10cgotSXbtnUXOgvQn9KV5gb4mUyaoICiLimBuVSIE67pzGnOmtFg60NbG3tZH9bY2UdLaftk/GBgQzLzqBeTEJzI1O+EZ2zw8HURRp7O46aaHq/T3QCGEfL28mh6nJi4giVx3J1Iiobx15v5AxQTwncE7gFATKdVoK21p7iWYrlfrO01QTqURCekgoU9SRHiUzKyz8G5WHORqIvR3nO1vq2dVcx57WBrpPaQSK8Q+iICrO4ydLCgo5pwfgbruNXZo6trfUsq2l5jRF06dXgcrvJZrTwmPGZTKT1trDgY7GXjWzhWP61gG9oZG+geSooskLjSZHFU1WSMQZkx6XKKCxGGky6Wk06d2/zQbP33r74NOZvk7IpF7E+imJ9Q8hzj+EOL8Qz+VY/xD8vc/sc3EKArXdnRTrWj0Kc5mhfcDpTkmBql4iGsXU0FiylBFnrHK7Exo6PST0wAAKu4+XNwURcSyMTmZhdBIpQaHn/PtyqL2Zfa2N7NM0UdTRetr7MzlUzbzoROZFJzAzMvZbp7afgCiKNHV3UdyHjBa3awZURlUKX3LVkeSqozyENORb4OO/EDFBPCdwVtBm6uGQppnDbS0UtWk4qm0b0NMTExDYe5CIIk8dRXa4+pyVxc5XtJt72NVSz66Wena21J9mzg+WK5jbq47Mi04gPlB5Tk+cgihSomtje0sN21pqONzR0u/EKZd6MVMd61GXpoRGjYtHy+Swc0DbwC5NLbva6ijvaj/tPv7ecqaook6WeVXRRPqN7XggiiLNZgPHuzQ09iWYJj0tZgOOYUZlSsCjPgbJFATJfAmWKzyXPQql3P23fLTNRbi9msYTCmqvqtpXaXUrrO7fI5nGpJL7EesfQry/ilh/JXH+ISQHhpMZHIHCa2zkx+p0UGpo8ywQinQtNPSc3iEeLFcwW53I3IhE5kYmE+8/Pvt1U08XB9ob2dvWwPaW2tN8ojH+QSyISmJhTDJzIhPOesj9qTA57OzXNLGzpY6dzfWU6Tv63S6XejE9Iob5MW4imh165gT9QoYoitR1GShsd4sYR9paOa7tGHD/TghSkhcRxdSIKKZHRpOpCp8o0Z8HmCCeEzhjOAWB453tHNa0cEjTwqG2lgFzMgPlcg/BzFVHkhsRhdrP/2vY4vMLDsHFwbZmtjRWs62p9jRPmI+XFzMiYpkXncD8mESyVOpzfuLRWkzsaK1lW0stO1pqTyufJwepPCfvAnXcuCwenIJAsa6FXW217G6r40hn02ll3MxgNVPDYshTxZATGk1KYOiY3htRFGkyGygxtFCib6XE0EqpoZUux+BTjmQSKTEn1EI/pUc1jPMPIco3mECZ4rwK07a6HGitPTSZDf1IdJPZ/dtgH/y1ekkkpASGM1kZzWRlFFnKKDKDI8esxOlsZop7iWhhZzOHO5vpcfRXsWL9g5kTkcTciCRmqxMIVZz5sUIURSq7tB51fl9bYz/C4iWRMC08xrMvZ6siz/ln2G7uYXdrAzub69nVUnda53yQ3Ie50QksjktmcWwyar+Ac7p95yNsLifHtR3ualq7hqK2geOvFN7e5KojmR4Rw/TIaKZFRk+ool8DJojnBEYNvdXCkbZekqlpoai99bQ5wFKJhMzQcKZFuIlmXkQUyUrVeXUi/jrRYTGxtbGGLY017Giu7Vc+lwDZYRGeUtuMiJivxWpQa9SxvrGCLxsqKNS29LvN31vOnMgEFsYkszAqibhA5Rn/P1EUqe7uZJemlt1tteztaDiNjMT4BTM3Mol5EUnMGiMZEUWRRpOeEkOrm2gaWjluaKXLcbqXTCaRkhYcQXJAmEcBjO0tUat9A/GSfHOUp26HlSaToR8ZbTDpKO9qo9N2evi7l0RCcmA42eNARp2CwFFdK7vaatnVVjvgIiNLGdFLRBOZGR4/LuVni9PhUUK3tdRQY9T1uz1M4cey2DRWxGcwJzLhnHdXi6JIrVHPzuZ6dra4rTanlpmnhEWwJC6FJXEp5ISde6J8vsJgtVDc0UZhWyuH21o4rGkZsESfrFS5SWhENNMjo0kNCZ14D88yJojnBIaEKIo0GLvY39rEgdYmDmlaqDboTrtfkNynt5zhXknmqqO+sZN+xgJBFDmm1bClsYYtjdUUaTX9bg9V+LEoNonFccnMi078WlbhoihyXN/Ol40VrG+ooNzQv+Q3WRXBwuhkFkQnMS0sZlxOwg7Bxf6OBjY0lbOpuQKNpb+6o5T7Mlud4CYckUljKr8KokhZl4Y97TXs7ailWN+McSCSKfUiPUjNZGWUW9kLiSYtSD3qEvipEEURq8uBzm7CYDdjsJvR203oe3+7rzONqBTeFxIJBMl8Ucr9CJH7o5T7oer9rZT5EeLjT5DM94zJsSiKtFm7KdG39JL1VkoNLWgHIKNSJKQGhTMzLIHZ4cnkhycSKBt96fqErWK3po5dbbWUnWKrkEu9KFAnsDwmg2Ux6YT7jo/q19hjYHtLLdtbatmlqes3WCFQ5sOSmBQujk9nYXTy12IJcgoCR7UatjXVDngcCVP4sSgumSVxycyPSTrrE8UuJAiiSI1B1yuYNA97LpsZFcvMqBhy1VHf6IEAXwcmiOcE+kEQRcp1Wg60NrG/tYn9LU20m08/wZxYJU7vXSWmTKwST0O33cb25jq+aqxma1PtaSMDzwelQhBFjnQ082VjBV82lNPY0+W5zVsiZXZkPCviM1gemzZuJT2r08HOtlrWN5WzuaWiX1C4XOrFzPB45kYkMiciacwNJ80mA7vaq9nTUcvejprTysgyqRcZQREepe5MSKYoimisXdT2dFDT00FtTwfNZn0/kmkTBp6edbYhRUKw3BelzA+l3J8wRQBJ/uEkBbh/EgPCxuTdFEWRdmv3SdW4156gtfVPpfCSSMhWxjBbncQcdQq5qtgxvcdaaw+72+rZ3VbLzrZaWvtEYkmAaWGxrIjNZHlMBnEBylE//0BwCC72tTXyZUM56xsr+n1/fby8WRCVxMXx6VwUm4rS5+sp1w5VOfGWSJkRGcPi2GSWxKWQpjy3TVQXAk5U7w5rWjmkaaZwgOqdXOpFrjrSQ0SnR8Z8o6fbnQtMEM9vOewuF8c62jyK5kFNy2nxFTKplBx1JPlRscyIjJnwxQyBlh4jGxuq2FhfxV5NQ79yob9MxvyYJJbEJbMoNpmIr8mb5RBc7NU08GVjBRsGOKEujE7i4vgMLopJJdhnfBotuh02vmqpYkNTGds01f26z1U+fiyNSWd5TAaz1QljshXobWb2a+vY017Dno4aGkz9/V1+3nLywxKZHZ7E9LCEMZFMh+Ci0dTZj2DWmbTU9mixuE4fOXoq5FJvQuR+/RTKELl/73X++EhHp6oIoojRYcHgMKO3mTA4zOh6f+ttJrqdA0/+6QsJEqJ9lSQGhJEccJKQJgeEEywffaRPu6WbQl0Teztq2NNRS11P/+EAvl4yZoQlMCc8mdnqZNKC1KNecImiSG23jo3NFWxoKqNQ198GkqWMYHlsBitiM0kLChsXsiWIIke0LaxvKOfLhgoaegye27wlUmZFxLMiPp0Vcelfm+fS7nJxsK3JU1Wp7uqv5sUHKlkWn8qKhDSmR8Sc94MOvg44BYGyzg4Oapo50NrM/tYmOk4RXqQSCZNCw5kZFUtBVCwzomIIn+hVGBUmiOe3DGaHnSNtrR4180h762nd5n7eMqZHRpMfHcvMqFjy1JHf2jij4SCKIsd1HWxsqGRDfRXHOtv63Z4cHMLi2BSWxKeQHxH7tU3gsLtc7NLUsa6+jI2Nlf1UxkCZDxfFpnJxfDoLopLGrYSotZrY3FzB+uZy9rTV9SslR/kFsSLGTQ6mh8WOWtV0CgKHdQ3s0FSxp6OGUkNrv2guL4mE3JBYZquTmaNOZkpIzKhGUtpcDkq7WijUN3DM0ERNdzuNZt2AsUDgJh/x/qEe4hbnp0LlE0BIH3Lp6yU/p4qTQ3DRZTe7iWiv8tpm6fKQ5tqejiGbp1Ryf5ICwskIiiJPFU9eSDxqxeiOrS3mLvZ21LC7vYY97TXoTomZCvXxZ1Z4EnPUySyMTCfUZ/Qn8FazkY3N5axvKmd/R0O/jMykQBXLYzJYEZtBjip6XN7/E5aU9b2WlLI+lhQJMD08lksTMrg0IZNIv69vQlGD0cDmxmq+aqxhr6ah32CJEB9fLopPYVl8KgtiEr/1SSKDQRRF6o0Gz/nyQGsz9UbDafdLDg5hZlQs+dGx5EfFEhsYNKEuD4EJ4vkNh9Fm86iZ+1qaOKZtw3mKaV+l8GVGVAz5Ue4vTVaYemI1PARcgsA+TSMbG6rYUF9FU5/ytASYHhHDsvhUliekkRys+tq20yG42NnqJpsbGiv6GevDFH4si0vn4vh0ZkeMX9OE1trDl41lfN54nAPaxn4kIDkwlBWxGSyPzWBKSNSoD8xmp52dbVVsbi1nm6byNNKUGhjObHUys8OTmRmWQIBs5OUwvd3EEV09hfoGCnUNHDe2DBjo7uclJzEgnOSAMA/JTAoIJ9ZPNepZ6183RFFEbzdR09NBXY+2HyHVWLsGfEyUr5K8kHhyQ+KZqoonLTAC6Qg9pIIoUmlsZ097Dbs7ajiore83M14C5KniWBKVwUVRmSQFho76NelsZrY0V7K+uYydmtp+i51I30Aujs3k0vgspoXGjBsxqDPqe5vwyjlyShPe9PAYLkvI/NpJqMlhZ3tzHRvrK9ncWI2hT1XLx8ubedEJLEtIZVl8KmG+E+rdUGgz9Zy0orU2U97ZcVoedZR/IDOjYsiPdquiqSETNoe+mCCe3zC4BIGidg07murY3lhHYVvrafOWowMCyY9yq5kF0bGkKFUTX4oRoLSznY+qjvFp9fF+5WmFlzfzYxJZlpDKRXEpX/uBu75bz7uVRXxYc7Tfdob7+nNJvFuJmRk+epVxMAiiyE5NDe/VFLKpuaKfKpgdEukpe6YGhY36uV2iwN72Wj5tLGZzy3HMfYhKsMyXBZFpzFOnMEudhFox8hO72WnjkK6e/dpq9nXWUGHUnHYfldyfvJB4ckLiSQ+KJDkgnAjFmSsZdsFJp62bTls3WpvRfdnejX2UPlAJEoJlfoT5BBHqE0iYTyChPoEEevue8TaanTZqe7TU9LRzzNBMob6eSmMbwimnWKXMjxmhScwKSyE/LJk4v5EfS+yCiyJdE7vbq9neVkWpobXf7dnKaFbGTeGyuCljUkJ7HDa2tlazoamcra1VmJwn7RBJgSpWJU/l6sQp4xLTdAIaczdf1Jezrr6Mgx1NnuulEgmLo5NZlZbH4piUr3Vh7xQEDrY1saG+io0NVTR0Gzy3eUkkLIhJ4prUySxPSJ2odI0AXTYrhzTN7OtVRIs7NKeJO1H+gcyPS2B+XCLzYhO+9Va1CeL5DUBrTzfbG+vY3ljLzqaG0zyaScEhFPSWAPKjY4kNDP6atvTCg8bUzSfVpayuKu0X7Kz0UbAsPo3lCanMj0n82ieL2FxONjZW8k5lIbs09Z7rwxR+XNqruIwn2QRos3TzYW0R79cU0mQ6qZDlqKK4LC6Li+MyifVXjvp5RVHkeJeGNY3FrG081q9hJc4/hKVRmSyOymCqKm5UJ/Dang42th5jd0cVxwxNp5XNUwLUTFUlkBsSR15IArF+o58A5RRc1JraqDd1oD2VXNq60dq7MTrO7lQjudSbUHlgHzIa5CGlkYoQ0gOjCJaPhchZOdbVTKGuniJ9I0X6BsyneFujfIPJD01hcWQms8NS8RlF41KruYuvNBVsaS1nX0et5/PxkkiYo07hyrgclkRljum7ZnM52aGp4YvG42xsrvCQUJlUyvKYTFalTGWWOmFcG/wGI6ERvgFcn5rD9ak549YINVaIokiFXsuGhio21FdS3KdLPkAm55LEdK5OnczsqPiJ5tERwuywU9imYV9rI/tbmjjc1orNdXJRKQFy1JEsiEtkQVwiUyOiv3UVxgnieQHC6nSyv7WJ7Y21bGuoo1Lf38AfKPdhXmw8C+KSmB+XMEE0R4keu40v6yv5uKqE3S31Ho1HLvViaXwq16RmsTA2+Wvza/ZFjVHHO5WFfFR9FF3vjGoJMD86iZvS8rgoNnVcS8AuQWBHWw3vVh9hS0ulR00Pkim4KjGbVclTyVCqx/TcreYu1jYd5bOGYqq6+5B8uS+XxEzmivhcckNGVyKt6+lgQ+sxNraWUNnd338b7aukICyFgrBk8kOTUfmMrinE5nJQ3aOh3NhCeXczFcYWqns0I1IuvSVeHjIY6hNImDwIhdfofHYu0YXBYfIQ3E6bcUQNRQCRCiXpQdFkBMaQERRDRlA0YT6jO246BBfHDE3s76xhv7aGIn0jTvFkaTvA24eFEZksj8pmdlgqcq+RN0/pbCa+aCrhs8ZiivXNnuv9vOUsj57EyrgcCsITxxQV1eOwsbahlPdqjlCsO6myJgSEcEPyVK5NyiFsHFVQgOquTt6rKuLDU76n86KSuCktl6VxaeeFVaOmS8fHVSWsrirtZyGK8g/kqpQsrkmdTHrI6CsX32ZYnQ72tzazvbGWHY31lOv6DwgJlPswNyaehfFJLIhLJCbwm81fYIJ4XhAQRZFqg45tDW5Vc29LU78VlFQiIdezgkoiVx35rVtBnSmcgsCO5jo+riphQ30l1j7vb35ELFenZnFZUua4dXmfCZyCwOamKt4oP8xOTZ3n+gjfAG5IzeG6s6CktJqNHnWzpU+UzYywOFalTOWS2MwxleUEUWRXezVvVe9ne1tlP5K/JCqDlXE5zItIHVUHen2PtpdsHqOiD9n0lkgpCEthceQkZoWlEOs3cv+t2WmjsruViu5mN9E0NlNrasc1QLORv7cPKQGRhPsE9yuBh/kEESp3/x0k8zsr9hary4GuV1nttBlPU10bzZ00WzoHfGyoPJCMoGjSg2LICIwmIyiGSMXIc1MtTjtH9PXsbK9kk6aEduvJ/STA24dFEZksj5rCrLCUUZHQ2u5O1jQWs6axmCazwXN9tF8wqxJncG3iVFRjKMUDlOg1vFd9hE/rj9HTRwVdGp3OqpSpzIlIGlel70Rl4t3KotO+uzem5XFTWt55MYlIEEUOtjWxuqqUtbVl/fzhWSo116RO5sqUSefFtl5o0PR0s73XCrerqQG9tb9XPUWp8pDQWdGx30i7wwTxPE9htNnY3VzvIZvNPf2DtSP9A1gQl8jC+CTmxsSj/JZ7RsYCURQ51tnG6qoSPqsp6+eHTA4O4eqUyVyVmkX8OEzlGQ90Ws28W1nE25VHaDa5T+oSYHFMCjel5bFonL1joihySNvEfyv2sbG5wtMopJT7cnXiFG5IziMtOHxMz91lt7C6vpB3ag/SYDoZ+zIjNIEr4nNYEZ01qpnZHrKpKenn1zxBNpdFZbM4InPEEUF2wUmhvpZdHWXs76yk3tSBeFoLAShl/qeRtWjfkBE33Hwd6HFYqeg+qdKWG5upN3Wc5t8ECJL5katMYHZYJnPDM1ErRlY9EUSBIn0jG1qPsam1hA7byeNXgLeil4RmMzs8BdkIY6REUeSIrpHPGov5sqnEM2lKLvXi4pjJ3JycT44qZkTPdSrMTjvrGkp5t/pIv3im+IAQbk+byXeScvEf587v+m4971UV835VEVqr237hLZGyIj6dWzOmka+OOy+891anky2N1ayuKuGrphpPRJxUImF+dCJXp2axIiFtojN+DHAJAkc72tjeWMe2xlqOtLX2a8iUe3lREB3HwrhEFsYlfmOalCaI53kCURQp02nZUl/D1voaDre19GsKkku9yI+OZWGvLyRdNT75dN9GtJq6+biqhI+rSqgynFR/VApfrkiexNWpk8kNizwv3l+xNz/wjfLDrKsv83Tphvj4ckNqLjen5427uukUBL5sKuO/5fso6nMSLgiPZ1XKVFbEZuIzCsWqL0oNrbxdc4B1TUc9qnKgzIer4/NYlTRzVJ3MrRYD65qL2Nh6jPIByeZkFkdMGjHZbLd2sUdbzu6OMg7oqk7L5gz3CSIjKIb0wGgygtwkU+0TfMb7iVNwobMbsY4gC7QvpBIJIfJA/L3PfNFpcdqp6ml1E9FeVbemp61f6RwgNSCSOeGZzAnLJFsZP6JS91AkNNBbwaLISVwcNYXZ4SkjJuxWl4Mvmkp4u+YAxwwn99FsZTQ3Js/g0tjsMQXjA5QZ2ni3upBP6o/S3TuyNUimYFXKVG5Nm0GU3/ieX+wuF180lPNG+eF+XtBMZTi3ZEzjqqTJ4056xwq91cLa2jI+rirhcPvJ993PW8bFielcmzqZOdHj65X9NqHLZmVXU31vz0YdLacITtEBgSyMS2JxQjLzYuMvWLI/QTy/RlgcDnY1N7jJZkPNaTtZslLlVjXjEpkVHYev7JsnuZ8rOAQXWxpqeLeiiK1NtZ5VpY+XN8viU7gmdTILYpPOC58VuH1Bn9WV8nr5YY7pTpaLc0OjuDVjGpclTkIxRvI3GLrtVt6vLeK1iv2ecrpc6sVViVP4Xno+6WNUN+0uJ+tbSnm75gCFuj4n1uAIbkyayeVxU/DzHtkBVBAF9mlreK9+H9vbyj0qnZdESkFYMssis1kcOQnlCMimSxQo7Wpkd0cZu7RlVHb376oO8wlkdlgGc8IyyVEmoPIZfRyOS3DRaTfSYTO4f6wGz2WtzUC7zYDe3j2gmjpS+Hn5EOajJLzPT5iPknCFknCfYMJ9QvD3Hr1FxC44qepuZX9nJbs6yijpauy3nUEyP2aFpjMnPJNZYekEy4Z/zwVRoFDfwIaWY2zSlPRrHIv1C+G6+Hyuips2qvD6Yl0zb9cc4PPmY54IrGCZL9cm5LEqeSZx/iGjeNUnYXE6WF1XzH8r9lPb7VblvSVSLovP4o70fLJVUWN63qFQomvjzfLDrK4t6bcwuzYlm1vSp5ESPPqIqbOFOqOe1b1+0Po+nfGxAcHckD6F69KnEOX/9UVIXegQRZEqfSfbGuvY1ljHvpZG7H2yWOVSLwpi4lgSn8yShGQSgpVf38aOEhPE8xyj0WhgS30NW+pr2HPKjuTj5c3c2HgWxyezKD6RuCDl17eh3xDUdul4r+IoH1Ye61dKz4+I5Tvp2VySmHFezTOu79bzZsUR3q8q9oS8y6VerEycxK0Z08kNG/+TXbOpi9cq9vN+TaHH56by8eOW1OnclDp9zI0WLeYu3q89yAd1hz3B4TKJlOUxWdyUPJOpqpGXEo0OC2uaCnm/fh/1ppMq9czQJC6JzmFx5CRCRtCp3e2wsFdbwW5tGXu1FRgcJ/cJCRKygmOZ01tWTg8ceeC4IAo0mjs4bqyjpKuWOpOGDpsBvd04YAn7VHhLvPD1Gt1+6BJdmF224e8I+HkpCPcJJso3lMzABCYFJ5IZGI/fKAipwW7q896V92tkkiIhWxnP7LBM5oVnkhIwfMXAJQoU6hpY33qUL5qLPc/nI/Xm4ugcbkjMJyt45KVznc3ER3VHeLfuIC1md2OMBJgfkcZNyTOZH5E6JiVOEEW+aqnklYr97Gs/mRhREB7PHRkFLIlOG3eFr8tm5YPqYt6sOEJd98kpXPMiE7klYxoXxaaeNz5+URQ53N7Cx1UlfFZz3OMHlUokLIpNYlV6Lkvik8+bRf2FCqvTwZ7mRrY21LKlvobG7v5Zu8lKFUsSklgSn8KMqJjzovl1MEwQz7MMh8vFQU0zX9XXsKWhhip9/zFmMYFBnhXL7Ji4b6SR+FzD6nTweV0F75UXs1fT6Lk+TOHHd9KyuSEj52sNdj8Voiiyt62BV44fYHNTlYemxPoH892MqVyfkoNKMfrxhcOhsLOZV8r38WVTmUcBTg0K4470fK5MyB7zvlisa+blyl1sbinzkK4IRSDXJ03nusTphCtG3pBQYdTwXv0+1jUXYe3N8PT39mFlTB7XJ+STHDh8B70gChzUVbOm+QDb2kpw9Ou8VlAQms6c8Axmh2UQIh/ZtpmcVsqM9ZQa6zhurOO4sZ4e58BTgNwd7MGE9aqPblXSfTnMJxi1IoRgmf+YfKEWlw2trYsOq54OW1c/JVXbq6wOtl1SJCT6R5EVnEhWUCKTghKJ8R2ZhccpuDjW1eBRi2t6TpnY5R/BytiZXBw1FeUIFgQWl50vmot5r35fP9vEFGUsNyQUsCxq8oijmVyiwDZNJW/XHGBXe7Xn+nj/EG5NncU18VPHHH92VNfKqxX7WddQ6ol7SgxQcUdGPlcnjly5HykEUWRHay1vlB/ud2yI8Q/ie5kzuD41Z1Re6LMNi9PBF7UVvFtRxD7NyepGuK+/+9ibPoWk8+jYe6HiRMPxCRHroKa5X3ZogEzO/LgEt4iVkIz6PBvpOUE8zwK6bFa2NdSxqa6Krxpq6e7TEeglkTA9MoYlCW6ymfYNMQufDyjpbOPd8mI+qS7tt+peGJPEqowcLopPOa9W3Q7Bxbq6Ml4+vr9fOX1hdDK3ZkxjUXTyuOZugtvMvrG5glfK93G48+SJYV5EEndkFDA/MnlM6o0oiuxur+E/FTvZp63zXF8QlshNyTNZEpU5YoXGITjZrCnlvbr9HNGfVJhSAtTckFjAZTG5+HsPrw5qLAbWtRxkbfNBNFaD5/pEfzVze32KOcoEvIfZJ0RRpMnSQWmXm2SWGOuoN2lOK4/7SGWkB8aTFZRAemAcEQoV4T5KlPKAr7XZyOK0ecr79WYNx7vchLndpj/tvsEyfyYFJZAVlERWUCLpQXEjUmJbLXr2aMvZ1VHGQV2VJ1LKW+LFAnUWK2NmMjM0dVhPqCiKFOkbea9+HxtbSzwe0xC5H1fHTec78flE+ylH/Npruzt5t/YAqxsKPX5NldyPW1ILuDFpJsHysfljW81G3qg8yDvVRzD2Njkp5b7c2OsDVfuOf4m5scfAWxWFvFdVhL43kilAJmdVai63Z84gNuD8is2r6dLxXnkxH1WV9Ks2zYqM44aMHC5NTJ8QWsYJRpuNHU11bKmvYVtDLVpL/6zgPHUUSxNTWJqYQsZ50B8yQTzHCY1GA5vqathUV8W+1qZ+qw+VwpdF8W5D8IK4xPMikuebAqPdxmfVpbxbUcxR7UnyFhsQxPXpOVyXlk10wPmjgIN7YfJOVSGvlR1CY3b7ehVe3lybMoU7MmecFR+XzeVkdd1R/lO2l7oet+ouk0q5Ij6bOzLyyVRGjOl5XaLAhubj/KdiJ8e73EqVt0TKyrgcvpc2m7SgkWd6tlmNfFR/gI8bD3q8f94SKYsjJ3FDQgHTVYnDHjDtgpMd7aWsaT7A/s4qDzkM8FawPCqPK2JmkhE0fPnW5rJzSF/Bzo5i9utK6epTkj+BSIWKSUGJZAUlkBWcRLJ/9LAk9nyC1tbVaw1wE+rK7sZ+ajCAFCmTghKYF57DvLAcIn2HV6u6HRY2tBaypvkg5d0nMzgjFMFcFj2Dy2OmEzWC5+m09fBxw0E+bDhAW280kxQJCyIyuD6hgFlhySMm9GanndUNhfy3crenDO/vLeeGpBncljJrzETR5LDzUV0Rr5bvp8FkANzWmKsTp3BX5mySAsdf3bM6HayuLeHl0gNUG922Ey+JhIvjM7grK5+8sOhx/59ngsH89UFyH65MyWJVeg7ZYWM7/kzgdAiiyNEODVvqa/iqvobijv7ViNjAIJYmprIsMYX8qFhkX0NJfoJ4jhGCKFLUrmFTXRWb6qpPC4VNDVF5Ptw8ddS4K1ffZoiiyMG2Zt6tKGZtTZnHhC+TSlmRkMaqjFzmnoedlQ3dBv5bdoD3q4oxO91l4zCFP7dnTuemtLyzUk7vdth4p/owr5bvp93qJnPBcgU3p07nltTpYz7h2l1OPmko4pXK3Z44JF8vGdclTuO21NlE+41cfSnrauX1mp2sbz3mycUM9wnkmvgZXBs/A7Vi+O9zdbeGNc0H+LL1CF19JgNND0lmZexMFqqH73I2O63s1x1nZ0cx+zpLsQonu8zlUhnpgbGeknRWUCKqUQaug5uo6+1dtNs6abfq6LDpaLd10mHVYRNG19UukUhQyYJRK0IJ91Gh9lF5LitG6ReFE81ETRw31vVaCOrpsBn63SctIJa54TnMD8sh3n94slBhbGFN8wHWtxbS7TwRnC5hhiqFlTEzWaDOGraE7hRcbG8v5726fezrrPFcn+Qfxi3Jc7ksJnfEZXiH4OLL5hL+U7GLSmM7ADKpF1fF53JH2hwSA8a26HMJAptaKni57GQlQQJcEjeJuzNnM+UsNCIJosi2lhpeKT3QLxN0Rngsd2bNZFls2nl33mnpMfJB5THeryimqedkzmt2aAQ3ZuRwRUrWeeW5/yag3dTD5nq3KLazqaFfBnig3IdF8UksTUxhUXzSORPFJojnKOASBPa1NLGmqoyNdVX95GwviYSZUbEeOTsxeGydlBMYHCaHnXfLi3mrrJDqrpNe2TRlKDek53Bt2uSzQt7OFGX6dp4r3sX6xpNZmJnKcL6fNZMrErPGHE00FLodNv5TtofXKw96SoyRvoHcmTGL65PzxhzPYhdcvFNzgFcqd9FxgsjKfPluSj43J+cT4jPy979I38A/K7awT3vShzdNlcANCQUsicwa1hbhEJxs1BTxUcNeSo0nvbzhPkFcFj2dy2JmEOs3NJGwuxzs6TzG5rbDHNSV4RBPHpTDfZTMDZvCvLAcsoITR5w36RAcNJhb0Vg70Fi1aCxaN9G0daK16U+LKDobCJIFoPYJRe2jIkIRRmTvT6xfFCr5yBcFbVYde7Ql7NQWc9RQ3a9RKt4vgoXheSyLnEGU79DTbGwuB9vaS1jTfJCDuqo+2+nHxVFTuSF+LtEjCPSv7eng/fr9fNZ0BJPTvV+H+gRwS9JcViUWjDhCSRRFtrVV8nLFLg51NgBuorgiJov7Jy0mKXDs03kOdjTyYtlutrScfJ1zI5J4cMpC8kLHljM6HEp1bbxy/ACf1ZV6cjbjA5T8YHIB16fmnFcWI+gdHNFSz7vlRWyor/LExCm8vLk8OZPvT55BVujYJqBNYHCYHXZ2NjWwqa6KzXXVdPYJr/eWSsmPiuWS5DQuTckg1PfsnUsniOcwEESRI20trKkqY111BR3mkyW3AJmchfFJLOtdLUyEuJ8daC0mXis9zOulRzyd3n7eMlYmZ3JDeg7T1CPvPj6XaOrp4pmiHayuOeY5XS+MTubOSTOZFzV82XgscAkCH9QW8czRbXTa3PtqSmAod0+azRXx2WPudBRFkS2t5fz12EaPwhnpG8TtqbP5TuI0/EfRVNFg6uTvZRvZpCkB3FFIy6Imc1vyPCYFD18mdIkC61uP8Er1ZlosOs9zzA+fxMqYmRSEpQ/pJRRFkfLuBjZoDvBV++F+zTfRvmHMD8thXngOGYHxI/qMBFGg3tRMUVc5RYYySo1V2AXHoPeXIu1tMnIrlGofFeE+KvxGmcfpEl102ro8pLbDpqPd2onZNfTYzBjfCHKVmeQGZ5IdnDbi/2uw97Bbe4yd2mKO6Cv6Eeic4BSWR+azIDwX32H8ty1mHWtbDrKu+RDtNnfZ20si5fKYGdyRfNGIQup7HFY+bjzEW7W7PWX4SEUw92Us5ZKYnFF5ag9pG3i5chdbNRWA295xU/JM7slcOGYPKEC5oZ2XyvawpqHEk8l8RfxkHsldQvQ4Z4GeQLu5h/+VH+LNipPHyuQgFQ/nLeCS+Izz8jips5pZXVXKO+VFVPbJVV4Yk8QPc/KZHTWy7+EERgeXIFDYp2rbd/S2l0TC3NgELk/NYEVS2rgroRPEcwCIosgxbRtrq8pZW1XWb2pQsI+CS5PTuSQlnVnRced1ZMGFjgajgZeOHeD9iqOe8kBSUAh3Zs/gqpQsAs7TkozeZuGFo7t5vfywZyV/WUIm90+ZS0bI2LIwR4LdbXX84chGyrrcJcTEABU/y13MspiMM7IdVHS18aej69nbUQtAmI8/901azFUJeaMaZWmwm3mpcivv1+/HKbqQIuHKuGncmbqQGL/hKwSiKLK1/RgvVW2kzuR+jSp5ADckzOPy6BnDzlo3O6183rqXL1r30mA+6XsK91GyNGIGi9VTSfSPGtFJTmvTU2Qoc/90lWF09PS7Pcg7gGhftUdpVCvCPKVwlTwYL8nZO26YnGbarZ2095bx26xaNFYtrZYONNb+E4qkSEkPTHQTUWUmaQGJI/Kp9jgsvUrxIQ7rKzxeWoVUzvzwXK6OnU9aYNyQz+ESBfZ3VvJu/U72d1YCIJd6c03cLG5NWjSihAGH4GJdcxH/qtjsIaCTgqJ5cNIKZoYlD/v4vqjoauPZ0i0eAhos8+W+rEXckDjjjKKLmkwG/n5sBx/XFSPijs27K2MWd2fOPmvB8GaHnXerinj+6G7PbPjc0CgenbaIOZEJZ+V/nilOxDK9WnKIdXXlnupQblgkP8wpYEXC+Wcd+CahvsvAhtpKPqsq42gfX6hc6sWC+ERWpmayNDFlXPbZCeLZBxU6LWuqylhTVUZdl8Fzvb9MxvKkNFamZjAvNnGCbJ5lHNO28eLRfaytvbAOPmaHnf+WHeTFkn2e8vasiHh+Pm3RWTX813Z38ufCzWxqcZ+8g2QK7p88n5tTp5/Rvmq0W3m+7CverjmASxSRS724PXU2d6fPw182ctJvczl4p24fL1dto6c3q3FueBoPZC4nLShy2MeLosjezgpeqtpAmdHdrBLo7cutSYv4TvxsFF5DHwiNDhOrm7bzSfMOj7rpI5UxLzyH5ZH55ClTh1XIzE4LR7sqKO5VNZst/Q37CqmcycHp5CozyFVmEuc7MgJ7rtHjNHOsq8JDmlutHf1u9/VSkB2U5iGiMb4Rw76ODquBTW0H2aDZT5Pl5PPNVGVyY/xSpihTht2uIn0d/678kkJDHQB+XnKuT5jHTQnzCZQNrzpaXQ7ert3DK9XbPSX4BeoMfpK5nJQRRG71xa62av58dD1V3e7XkhEUwW9yL2V6WPyonudUHNO18ofCTezvcJf21YoAHspZxDWJOWfNj95tt/Gf4/t5uXS/x1e+MDqZR6cuJEt1/jb01Bv1vHT0AB9UHvOIDsnBIdydnc81aZPPij1pAidRa9CztrqMNZVlVPRRQhXe3lyUkMzK1EwWxSeNOZXgW088T7zBa6vK+zUI+Xh5szTxzN/gCYwMoiiyX9PEC0V72dZc67l+YWwSP5xyfpdbHIKL96uK+VvxTk9syKQQNY9OXcTC6KSztt3ddit/L9nB65UHcYoCXhIJN6dO5/7J80fltTwVgijyWUMRT5Vs8pTrl0dP4mfZy4nxV47ieQS+bDnKP8o30WoxAJAeFMlPM1cwOzx1RM9RqK/lxcr1/QjJDQnzuClhAQGyocs/WpuBDxu3sa5lt6dRKM5XzbVxC1mknjbsNB+L08p27QG2tu+norsOgZNJFVIkpAYmkhvsJprpgUkj9oAOBJvLhk0YWRj8CUiQEOAdcEb7V7u100NCi7vK6Xb2794PlSspCM1lecRcEvyH9ieKoshxYz2fNe/kq/YjnvcrOziJG+OXMVOVOeS2iqLIvs5KXqxa32+B8d2khVwXNwffEdg5dDYTL1V+xYcNB3CKAlIkXBM/gx+mLSZMMfJGOqcg8H7dIf5eusUzE35lXA4PZy9FPYrnORWiKLKhuZw/F272dMFPDonk/6YuZ0b40ArxmaDDYuIfR3fxdkUhTlFAAlyZNJmHcucTF6g8a//3TDGQzSrc1587s2fw3cy887bq9U1CeWcHa6vLTxPkAmRylielcvkYBLlvJfFsN5tYU1nGJ5Wl/SRlmVTKwvgkVqZkclFiCgHyC3MO6oUEURT5qqmGF4r2crDNfbLxkki4PDmTH04pOK8N5qIo8nlDOU8d2UZt73SRuIBgHspdwBVJWWdNxRBFkU/qj/Hnos1orW6isCgqhV/kLSU1aOxNEeCepf67os89oy2TAkL5Ve4lzFUPr1r1xaHOOp4+/gWlXe55zmpFEPdmLOWymNwRzfcuMzbxYuUG9na6y55yqTfXxs3ilqThS7AtFi3vN2xhg2a/Jx4oNSCGmxKWMSdsyrD/v7qngQ2anWzvOIi1DxmMUqg9iuaU4HT8vUdH7p2CE61Ni8bahsaq6f3t/tE7Ts/UHAl8pD5EKNREKiKJVET0/rgv+3uPLjRaEAVqTU0eInrcWN2v2SojMInlkfOYGzoNn2FU5laLlvcG+AxujF/K3PCcYT2429pLeKlqA7V9LBW3Jy/hyth85CMg+PU9Wp4r28CWtuMA+HrJ+V7KPG5JnovvMNveF3qbmWdLN/Nh3WFE3BFM905axM3J+WfUsGNzOflf5QFeKN1FT2915OrEKfw8dwlhoxiwMFrUGfU8XbSdNXXu90UmlXJz+lTumzKX0POwMfMETA4775QX8fKxg7Sa3Na3YLmC702exu1Z0wmZ6K846zhhQVxTWcba6vJ+I76DfRRcmpLO1WlZzIiKGfbcd94Qz+3bt/Pkk09y6NAhWltbWb16NVddddWIHz/cC+mx21lfW8knlcfZ1VTvKeF6SSTMiU1g5Vky0U5gYLgEgS/qKnihaC+lOvfJxcfLi+vSpvCDnHziz+NVOMDu1jr+cmQbRZ3u+d6hPn7clzOHm9KmnlUrRqlew2OH13NI20sMA1X8ZupyFkaNjhieCoPdwt9Lt/Be7SEERPy8ZNyTuZBbUmeNyseps5l45viXrG0uBNxThr6XMp+bk2aP6IRf29PGf6o28lX7McDddLIyZibfS14ybNNJbU8r7zZsYmv7EY+PcUpwMjfGL2XGMGqb1WVjR8dBNrTtpKqnwXN9tELN8si5zA6diloxsqgdi8tCnam+H7lstWjosHX0U03PNgK9A08jo7F+sUT4qEekktpcdo51VbC5fQ/7dcWeqCt/L18WqQtYETmPOL+hY4K0ti4+atzK2lNU5xviL+KiiOlD+kldosCG1kJert7kaSKLVCj5fspSLo6aOiIv6hFdPU8f/5JjBvf3JcY3hF9mr2SuOm3Yx/bFUX0zvyv6nKN690IqNTCc3+ReSn544qie51RorSaePrqVD2oKEYEAmQ8PZC/gltQz85UOh6OdGv56ZCs7WusAN6G+e3I+d07KP2u+0/GA3eXi0+pS/lW8z5Ns4uct4+bMPO7KnkHExGz4c4K+Tddrq8r7JfzEBARyZVoWV6VPIl01sBBy3hDPL774gl27djF9+nSuueaacSGeDpeLHU11fFJxnA11VVidJ1fvUyOiuCptEpelZBB2no2T+ibD7nKxurqEfxXto9boVnn8ZTK+mzmV72fPIMLv7K32xwPHdBr+emQb21vcdgA/bxl3ZeVzV1Y+AaPwPY4WXXYLzx7dxlvVhxFEET9vGfdmzeN76QVnRHQFUeSj+iM8W7IZfe889Utjs/lZ9jIifEduWRFEgU8aD/O3sg0YHRYkSLg2fgb3pC8ZtukHoN3axYtV6/myxU0aJUhYEZXH91OWDhuJdNxYz7v1m9jdecxz3UzVJG6Mv2hYf2GtqYkNmp1s6ziApbcb3FvixazQPFZEzmNyUNqISFq7tYNCQxGFhiLKustxDRKZJJfK+xHBE5cjFGoCvEe37w+moLZaNRgchkEfp/ZRk6fMIU+ZS0ZgOt4jUBD19i42t+1hY9tu2m0nPV+TglJYHjGPOWFTkUsHtyMZHSY+adrBJ8076Haae7cjhOviFnNJVMGQCqpDcLKm+SCv1mxBa3M3EMX7hfOjtBUsVE8e9vMRRZENrcd49vh6NFZ3F/3yqGwezrpkRBmxJ3Diu/JMySYMdrdXeCzflYFQ1NnMbw+t56jevZBNDw7nsWkrKFCf3Uagna11/OXwVo7q3MMfwhR+3J8zl1Wpeed1L4NLEPiyvpIXivZQ0ukWLuRSL65Lz+aHUwqID1J+vRv4LYJLENjb0sgnlcf5orqCHsfJPOKsMDVXp03iirRJRPifPL6dN8Sz3z+SSMZMPA0GA9UWE59WHmdNVRm6PjlVScEhXJU+iSvTJk3kbJ5jWJwO3i0v5sWj+/uVSu6YPJ3bJ09D6XN+l0oaug08XbidT+tKAXeJ6qa0PO6dMpdw37O3cBFEkY9qi/hr8VfobL3EMG4Sv8xbStQZRrKMl4pT1d3G749+RqHerRRmBEXy6+wrmBIyvGfNKbh4v2EXr1RvwuxyH7AWqidzV8oyUgKHbjw61lXD67VfcsTgbqqSIGF+eA43xi8lNTB20MfZXHZ2aQ+xvm0XFd0n/cSRijCWR8xjsXoWSvnQyolLdFHVU+0mm/oiWqyt/W4Pk4cR4xvtJpa+J0lmiCzknHiVrS4rbX1K+RqrhlZrG43mRpx9yucKqYIpwZPJU+aSo8whSDb06xZEgULDcTZodnFAd9Sj4AZ4+7NYXcDyiLnE+g3+uZmdVta17OHDpq/Q2d3HAaUsgGvjFnF1zPwhCajV5eDjxj28XrvVMySgIDSdhzKvIM5/eIuJ2WnjXxVbeLtuLy5RIMDbh3szlnJdQv6I7B8nMFB14EeZC7l1lNWBU3EiCu2p4q/Q9xLblfGT+UXeRUSchRGcJyCIIp/Xl/FU4Xbqei1D8QFKHs5bwOWJk867QRx9IYoiW5tqeb5oTz+r1srkSdyTU0CG6uyliEzgdFidDjbVVfNJ5XG2NtR6JjhKgDkx8VyZPolLktMRrbYLk3jabDZstpP+K6PRSFxcHHNe+jvNzpPXh/n6cXlqJlenTyInPPK8bVD5psJot/HG8SO8cuwgndZepcPPn7uyZ3JTRu55bw432Cz84+huXi8/5AlmviIxi4fy5pMQeHYXL8W6Fh4/tJ5CnZsYpgWF8X/TVjAnIvGMnne8fGsWl52XKrfyRs0unKKAr5ece9Iv4sbEghGVQQ/ranj6+KfUmNw+6+zgeH6auZKs4KEJq8ai4+WaNWzrKATc5fiLImawKv4i4vwG9wTr7F180ryJzW17MLssnscWqHJZETmP7OD0IbvbTU4TR7uOUWgoothwFJOr7wAJL9ID0jxKYqTv8N36XwesLislxlIKDcUUGYrocpycHiNBQkpAMnnKXHKVOcT5xg55vOy0GdjcvoeNml1o7Sc9qpOD0rg6dhnTlFmDPt7ucrBes5/3G7egsbpLpmqfEO5MvpxF6qlD/l+T08qbddt5q3YbDtGFTOLFzUkLuS1p0bAJB+CelPWHY59xtLf8nhUcw2+mXDGiDNm+GC8/9KnQ28w8e3Qbb1ef/H7eN3k+t6fPPKtB8A7BxbuVRTxXvMvjHc8JjeRX05dQEHFmHf3nAvs1jTxftJdtTScXkysS0vhx7ixyw8d/ctQEhobeamFddTmfVBznoObk6FwfL28WhEfx8jWrLjzi+dhjj/H444+fdn3c07/DPzCAi5PTuTJtEvNiE86qV2YCA6PTYuaVkoO8XnqY7l7pPS4wmB/lFHBtajYK7/M7DsPmcvJG+WH+cXS3p5tyflQij05bRLbq7JIKvc3M00e38m71Ebfvy1vO/dkLuDVtxhmdeARR5MO6wzxTssnTqXtFXA4PZy8jfJQNDTvaK/jTsTW09HarL46YxKOTLyPSd/jw705bN/+oWMf61kIAlDJ/fpx+CZdGTxuS+JmdVt5p2MxHjVtxiE6kSFgRVcDNCcuIUAw+9abL0c3qpo18odnuCXaP8AllWeQ8LlLPQikf/MBndBjZpd1DoaGIiu7Kfh5Nfy9/cpVTyFPmkh2cPepmI3ArNgaHDo21GavLMvwD+kAqkRIqD0etiEYuHb0vTxAF6kz1HotAvbmh3+2hchV5ylzyVTPJCEwflAy6RIEj+lI2aHZySH/M46/NCEzipviV5CgzBt0Gp+BiS/shXqv9wjOeMysokR+lXkVm0NBl5kaTlqfLPmNfbwNalCKEn2auZL46a9jX7hIFPmo4yN/LNtLjtCJFwo1Js7gn/SL8hwnA74uBEiBWRGfxi5wVZ1x+P6Zr5bHD6znS6T5ppwaF8X/TljM3IumMnnc4mBx2/nv8AP8u2YfJ6T52L4tN4+fTFpESPLaRoucSR7UaXijay5d1FZ7E2nnRCdybN5tZkXET4tPXgEajgU8ry1hdUUq1QYdgsdL40G8uPOI5mOL51qF9XDll7CMBJ3Bm0Ji6eenoft4uL8bSmxuXpgzlntxZXJE86bxfBJzoVP/L4a009BgA93jLX0xfzMLo0QVSjxYuQeC9mkKePrrV4yG7MiGbn+cuGfNM9RMo72rjscK1HnVmrNmEbVYjT5Z87pk6FKkI5ufZl7EoYtKwj3UKLj5q3MN/qjdictqQIOHq2ALuTltOsGxw0uYSBTZqDvDfmnXoHe7ybJ4ylR+mXkVKwOARP90OE5+2bGJdy1ZPY0tGYBLXxV3CVOWkIUluTU8tG9s2s193oF9pOsY3mjxlLnnKXFICkkccBC+IAjq7Fo212f1jafZctglDTxoaDhIkhPqoiVTEeH6iFDFEKKLx8Rp5s6TOrqPIcJRCQxElXaU4xJPTl+J8Y1kasYTZobPwGWIOvNamZ23LV/1IfnZQGjcmXE5W0OARWlaXnY8at/Juw2bPZ7U0YgZ3JF1GuEI56ONOdMD/rXwNbb3+zblhmTyYecWIRnBqrd08dfwLvmw5CrjTFx7NupQlkYOrtQOh22HlH8e38nbNflyiiL+3nJ9kLeGm5JmjKuOfCkEU+bi2mL8Ub+lntflF3tKzNv3oBDosJp4r3sk7lYW4RNEd15Y+lZ/kzDuvO+BPoNLQyb+L97G66uTkqOnqGO7Nm8Xi2OQJAvo1QBRFSrTtfFh8mMeXXnrhEc9TcS5mtU9gcDQYDfyreB8fVh7zTOuZEhbBvbmzWZ6Qdl77hE7gUHsTfzj8FYc73AqD2jeAh/Pmc23ylLMeWn9Y28Tjh9dzTO82+WcEq3ls2gry1WdW4jI77bxwfCv/q96LSxTx85Zz/6TF3JycP6pFgEsUeK9uHy9UbMbktOElkXJz0mx+mLYYvxEoRMWGep46/gmV3W4v5KSgWB6ZdBWTggf3YgIUGar4d9UnVPW4P5NoRRg/SL2C2aHZg544TE4La1q2sKZli2d8ZGpAPKviLx+y/OsUnOzXHWRT22aqTTWe65P9k5gTOptcZQ5qxfCesW5HF7WmylNIZgsO0T7g/aV4oVZEEOA9uuOWU3TSbtVgdvUMeh+VPKwfIY31SyDGN2HYwHyby8bx7jIO6Y+wt3Mf9l4y6O/lx4Lw+VwUsZhwn8HfC529i4+b1rNes8tD3POUk7gx/nLSAxMHfZzWZuC/NZ+zse0A4A76vyF+CdfFLRmyjG5x2nm1dgvv1O3AKbqQS725NWkR301ciM8I5rfv7qjiT8fW0Gh2l/0XqDN4dPJlI5qo1RdlXRoeO7KOIn1vGV8ZxWN5lzEl5MxmtBvtVp49to03qw4hiCK+XjJ+lDWHOzNmnfUw9aouLX8+vJVNTe7Z84EyH36UPYs7MmdcEPnWjd1dvHh0P+9XFGNzuc9NWSo19+bN4uKE9PN2IMk3Gd+45qIJ4nluYbBZeObwLt48fsSzqsyPiOXevNksiDk788jHG0a7ld8d3MwH1W7Vw89bxg8mF3DXpHz8zrJybnM5+WvRFl6rdJ9oA2U+PJC9kO+mTj9jdbhE38ID+z+gyWwAYFn0JH6ZczGRoywBtlm6+NmR9ynqbR7KUcbxqykryQga3jflEJy8UPEF7zXsAiBI5seP0lZwRczMYfyUVl6o/NhDQPy8FHw3cTlXxswfNMdRFEW2tO/lf3WrPUHoiX4xrIq/jHxVzqD7okNwsL1jB2taPvfkaXpJvChQ5bMs4iKSA4YvbVpdFooMBzmo20l5d4lnhGRfeEtkqBVRHkXyBBkMV0TgJRkbeRBFkR6nEY21mdZT1NRuZ9eAjwmRhTJDNYeZqnlE+Q5N/MHtbd3RsZNN7V/RYXNP85EgYVZoAVdFrxzSz6q16fmg8Us2t+/2xDHNC5vOHUnXEiIf3JZRbmzgX1WfUGJ0+/UifEL42aSbyRkmpaDO1M5Txz/lkK4acHe//yH3JlIDh99XrS4Hr1Rt49XqnThFFwovGb/OvoLLY/OGfWxfCKLIB3WHeKZkM0aHFQnww4wF/HjSwjNSPwHKDG08dng9BzoaAUgJDOVvs68iK+Tse4p3a+r546EtHNO5PdlxAcE8OecyZl0A/k+ANnMPLx87wJvHCz1TnDJDwvntrCXMiT4/x4h+U3HeEM+enh6qqtwrqqlTp/LMM8+wePFiVCoV8fHD79gTxPPcwikIvFVWyDOHd2KwuVWlhTFJ3Js3i/zIszeBY7yxs7WOn+3+nBazEQlwQ2ouD+bOR30OYp1qjJ38ZM9qSg3uA/l3knJ4JGcJYYoz65IXRZH36w7xh+IvcQguonyD+U3upSyOSh/1cx3Q1vCzI++jt5sI8PbhJ5nLuTZ+xrCKGUCLWcevi9/muNGt/qyMmcE9aZeglA/9+o4aqvlr2dtorDokSLgseja3JV6Ccojg+EZzK/+ufpdSo/sYEusbyar4y5gdmjfotjoFJzu0u/isZS06u1vpUsqCWaJezCL1AoJlQ/tVXaKLMuNRDuh2Umw41E/RjFbEEeMX30dtjCXUJ/yszmg/FSZnNxpri4eMtlqbqDNV9Svtx/omMlM1l+mq2QTLhlb3BFGgyFDMxrbNlBjd6Q4SJMwNm8OV0SuHVIPbrFreb/yCre37ejvBfbkl4QqWR84b9PMRRZHtHYW8VL2GdpseCRKui1vMbUmXDBkiL4oim9uKea58LVpbN3KpNz/NXMmVMfkjWgjX9nTw+6OfcUhXB8D1Cfk8knXJqCdTaa09/PXYBtY0uhe0s8OTeWrmNah8zvz7vaahhD8WbqLDakIu9eLnuRdxa9qMs77QF0SRT2tLePLIdlrM7sa0OzJn8LOpCy8I9RPcTS+vlh7i1ZJDGO1uu97Fien8Kn/ReZ8f/U3BeUM8t27dyuLFi0+7/rbbbuO1114b9vETxPPcYWdLPY/v2UyFwT1iNCMkjN/Ouoi5F9Cq0eyw8+cjW3m9/DAACYFKnppzGTPVZ580i6LIR3XFPH54PWanA5WPH3/Nv5zF0aMLtR4IZqedxwvX8VljMQBLIjP44/QrCZaPLq5KFEX+V7OLv5dtQEAkPSiSp6fdSJz/8L45gK1tx/hDyYf0OK0Eevvym+zrhm36cAhOXq/7kvcatiAiEqlQ8WjmzWQrB/fW2lx2Pmj6kk+bN+EUXfhI5ayKv4zLoxYP2lnvFJzs7tzDp81r0drd+7BSpuSK6MtYED4f2RB5lKIo0mCu4YBuF4f0e+hxnuwKV/tE9ZK4OYT7nJ9zsO2CnZKuIxzQ7aLUWOjJG5UgISNwMjNU88hVzkDhNfT+Umuq45Pmzyg0FAEgRcq88LlcEX054T6DRxtV9zTw7+p3PCH96QGJ/DBlFUkBg3/vzE4r/6r6hC81+wBI9o/mF1nfJdF/aBXTYDfxu2Pvs1tbDsCyyFwezbpmRM1DLlHgxcqveKlyK+BW+Z+cvoqIUeR+nsBnDcU8VrgWi8tBhCKQZ/OvY2romR9ndDYzP9+/ls0t7jixpdFp/Dn/8jMalztS9Dhs/OHgFt6pcn/+yUEqnp5zOVPDR5cM8HVCb7XwzOGdvFlWiCCK+Hh5cVd2PvfkFkz0iJxlnDfE80wxQTzPPhqMBn6//yvW17sPdEofBQ9Nm89NmbnnfdNQXxxsb+Kh3Wup7zYAcEv6NH4xbdFZL6sDdDts/N/BL/iswd2cM1udyNOzrhiXnL7abi0/2f8BlcZ2pEj46eSL+H7anFGrICanjd8WfcwmjVvVujwml19NuWJEk4dcosCLVRt4o3Yr4I5I+l3OTUT6Kod8XJO5gz+U/s/j5Vwemc89qVcPOU/9qKGCF6rfos3qJo8zQ6ZwZ/J1g04YEkWRPZ37WN38Ke02d+h0sCyYy6MuZZF64ZAB6FpbOwd1uzig20W77WRmZ4B3ENNDZjNTNZd4v7E1LDgFBx22Jqx94plGAqlESphPDP6j9IaegMnZzWH9Pg7odlJrqvRcL5PIyVFOZ6ZqHplBU4ZUaat7aljd/ClHu05MmvJiQdg8roq5EuUgpXSXKLBes50369dgcVmRImVl9GJuSlg55Gews6OYZ8vfx+g0IZN4c3fKSq6MmT/key6IAm/X7eDfVetxiQJJ/mr+knfriHI/Aba3lfOrwg/pdlpRyf35y7QbmBk6+q7ySmM7P9n3PrU9nXhLpDycvYxbUwrOWKEURZHXKw/y56LN2AUXkb6BPD3rSmad5eD5E/iquZpH93xBu6UHqUTCjybP4ic5887r8PlTUabr4PG9m9nd6l4MRfgF8POZC7k6ZXQNZhMYOSaI5wSGhdlh54Wiffzn2H5sLhdeEgm3TJrKT6fNPe+D3/vC5nLyTNEOXirZhwhE+QXy19mXMj/67MaTnEBxZws/2bOaBpMBL4mEB7IX8oPM2eNibv+iqYRfH/kMs9NOmI8/T+d/h/ywxFE/T013Ow8deodakxZviRc/m3wp18XPHNEBuMth5rfF77Cv001ibkyYxz1plwyb6bmzo5gny97B7LIS6O3HTzOuZ3547qD3dwou3m1cx8dNGxARCZUruTP5OgpUuYNuZ4dNy6u1//OUiAO9A7k86hIWqxcN2qUtiiKlxiI2aD6lxlThuV4mkZOrnMEM1Vwyg7JH5c90CHbarPW0WGpottTQYqmhzVqPq0/n/GgRLAsj2je530+QbGTK9AkMRaxnhy5iacTl+A0x+72yu4rVzZ963l9/Lz9uSriRuaGzB/1MdDYDr9R+yO7OIwAk+sfwUPodQwbQd9q6eLr8PQ7o3LPGF6mn8mD6DfgOo2IW6ev4dfFbaG3dBHgreGzKKuaGZw75mBNoNOl46PA7VBg1eEmk/CRzObckjWFB57Dx6yNr+LLZvehcEZ3F76ddMS4Tz47r27h/z2pqujuRAD/Omsd9k+efE0HAYLPw2IFNfFLrfl2TQtQ8M/dyJoUMnql7vkEURdbXV/L7/V/R2O32Rk9TR/PE7KVMCTs/M3kvZEwQzwkMClEUWVNTxh8PbPVMG5obncBvC5ZccBMhSnRtPLRrLWUGd3PEtcnZ/HbmUoLkI4+bGSsEUeTl8r08XbwVpygQ4xfM32ZfxbSw4Rs7hoNdcPHUsQ28Ub0fgBmhCTydfy1qxegV1I2tx/ht0WrMLjvhPoE8NX0VuSEjaxyo6m7l0cI3aLHo8JHK+NXk77AsanDyCOASXPy39nPeb9wCQHZwEr/KupUwH+Wgj2m3dvJMxauU904cWhYxl+8lXoPvIMqoIApsbvuKD5o+wibYkElkXBFzOSsilg0ZC1RrquSz5nep6ikDRl+KBrC5LGisdbT0EswWSw3t1sYB57UrpH4EDOOxPBVOwY7B0THgbQHeytPIqFIWPqLxkgNZCfy8/FkeeSULwpchGyIztMxYztsN73oyQacEZ/O9xFsJ9Rk8//GA7ijPV72J0dGDj1TOXcnXs0Q9a9BtFUWR1U3beanmM1yiQIJfBP83+XvE+w9tb9DajPyq6C2KDfVIkHBnylJuT148Ir+yxWXnD0c/Y22zu7S8LGoyj+VcParMzxPb/lbNfv56dAMOUSAxIJTn8q8jPfjMrRlmp50nDm/gg1r3Nk4Pi+Vvs64i2n/4bN3xwBf1Zfxy33r0NgsyqZQHcxdwd1b+BdU1bnU6eaXkIM8X7sHsdCABVmXk8Mj0BYT6nv8RUhcKJojnBAZEaWc7j+3dxD6NuzEkNiCY3xQsZkXCyGZXny9wCgL/LtnLc8U7cQgCoT5+/HHWxayIH32jzVigtfbw8L417NC443kujZvEH2ZcOi6EV2Mx8tP9H3iyOe9Mm8tPspaMWuVwCi7+Ub6J/9XsBGCGKom/TLue0BHMWAfY2FrEH0s+xCo4iPZV8ee875IWOLTXS2/v5o+lr1NocDcDXRu7kDuTVw6pju7RHuH5qrcwuyz4eflyT+pNzA2bNuj9Wy0aXql9lcoe9/9ID0zj+0nfI1Ix+EleY21hbcv7FBnc3fTeEhkLw5ezWH0JwfLhiWGbtYFDus1UdB9Ba2sesLvdzyuIaN9kYvqQwhB5xJi+V1aXmVZLbT9y22FrRhyA3Pp6BRDnl87UkEVkBRXgPURZG8AlOjnWdYR1LR/SanXvYyGyUC6NvpZ81fwhm7a+0Kznk+bPcIpOFFIfro+7jsXqhYM+Rmfv4rmK/1Hc5fZjzg+bwQ9TVuHnPTjBP2ao4Xel/0NnN+Lr5cPDGatYoM4b8jU5BCd/K1vLx017AVgQnsX/Tbl+SEvHCYiiyPv1+3my9AucoovkgHCenn4jSQGjX4QX6pp4cP8HtFqMKLy8eTxvJVfE54z6eQbCmoYSfn3gc3qcdoJkCv6cfxkrYkem7p4pOiwmfrH3C0/00vTwGJ6eczmJQRfWiGqNqZs/HtjKp9VuZT1I7sOD0+Zxy6SpF5St7HzFBPGcQD8YbBaeOrSTt3oN1wovb+7JncUPpsy8YLoWT6DGqOPBXWsp1LpHTq6IS+cPBSsIO4uz1ftih6aGh/Z+RqfNhMLLm99MXc4NyXnjQtz3ddTy0IGP6LSZCJT58OfpV7MkavAJMYNBZ+vh0SPvc6DTrSDemjyX+zOWjWjkpVNw8a/K9bxdvx2A/NA0npiyiuBhutZLumr5Xcn/6LR3oZDKeThzFQvVUwe9v81l57+1H7GhzU2M0wMSeTDje0QoBvbpuUQXX7Su55PmT3GMkPh02fV83voRezu3ISAgQUJB6AIujbqWEPnQ01psLgtHu3ZzSLeJBnN5v9sCvVWnKY/BstCzunizC1Y0lvp+ZLTN2oCAy3MfP69ApoYsYoZqKWrF0I0ugiiwX7eDdS0fYnC4u/+jFLGsjL6B7ODBx1u2WFp5pfY1qnqJf0ZgOnck3T4o8RdEgdXNG3m7fi0CApGKMB5Mv4O0wMH9ijqbkT+Uvk5xlzs+6Tuxi7gz+XK8htl/P2s6wFPHP8EhukjwD+fPebeQ6D+y0nCRvoGHD71Lh60bf28fnsi5mouiJo/osX2ht5l55ODH7Gp3b/uNSTP4ec7FZzTv/QQaevQ8sOcTinrH7d6UMo1f5S09J8dwURT5sOYojx/YRI/Djq+XjF9OX8x304cehXo+4oCmif/bs4lSndsTnhESxmOzLpqIXzpDTBDPCQDuqTnvlBfx5KEdnniky5Iy+OXMRcQGnptSzXhBEEXeKD/Mnw5/hdXlJFDmw+P5y7g6afI5OfA5BBfPHt3Gi2V7AEgPDufvs68mLfjM7QmiKPJK5W6eLdmMgEhmcATP5V9PfMDoPH0AR/WNPHz4XdqsRvy85DyeezXLorJH9Nguu4nfFL/DAZ2bVNySuJAfpK0YMqdQFEU+bd7Bv6s/xSUKxPmp+e3kO0gYokTaYG7h6fJXaTC3uCcdxSzjxvjLByXGDeZGXq55lXpzPTB8qdfsNLGpbS1b27/0xCFNCZ7Oyujrh8y4FEWRZksVB3WbKDbsxCa4J01JkZIRNIOpIYuI98sgcJTl87MFp+BAY63nuHE/h3Rb6HbqPLfF+2UwQ7WUKcq5yKWDK392wc6Ojo1s0HyK2eXOSU32z+DKmFUkBwxcQRBEgU1tW/ig6SPsgh2ZRMY1sVdxceTyQRcBZcYanql4lQ6bDi+JlFsSrmRl9JJB7+8SXLxSu44PGr8CYEpwCr/OuhWVz9DngZKuRn5R+AYdNiN+Xj48NuWGEY3bBOi09fCzw+95IpduT57HvRlLR7Rg67ftosA/y7bxzzL34i03JJa/FVw36qzdgXA2j0MjQVNPF4/sXseeNrftYn5UEn+dfQlR/hfW+Xmwc+Ov8hcTE3BhvZbzBRPEcwLfqFVdi8nII7vXsUvjJh7zIhP565xLiT5HB7smk4EH9nzima98c8o0fjlOSkOPw8YvD3/KxhZ3+efKuFx+m3cZvqN8blEU+bDhAH8p+Ryn6CLRP4xnpt9IcuDIFJ8KYws/L3yDVqsehVTGr7KvY2nk0GVCi9PGsxXv81W7O75qYXgeD2bcgN8gJU5RFNnYtptXaj/ALjhQygL5Sfpt5CkHHs3pEBx81rKWda1f4BJdwza3OAQ72zs2skHzmWfyT7J/ei+JGlw5tjh7KDRs46BuExprvef6UHkUM1QXMTVk8XlDNgeDS3RR2X2Eg7pNlBsPejynPlJfcpTzmKFaSoxv6qCLNDdZX9NL1t1B3DnBM1gZfT2RvgNP6OmwdfDf2v9RanTvu0n+SdyZdDuxfgOTe5PTzAtVb7Ont/FoqjKL+9NuRSkf3Lu8o6OYp8rexuyyoZIH8uus25gyTOC8ztbNr4reotBQB8D3ky/ijpSLRuT7dAou/l62kddr3cMR8kOT+fPU61CN0KLSF1s1FTx6cDVGhxWV3I+n87/DrPDxaXrcqanhoX2fobW6Ky+PTVvBd5IGb8QbTwiiyP/KDvHnI1uxuZwEyX14YuZyrky68DrGDTYLTx86Gb/krgYW8IMp+RdcNfDrxgTx/Bbjm+RjEUWRj2qO8fiBTXQ7bCi8vHvLO9PO2bjOLxvL+MWBdRgdVnf5e+blXBw3Pt6qSmM79+97n7qeTmQSKb/MvYQbEqeP+uBtdTn447E1fNbkPqEvjczisZyrCZCNzHO6vrWQP5V8hE1wEOMbyp/zbiE1cOiuz0ZzO48f+y/15ja8JFLuTr6Cq2MXDDHy0sw/q972dDtPVU7qJR0Df6+reqp5pfY1WizusuKMkOncknDzgHE+gihwQLeTdS0fond0AhCpiOGK6BvIDp424DaJokitqYSDuk2UdO3B2Uu2vCUyJgfPZoZqKUn+o1PT7S4zGmslGms5rZZyNJYKbMLgoy8HghQpoT4JRPpmEKXIINI3g2BZ5Ki2w+jQcUS/lYO6TejsGs/1kYpEZqiWkqdcgK/3wETKYNd57AkiYu80o4VcEnXNgPYEURTZrt3Juw3vYXZZ8JJ4sTL6MlZGXYb3AOHs7sXHLl6p/RC74CBEFsQD6beRoxz8O9VobueJY69SZ9YgRcrdKSu5JnbhkO+JU3Dx94p1fNCwG3DPen9syqoRfyc29DblWVx2IhRBPDVtFVNCRp/T2WjSc/++9yjrajujOLSBoLX28NDez9jZ5rbUXBE/mSdmXELgOHTUjwRVXZ08tGstRZ3uxIRL4jP4fcGKC2Lm+6n4pvQ/fJ2YIJ7fQgzUuXdjRi4PT59/QXbuaS0mfrnvSzY0umN8poZF8/Tcy0kOGn35eSywuZz84chG3qp2q3lTQ2P42+yriPVXjsvzf950jN8c/gyzy0GkbxB/y7+OXNXoO+JbzAYeOvQOx40tSJFwf+ZybkueO6KDpVNw8ULlF7xb7/ZZzgpN5/GcVQTJht5fdnQU8VTZO70qVBC/ybptyED48u5anil/lXZbp3sefPwVXBkzsAJlc9n4qGk1G9o2ISISLAviloSbmamaMeBzN5hqeLvhPzRb3KU/pUzFZdHfGbRRxj2pZztftX1Ap/1kxFCkIoEZqmVDkrK+cIlOtLY6NBY3yWy1ltFpa4ABGo/OFL5ewUQqMojydRPRSEU6ft7DW2UEUaDOVDoguc4LWcRFETcMGtGksTSzpuV9irsOAiCTyFgReRVLIy8fMGpKZ9fzet2bHDEUAhDrG8v3k24fdDRpvamFp8tfodGiQYKEa2KXsyruskHL2haXjb+Vv8+WEarrJ7Cu+RB/Pb4au+Ak3i+MP+fdQlLAyLrNq3tjyOpMWmRSL34++XKujR94PxwKVpeDxwvX8UmDuzN9aVQmf5x+JYEjJMFDQRBFXirbwzNHt+ISRRICQnhu9tVMUQ0/TnQ84BQE/nVsD88V78IpCoQp/PjTrEtYFnfmgzPONURRZG1tGX/YfzLxZV50Ar+ddRHpISPLiP02Y4J4fsuwob6SJ/Zt8WSVTVfH8Pjsiy7YrLIvG8r51d71dNrMyKRSHsiZzw8mF5wzxba2u5N7d31MWZfbpvCDzNn8dMpCZOPQIOAUBJ48toHXq90TW2aFJ/H0zGvHNHLvgLaGRw6/h8FhJkTux1+m3kB+2OAEsC+67CZ+VfQWh/TuzvzbkhZzV+qyIf2cLlHglZq1Ht9dTnAKvxrCdyeKIp80b+LN+s8QEIjwCeXBjDtID0wc8P4N5kb+WfVvWq1ulW5u6BxuSriBgAGIoFNw8nnrh2xqW4uI6IkGmh++DPkg0UA1PUdZ1/IqGmsdAHKpglzl/GHL0AA2l4k60yFaLKW0Wsppt1bh7DNO8wQCvdUeghilyCBANnQT0+mvy067rdpDaDtsNbh6CWNfBMuiiPJ1k9EE/2mE+SQO+bxmZzdFhu0c0G2irddOIJPImRt+BYvU1yKTDqyS1fZU8mnLO1T3uBusYn0TuSXxh0T7nq7+iaLIPt1+3qx/m25nD1KkXBN7FZdHXTrge+tuMPuQDW3usnZGYBIPZ3yfMJ+BbQ1uP/FOXqz+FKfoIs5PzWOT7xg2cul4VxO/KHqDNmsXfl5y/m/KDSxUj6xxqMdh5f+KPmZLm7uCdE3cDH6RffmojwWnjrxN8Ffx/KxVpAaNjzfzkLaJB/aspsVsRCaV8vPcpdx2DsZtnsCxTg0P7lpLRZd78MN3Uqbw2Myl45Jneq5hdtj5Z/E+Xjp6MuP65sw8HpmxgCD5hfd6zhUmiOe3BD12G4/t3cwHle4JI5F+ATx6AU9nEESRZ4t28I+j7vJYpjKcZ+ZeTpbq3I0qPKpr5Y7t76KzmVH5+PF0wRUsiBraUzZSOAWBnx9azbom9+d1d/o87s9aPCTZGwy7Oyp54ODb2AUnWcHRPD39RqKGmSR0Al0OM/cd/A+V3a34esn5TfZ1LI6YMuRjXKLAk2Vvs7ntEADXxy3mjqTLBu00FkWRV2o/YF3rNgDmhU3nhyk34j9IlE6ZsZxnK/6OVbASIgvhe0m3kasceJu6HUb+W/t3qnrcZGB6yGy+E3cbAd4DewVtLgtfal5nf+d6wJ2vuVB9DQWhl+AzTHan2WngsO4Tjug/wyaY+t3mIw0g0jedKEVmL9lMx997fBV5p2Cnw1bjLt1by9FYytHZm067X6L/dApCVxHrN2XI774oitSbj7O+9Q1Pt36YPJpr4+4j3n9gH6woihzU7eLDptcxu0zIpT7ckvAj8kJmDnh/o6ObN+vfYp/OHV81P2wu30u6bdBpSbu0h/ln1duYXRbC5CE8kf0TonwHJ2SlXXX8ruQ1tPYugmX+PJl7D0kBQ0d96Ww9/Lr4LY7oa5Ei4fGcVSyNHDqT9gQEUeC/1Tt4oXwzIiJLI7P409Trx7QQLdY188D+92m1GFHKfXl13q1kBo+PQNBlt/Dz/evY0Oz+XO9Iz+cXeUvPmS3J6nLybOEOXip1D/PIUIbz8qJribtAZ6WfOtUvNiCIZxdeRn7k2R/BfCFignh+C3CorZkHtq2joduABPhhTgH3580+JyMizwZMDjsP7lrL+kb3NJk7J83kkakL8fEa+QSZM8Xe9np+sON9epx2poRE8dL861CPw9hLcJPOnx38mC+aS/CWSHly5jVcHDP6uBaAvdpq7j/wJnbByeKISfxp6nUovEZmhO92WLj/0MuUGZtRyQP4+/Q7SRnGz+kSBZ4ue4eNbQfxkkh5NPNmFkcMnrUpiAL/qXmfLzU73KHeyddxSeTg/s/D+kL+WfUvHKKTzMAM7ku9hwDZwOXuZnMDL9U8g87egUKq4OaEHw5KgMCtcn7c+AJ6h1u9zletYFnkTfgNQlJPwOho52DnRxQbvsAp2gBQyqJJDJhOtO8kIhUZhMijkYxh0XCmsLp6aLNW0Gopo8lcQr3psCfjM9p3EgWhq0gOGHp0o3uC0z7WNL9Mt1OHBClzw1ayNHLVoOqn0WHg9bp/Ud7tXjhdGnUtKyKvGtDSIIoiWzu287+6NxARmRYylR+l/GDQ8ZltVi2/K/0nzZY2QuVKfpf9E6J8B2+M09u7+VXxS1T2NKGUBfBU3o9J8B96P3YKLv5c+jHrWg7hJZHy+5ybWBQxssQHgG1tZTx8+F0cgovlUdn8Me87o+54B9DZTPxwz9sc1bcQLHOTz0nK8SGfoijySvk+/lS0GYBrE3P448zLzqm//0B7Iz/e/intlh5CfHz598KrKYgY2dCK8xG7Wur5+c71NHQb3CNEcwr46bS541IB+yZhgnh+g+EUBP5RuJt/FO7BJYrfiFVYs6mLO7/6iOP6duRSL/4462K+kzK0Ajfe2Nxcwb27P8YuuChQJ/DivOvGzaTvEFz87ODHfNlcikwi5dn867goemwNSvu1Ndx34A1sgpNFEZk8Oe0GZAM0cQyEHoeVnxx6hVJjIyEyf16YefewfjdBFHim/D3Wa/YjRcqvJ9865OhLQRR4sfpdNrTtQoKEH6fezEURswe9/07tbl6peRUBganKPO5J/eGg5KTIcIDX6/6FXbAR5hPB3ckPDhqPZBesrG99g72dXwCglIVzTdy9pAQMvV/p7c3s175PSdcmBNwjLyMU6cwKXUVq4OyvhWgOhy67hgO6Dzlq+NJTlg/3SaIgdBXpQfORDjGX3eLsYV3rqxzRf9X7uBiujbuPOL+Bo5RcootPmt5ma8eXAOQp8/luwg/w8RrYr3hIf5h/Vb2IQ3QyKTCTn6Tfi+8gKrPe3sX/Hfs7TRYNKnkwT2T/hBjfwffPboeZR4v+RWVPEyGyQJ7K+/GwZXeXKPD7Yx/wZesRvCRS/pT73RHHLYF7zvuDh97BKbq4OHoKv8+9dkzk02i3ctfuNynWNxMsU/DKvFuZrBw/X+ZHtcX84sBaXKLI8pgM/jb7qnO6iNeYu7nrq484qtPgLZHy+4IVrEobmcJ8PuLU6mJOWCTPLbqc5OBz03NwIWCCeH5DUWfU88DWtRzpcDdFXJWSxe/mLLugfSeH2pv4wbaP0VrNhCn8eHHhNUxXn/nYydHgk7qj/Gz/GlyiyNLoNP4+55pxO0g7BBePHPiY9S1u0vlcwfUsHkMoPMDBzlru3f8GVsHBAnUGT09fNWLSaXLaeODQKxzraiBY5sfzM+4etnNdEAX+VvEBX7TuRYqEX2bdMmQovCAK/Kv6HTa17UaChHvTvssS9axB7/+lZgPvNLwHwLywOdyRdPuA5VhRFFmv+YR1rR8CkB44mTuS7sd/kCag2p4SPm56Hp29DXCrnBdH3TpkWb3dWs2+zveoMO7wqIdxfjkUhK4iwX/gzvjhYHZ2YnMZR/UYicSLIFk00lHMij8Bk1PHQd3HFOrX4ujNIFXKoskPvZ6s4IvwHnIs5kE+afoX3U49EqTMD7+SJRE3DDpKc492K+81/heX6CLGN567kx9E5TNwefy4sYy/VfwDq2Al0S+BhzJ+SpBsYMXZYDfyfyV/p9HcSogsmCey7x9yzrvRYeJnRf+iuqcZlTyQp/LuJc5v6Agxlyjw+NH32KgpwlvixV/ybmHOCGe8A2xtO87Dh97FKQpcFpPLE7nXjMku0+2wcteutyjSN7nJ59xbmBwytGVgNNjQVM5P9qzGLriYE5HIv+deh/85rIhZnA4e2f05a+vdlpjvZc7gV9NHP4XtfMK62nJ+sXM9XXYrvt4y/q9gMTdmnJsYq/MdE8TzGwZRFHm/8iiP7dmM2ekgSO7D7+cs48qUka/Uz0d8WH2UX+79ErvgYlKImv8supbYgHMbbP9G5UEeO+z2/l2VMIW/5F8+bgdGu+Diof0fsqm1DJnUi+fyr2dx1NjGeh7W1XHP/texuhzMDU/j2ek3IR8hObY47Tx4+L8UGuoI9Pbl+Rl3kR409AlOFEX+Xvkha1t2I0XCo5O+y5IhyusuUeCfVW+xpd1NUu9Lu5VF6vxBn/uj5tWsaVkHwIqIZayKv36QLncrb9W/xBGDuxlrYfgKro69eUCCahesbGh9iz2d7ucNloVxTew9pAbmDbrdzeYS9nW+S03Pfs91yQEFFITeQIzf6KwQFqeeVkshrebDtJgP0+VoGNXjT0Am9SPSN49ov6lE+05H5ZMyKqXV6urmiO4zDulXY3W5u3MDvMOYGXotU5SXDhoob3Z2s67lvxQa3L5ctU8c18bdS6zfwB3KNT3lvFzzN7qdRgK8g7gz+QFSBslLrTXV8XT5s3Q7e4hSRPJIxoODDgAw2Lt5rOTv1JtbUMoCeSL7AeKGIJ9d9h4eKfontaZWQuXBPJ33Y2L8hm7acQoufnv0Xba0HUUu9eavebdSEDby7+YWTSk/O/weTlFgZexUHsu5akzks8dh467db1KoayJIpuClOTePKd1iMOxuq+OHOz/A5LSTp4rmlQWrUPoM7WseT4iiyD+O7uaZoh0ALIhO4h/zryR4HMYLf11oNXXz4LZ17G51f7+Xxafyl3kXX5DpMeOJCeL5DYLeauHnu9bzZZ3b+1gQGcszCy674CYP9YVLEPjrkW28WOomEyvi0nlm7uXndDUuiiLPl+7kb8fc00VuTZvBb6YuHzcjvs3l5P5977O9rRKZ1It/FNzAwsixRYwc0dVzz/7XsbjszAlP5dnpN+EzQk+n1WXnocOvcVhfQ4C3gn/MuJPMoKFPbKIo8kLVx3zavBMJEn6WeRNLIwePkXGJAs9XvsnWjn1IkfBA+u3MDx/4/oIo8Hr9W3zVvhWAa2OvZmXUZQMqBjq7lv9UP0OTpR4viRfXx32POWGLB3zeOtNxPm583hORNEO1lEuibkfhdfrJQBRF6k2H2dv5Dk3mowBIkJIRtICC0BsIV4wsGcDm6kZjKaLFfJhW82F09upT7iHBRxrEaHYpp2DHKVr6XecjDSLKbyrRftOI8p2GUp4wIoXFLlgo1n/BQd2H9Djd+aa+XkFMC7mKqaorUXgNrBiXdu3n0+Z/0+M0IEXKfPXVLFFfP+Ac+NF8Ri2WVp4sfwadXYdKHsIjGQ8R7Ttwedno6OG3JX+nztRMsCyQJ7LvJ95v8MWSwd7DI4UvUGfWEO6j5Om8HxPlO3QEjlNw8avit9jeXopc6s1TU29nZmjqkI/pi42tx/j5kQ9wiQJXxU7j/3KuHFFI/akwOWzcvfstDusa8feW8+Lsm5keNn6eyKLOZu7Y/h4Gu4W0oHBeW7iKSL9zez79vL6MB3etxepykhyk4pXF3yHpHEXjnQ0IosjLxw7w5MEd2AUX4b7+PDn/EhbHjezY8U3EBPH8hmBHcx0Pbf+cNnMP3hIpD0+fz91TZuJ1AZcquu02Htj5GZub3Sfp+6bM4ae5889Z5yW4ie/vjmzgjSp3h/b9k+dz/+T541YuMTvt3Lv3PfZ01KDw8ub5WauYqx5bZ3yxvpEf7f8fJqeNgrAUnptx84gbiWwuB48c+R8HdFX4efnw9+nfZ7Jy6BOaKIr8q+oTVjdvR4KEhzNXsTxyYOUS3KTzH5Wvs63jAFKkPJhxO3PDpg94X6fg5KWal9mnO4AECbcmfpcl6kUD3re6V03rGUZNc4lONrS+yS7tmt7cz1Cujr2HtMCBLQFmZxcbNc9R2e2O8JHiTbZyGTNDryNEPvCEnhNwCBbaLMW09CqanbYKT1n+BELkyUT7TSfabyqRvnn4eI2uOU0QXehsVW4yazmMxlyE4xQi6uulcpNQv2lE+04jUBY9TJi6ndKuTezvfB+Dw03M/bxCuDj6QZIDBv5szc5u1rS8TLHBrVRFKOK5If6nRChOn3x2uiq9vFeVPl2R77R18mT5M7RaNQR6B/BQ+k9JCkgccBuMjh4eL3meGlMjQbIAnph8Pwn+g39Gens3Dxe+QIO5DbVPCE/n3Uuk79DkxiE4+UXhm+zSluEjlfHMtO8xTTVy8rC+5Si/OPIBAiLXxM3g11NWjo18Ou38eM877NPW4esl45+zbxy3KUcAFV0d3L7tbdosPcT4BfPawhtJDhpd1NeZ4phOw11ffUSruZtguYIXFlzFvKjEc7oN443Sznbu37qGSoN7YXdb1jR+OXPht3Lq0QTxvMBhdTr568HtvFLiDm9OCVbx3KLLL9hczhNo6DZw51cfUtGlxcfLm7/OvpQrk86tXcDmcvLwvs/4vPE4EuDXU5dze/rgXdGjhclh4wd73uZQZwN+3nL+NftG8sMSx/RcRw1N/Gjfa/Q4beSHJvPczJvx9RqZKmwXnDx65HX2dlbg6yXn2Wl3kBsy9HaIosiL1Z/xUdNWAB7MuIFLogb3aLpEF89VvM4Orbvb/cH07zEnbOByvM1l4x9V/+Ro1zG8JF78IPlOCkIHJj27tV/xfuOruEQXsb4J3JXyICr56eqVxdnDOw1PUd1TDMD0kIu4NPp2FF4DZ6LW9Ozny5ZnMLv0SPFiquoKZqiuJVA2dFlWZ6vmSOdr1PXsQMTV77ZgWZybAPpNJ8o3D1/v8R2tKYhOtNZyWsyHaLEcps1yFNcp+aFBslhyVDeRHnTJkN5QQXRRbtzOHu2bnkimXOVlLIy4e9Dye0nXXj5t+jcmlxG5VMEN8Q+SGXS6mj2wD/c+/AdIDzA6unmm4m/UmupQSH14IP1+JgUN7LHsdph4vOR5qk0NBHkH8Fj2fST5D67Y62xGHi58gUZLO5EKFU/l/ZgIxdDkcyzflb74vLmIXxd+hIDIdfEz+WX2yjEtYq0uB/ftfY+d7dX4SL35x6wbmB8xcgV2ODT2GLh92zvU9egIkfvyyoIbyA0derE13mi39PCDrR9zRNuCl0TCb2cu5daMgReqFwqsTgd/OrCN10rdww3SlKE8t+hyJoeeuxjA8wETxPMCRpmug59sXUuZvgOA72bm8euCxaOe3X2+YW9bAz/athq9zYLaN4D/LLqW3LBzM13jBLodNn6080P2tNchk0p5suAKVsaPLdJoIBjtVu7e7W4WCPD24aU5NzM1dGxpAyWGZn6w7zV6nFamqxJ5fuYt+HqPjHT2VXEUvSrO1GFUHFEUeblmLe83bgHggfTruCx6zqD3d4ku/lbxP3Zq3dE0D2d8n1mheQPet8fZw7MVf6eqpxq5VM79qT9mivL0GBtBFPik+W2+and3ok9VFnBzwt0DdkxrbS28UftHtPYW5FIF34m7n8nBA5Nku2BlW9t/KDKsBUAlj+eymEeJUAx9Utfbajnc+Rq1PVs81wV4R/ZTG/2HIa3jDZdgp91a0quIHqHdUuLpvg+URTFVdRupQSuGJKAOwcbOjlc5pFsNQIg8hkujf0aU78Dkz+Ts4p36p6k1HUOClEuibmNO2OUDkqsiw0Fer/unJ3ngntRHCfc5/QRscVl4ruJ5jneX4S3x5p7UHzA9ZOBFS4/TzOMlz1PVU0+Atz9PTL6PpIDBv1daWxcPF75As6WDKEUoT+X9GLVi6AWB1eXgZ32qA89N/z7Zw1QH+mJdUyG/LvoYEZEbEgr4+eSB7SPDwe5y8sD+D/hKU4FM6sWzM78z5gSMgdBpNfH97e9xVN+Kn7eMF+ZcO245xSOF1eXkl3u/4OOaEgC+mz6V385cesHHE21tquHh7V/QYTEhk0p5ZMYC7sqeeU6reV8nJojnBQhBFHm15BB/ObgNm8tFqMKPJ+dfzEXx47fi/brwbmURv963HqcoMEUVyX8WX0uk3/jkY44UWmsPd2x/jxK9Bn9vOf+a+x3mRo5fKUtvM3Pn7jcpNbQSLFPw8txbyB5jh+rxrhbu3vsq3U4r01QJPD/zFvy8R5Zc4BRc/Lr4bba1lyCXevP01NuZMYxvTRRFXq39nHcaNgFwf9p3WBkzd8j/8UzFq+zpPIK3xIuHM75PQejAUSl6u56nyp+lydKMv5cfD6b/hNTA07fHJbp4p/5l9uncntvLor7DisirBjx51/Qc5e36J7G4egiWhXFL4i+I8h34s2y1lPF5y5PoexW+aSFXMV99x6BZlQAGez1HOl+junszJ0ZgJgUsJi/0VkJ9zq/vo0MwU961lkLdm1hdesCtgE4NvY2UwKVDEtB60xG+aHmKHqcWCVJmh91MQdiqAUvkTsHBmpaXOajbCMAM1TJWRt85oO+zxdLAi9XurNUgbyU/TnuUaN/TSZxdcPCv6hc5rD+CBAl3JN3OgvB5A26ryWnmiZIXqOipI8Dbj8cm30dKwODEsMNq4KHC52m1dhLtG8bTeT8mzEc56P3B7Yd++PBrHNLX4O/twz+m38Wk4JE3+nzWdITfFq1GROTGxFn8LGvgiU3DwS64+FlvEsaZZv4OhB6HjR/v+oidbbV4S6T8NX8lVyaOPM90PCCKIv8u2cdfj2xFBGZHxPPPhVcTcg4bn84GOi1mfr7zSzY0VAEwOyqeZxZcSnTAN5u/wATxvODQZurmoe1fsKOlDoAlccn8df4lhPuOfozi+QSH4OJ3Bzfzerm7BHF5wiSenHPpOVdvG3r03LbtHRp69Kh8/HhlwQ3kqMYvtkRr7eH7u96gwtiOSu7Hf+fdSkbw2Mosx7ta+MG+1zA6LOSGxPPP/FvxHwXp7Nup++TU28gPHb6h6X+1X/Bm/QYAfpx6NVfFLhj0vg7ByTMVr7K3sxBviTc/y7yTmaqBszHbrR38tfwpOmxalLJgHsl4kFi/00/kDsHOa7UvUNx1EClSbkq4m4LQ+QM+5/7ODaxp/g8CLuL80rk54VECZaerWYLoYq/2HfZo30JEIMA7jEuiHyLBf/DO/C57Yy/h3OTxbiYGLGRq6O1jIpwuwYpDMI/qMRKJFLk0eNSExSFYOG74hGL921hdBsBtA5ga+j2SA5cMmuVpdXWzSfM8ZcatAEQqMrg0+meofE7/nERRZLd2LV+0/g8RgST/bG5KeGTAMH6jw8ALlX+mxdqIn5c/P0x9hCT/0/dFl+jitdrX2a7dCcCNcddzcdSKAbfV5LTwu9IXKO+uxd/Ll8cm30dq4Ome0xNot+p5qPB5NFYdsb7hPJX3Y0J9hm7K7J8AoeAfM+4iI2jk5ehPGg/xWPEnANycNJuHJ10yJvLpFAR+efhT1jQWI0XCH6dfyZXx45eDaXe5+Nn+NaxpcKuOv85bxvcyBvdyny1sbKzkgZ1rMDntxAco+c+ia8kIObdVhPGGKIq8W17M4/u2YOlNofnj3OWsTJ70dW/aWcUE8byA8EVdBT/f+SUGmxWFlze/LljMdzPzLvhcMJ3VzI+3f8KeNnfkxIO587lvypxz/rpK9Rq+t/1dtFYTsf7BvLbwJpICx6+bss1i5I6db1DToyVcEcCrc28lZYzzl0u7mvnB3tfodlrJUcbxz/xbCZCNLHakbzahTOLFX/JuZXb48Hmhb9St5/U6dxj4D1Ou5Nq4RYPe1yE4ear8FfbripFJvHk08y6mqwZWStqs7fy57K/o7HrUPmp+lvkg4QPkPFpdFv5T8ywV3SV4S7y5Pek+cpWnewhdoosvW19jt9YdlZSrnM/VsT8eMGdSb29mXfNf0FjdowMzghayNPJefL0GPoYY7c0c0b1GlXGjx8OZ4D+PaaF3EKoYeRKBKLrQ20ppt+yl3byXTmsxYm8ZfDTw845G7TsLtd8s1L75yL1GnmDhEMyUGlZTrHsbm+DOEFXKE9wENGDxoLFMx7u+YpPmeWxCD94SHxZF3E2ucuBycbnxEO81PINNsKCSR3Jr4i8JV5xOVM1OE/+ufpJaUyVyqQ93Jf+UzKDTFymiKPJe4wd8oXHHml0fey2XRV864HZanFaeKH2Bsu4a/Lx8+e3ke0kPTBz0/dBYdDxU+DztNj1xfmqeyv0xKp+hzyUmp42fHv4vRw31BMn8eGHGXaQGjtwW9FHDQX539FOAM1I+XaLAY0fW8mH9ESTA41NXcl3i4Aun0UIQRX5/ZCP/q3SPNv1B5mweyVl8zo/RZfp27tr6EY09Xfh7y3lm7uWsiB9b7Nz5hNouHT/ZupYirQaAa1In88TspQRewLnbQ2GCeF4AcAoCj+/dzOvHjwCQHRrBc4suJ1V5bjsNzwaaerq4cePbngPJs/MuZ3ncuT+QFHe2cMvWt+hx2skMVvPqwlXjNgIT3KPvbtz2XxpMOqJ8g3h13m0kBIyN1FZ3t3P77v/Q7bSSGxLPCzNvGTHpBPhzycd82rwfb4kXf8y9eUTTWD5u3Ma/qj8B4O7kK7gufuAYHHD7L58sf4W9nYXIJN78YtIPmBoy8P/Q2fU8UfIH9A49UYoofp75MEq58rT72QUbz1f+iVpTJT5SBXelPEhG4OklRafg4J36JynrdjfbLY24iUXqawchRdv5ouUpnKINH6k/SyPvZVLwkgG30+4ycbDzPxw3fOIhnHH+c5geegdhipGF/LsEK22WPbT0bKbVvAOHMLrA+OEhQeWTTbT/RcQEXIS/bGSlX7tgplT/IUf172IT3FmeIfJk5kY8RKRvzoCPMTra+bLlaRrMhQCkBszm0pifD9h41Gat543aP6F3tKOQ+nFb0m8GnPVuc1l5ueZvlHUfxUvixd3JD5EVPLBy90nzZ6xudhO2m+JXsSJy2YD3szit/P74Pyk1VuPnpeD3U346ZMNRq0XLQ4Uv0GEzkOAXwXPTHsDfe+jvlslp5f6D7ilfSpk/LxfcQ4zfyI/NHzcc5Ile8nlr8lwenHTxiB/bF4Io8ofiL3i7xk0O/zDtCq5JGHyIw2ghiiIvlu3hyWL35KqbUqbxxPSLzzn51Nss3LNttUeo+M2Mi/j+pPFr+vy64BBc/P3Ibp4v2osgisQGBPPq8mtJDxk66utCxATxPM9hcTq476s1bGyoQgK9s1/nIfe6sM3VAG3mbq7f8Bb13YavtXTSYurimk2v0mE1kR8ez0vzriNwHEOLnYLAXbvfZG9HLTF+Sv437zZi/JVjei6T08bNO/9NnUk76vI6wJrmA/yx5COkSPhD7s0jmj99rKuGh468gIDAHUmXcWPC0iHv/1HTet6s/wyZxJtfTvoheSEDl42cgpM/Hv8L1aYaohVR/HzSIwTLTlfsRFHktboXOKzfg5+XPz9KfZRE/9ObHATRxQcNz1HctRNviZzr4u4nWzlw09PBzo/Z2v4iAHF+uVwS/TBBstOn2IiiSF3Pdva0/w2zSwtArN8spoV+D7Xv8ITdIZjQmHbQYtqMxrwTl2j13CaTBhLuOxO1bwFqv1n4e8eN6iTuFMxoLYdot+yj3bwXo6N/NmiwPIOYgIuI9l9CkHz4phC7q4cSw4cc1b+HXegBIDP4CmaG/XDAqCdRFDis/4Tt7f/FJTqIVKRzddwT+A/QrW9ydvFW3V+oN5fh6xXAXSm/GzBuySE4eL3uBQoNB1BIfXko8wkiFQNbXT5tXsPHzZ8gQcKjmQ8P2u1uddn4fem/KDFWEqUI58ncR/H3Htwf2GLR8uCR5+m0d7FIPZVfTrpl2M+l22HhvoMvU97dTGZQDP+e+cMR5+dC/7L7X6Zez4rosY0BFkWRvx7b8P/snXWUHYX1xz8z89z2rXt24y6EhECCBHcp7lKkLaXyg1JKW6hQSluqtNSQ4g7FLWgIkEDcPZt1l+c68/tj3iRp3jzZ7G52SfZ7zp6Tk7kj772R79x7v9/LI1sXYxQlHj/q6n41mQd4fvtKfrz0TWRF4bbpx3HDhNRjbgcKUTnOr5a+z6OJ1qxfzTmZy8f1H8keTHzZUs/3P3qDel8PLpOZh088j9kl+3dC30BjmHgOYXSFgnx9wYssb23ELEn8Zf6ZnFr91S8rAHSEAlz87lNs6Wmn0pHD8ydfvt9FRKA2z1/0/mNs7GllfE4Rzx1/JY5+mruu4fdrF/DQls+wSUaemX8dY13px/SlgqIo3LbiOd5tWkuxxcXTR95Injn73t5NngZu+OIfROQY3xhzEleP0s/u7YnuiI9vLf097ZEeji2aye0TL0/7EF7TvZmfr7sPGYUbR1/KiSWphUeP1zzJe60fYJNs/GLynRRZ9F863ml+hdcbn0NE4qaxtzPWmUxkFUXh1cZ/80XHO0iCgcurf8Q4Z3KpUVbifNTyb5Z3vQzAIblncWzxN3X7GmUlzuK2+1jf/RKgCnHmFd1CuT21QT6oZLPR9z4N/vdpDS5G3sPSyGooodx+PGX248mzTN2ncZepEIy10uT/iAb/+7QHl/2PnZPTOJIy+/FUOE4ix5y+JSAU7+GLtn+w2aO2KjgMpZxS8XvcJn2BTmNgPf+t/xnBuIccYynnVf5Kt+8zIof4z/ZfUBvYhNOQyw2j7ybPnGz7FpNj/HXLr9nu30ShuYQfjP8lNkPyea4oCg/ueJhF7Z/hMrj4xZQ7yTPpK9I9UR8/WPUb2sJdzMmbzm0Trk97Hq/r2cHNK/6GjJxRQKehOdjNVYvvwxMNcE7FHG6b9LWM6+yJ+za+y8PbPsEmmXjyyG8y0rFvL+GKovC9L55nQeMGii1OXjj2Bgos+gMA9hWPbP6Su1a8iwD8fd75nFSxb6N9+wJFUfjdio/5x7rFCMAf5p3BuaP2r/BpoHAgP/uhd3ztq+tE/hVEvbeH815/kuWtjbhMZp445cID5sTriYS48v1n2dLTTonNyVMnXDIopDMuy/zf4pfZ2NNKgcXOA0dd2O+k892G9Ty05TMAfjXz7H0mnQBP1Szm3aa1GASJe2de3CvS6Y0G+cmqJ4nIMeYWTODKkfMzrhNXZO7Z8DjtkR4qrIV8f9wFaR/WHeFu/rD5YWQUjis6nBOKU1ssfda+mPdaVeuhb4y6LiXpXN29lNcbnwPggsqrdEknwHstT/NFxzsICJxf+V1d0hlXYrzecM8u0nlM0XUcV3yjLumMyWE+aPrZLtI5Pe9yzq16JCPpbPC9z4Lac1jW9jOaAwuRlQgOYxXj3F/n2PInOGXEm0wr+AEF1kP6lXQCWA1FjMq5kKPK/sVp1QuYWfgzim1HImDAG93Bpu4Heb/+Qpa33kUkMR5TDxYph6NLfsTpFX/FaSzDF2vitdobaQ2u040vs03ikqo/kWMspSfaxNM7/4/m4OakOJNo4YrqH1NsGYE31sV/dvwSb7QrKc4gGrhu1PfINebTFm7mPzvuI67Ek+IEQeDKqssZYavEE/Nw/9Z/EJP1+2RdRge3jr8eg2BgSecqXml8P+XnB5icM5LrRp0BwD+2/pdNnswjTUusbn4+9SIEBF6uX8KbjcsyrrMnbhx3PLPzRxKIR7hl2dMEYuFera9BEAR+PfNsRjkKaAl5ufnLF4jJcuYVe4Grxs7istEzUYCbF7/C2s6mft1+NhAEgR8ecgxXjVeP4wefvcFbOzfu9+MYCORarDx16kWcOGIM4Xicb73/Mo8nWu0ONgwTz/2EDZ2tnPv6k2zr6aTU7uTFMy7jsJJ983gcavBHI1zzwfOs62yhwGLjyRMuptLpHpRj+e2qD/igcStmycC/jryAcnv/jhbd6mnj9uVq79Y1Y47g1Ip9tzlZ2VnLnzaowp5bJp7CtNzszwdZkfnl2udoCHZSasnlZ1P1Z53vjSdr3mV512bMopE7J1+DLU2vW0yO8/tND9ET9VJtK+eGURelJKn1gXr+U/MoAGeVncGMXP0+vsZgHY/V/AOAowpO4MjC43XjPm17jY9aVTPyM8tvYJo72WZHUWTebvw9m72fIAlGzii7ndn5+kQ6HPfydsMt1Pg+RhSMHFv6c2YXfANDGlulYKyVxc23sKTlB4Ti7dgM5UzM/SbHVzzPiZX/ZUr+d8i1TN5v/XBmKZdq1znMK/0rZ1R/wOyiX1Nqmw9Ajfcl3qs7lwZfevJVapvBWZX/pNA8kbDcwxv132Onb5FubJ65gkur/0yJZRzBuIcX6n5Me7gmKc5mcHL1yDvJNRXTGWnmkR13EYz7k+KcxhxuGH0zJtHMRu9aXml4OsXnNHPTmBuxSVa2+rbxTN1zKT/PWGcV1448D4DHa15hXc+WtJ///Mr5zCuYSlSJc9e6R/BEk49zbxxRMJ5rR6vn6e/Wv8xWb/aEzCBK/OaQCyk0O9nua+OuNa+yr0VGh9HMXw+/CJvBxJftO/nDugX7tJ1UEASBO2eezFElowjGo9yw6DmaA/3ds5zdcfxs9olcMHoqsqLw3UWv8mHD3uNov5qwGoz88/hzuHT8dBTgp58t4N6ln+zzOfFVxTDx3A9Y1LiTC15/ipaAj/G5Bfz3zMsPmObiUCzKdR++wPK2BnJMFh4/4WJG5wyOQOrpbct5aLM6tu/ew85kRj9P5fBFw3x3ybMEYhEOK6jm5snp+yLToTPs44crniWmyJxSNpWLq+f0av0nahayqG0DJtHAr2dchsuYPJN8byzt3LjLNul74y5gpCO9UvfxnS8n1MMWfjjhOswppiYFYgHu2/J3InKEya5JfK38bN04f8zLv7f9gbAcYqxjEudVXqEbt6LrQ95s+g+gConm5Cfb6yiKwvstf2eD50NEJM4qv4MJOfN1t+eLtvBa3Y00B1dhFO2cUv57Rjv1Ca+6bZkdnhdZUHcejf4PEDAw3n0tJ1a+yMS8b5BjHjPorhNGyUml81SOKP0TR5c9iMNYRSjezpKWH7C4+RaCsdaU61oNuZxW+Rcq7YcTV8K81/gTNna/qhtrN7i5sOp3lFomEIp7eaH2x3RHmpPiXMY8rhn5MxwGN82hGh7fcTcROTm7V2Gr5oqqbwLwYetbLO74WHe/xZYibhh1HQALWt7n844lKT/PySVHcXThbGRk/rDpYboiPSljBUHgB+MvodSST0u4i3s3PoWsZM4cXjPqOObkjyMsR7l91RP4oqGM62jINzv43cyLkASRtxpX89zOL7Jed2+MchZwz8xzAHhk62LeqF+7z9vSg0EUue+IrzHGVUBL0McNi54nEItkXrGfIQoCvzn8VM6omkhUlvnmx//l8+ad+/04BgIGUeTX807i5pnqy/TfVn3ODz55i0g8uQJwoGKYeA4wXtiylqvefh5vNMKckgqeP/1SSu37vwQ9EIjE49yYsExyGE08dvxFTMzd97JzX/Bp8w5+tkzNHn5/ytGcPqJ/R3HGFZlbvnyRHb4OSqwu/njY+RjEfbt84orM7SuepzXkYaS9gDunnt0rIrOscxv/2qJaz9wy4SwmuDI3qbeGurhn/RMoKJxeegQnlqRXjH7evoJXG9Wy+XfHXkmpVf93VXvy/kNLuIU8Ux7fGn2DbuY1rsR4aPt9dETayDcVcu2o7+oalW/wfMlLdfcDMK/gTOYXnae730/bHmNl12uAwGllP2S0U5+4d4a38Wrdt+iO1GCTCjiz8n7KbKktabyRGj5pvJ4Vbb8iJvvINU/muIonmZx/E1Ka7KgeFCVOXA708q/3pdgC66EcX/Es43OvQ8BAo/8DFtSdx/aeF1BSkCqjaOXEsnsY5zodBZlFrfeyrP0h3cyLSbRy7oi7KDBX44t18Hztj/BFO5Li8s0lXDPyTiySnZ2BjTy9817iSnKZfEbuYZxaci4Az9Y+zHZfcgkf4JDcGZxZejoAD+94hPpAvW6cIAh8a/QlVNpK6Yp6+MOm/+iW8TU4jFbunHw1RsHA4o71PFf7QcpYDaIg8vOpF1FicVMf6OCudc/1Kkt1SF4V359wEgD3rn+LNV11Wa+7N04qn8h1Y9X+1J8uf4V1XY37vC09uEwWHjjqQvLMNtZ1NXPL4leJ93NZPxtIosifjjyDEyrGEI7HuDaR4DgQIAgC3ztkLr898hQkQeCFLWu55t0X8ET2rRXjq4Zh4jlAUBSFv6z4lFsWvklMkTlr1EQeP+VCcsz9p6weTMRkmf/79DU+aNiGRTLw0LHn7/cRmBo2dLVw46cvEFcUzqmawk2T9Kef9AW/X7uAhS1b1BnKcy4ivxe9mHvjH5s/YEnHdqySid8feknWU4kAWkM93LH6aWQUTi87lDPLM1uOROUYd69/DE/MzxhHOTeOSS+QaAi28NetTwBwTvkJKacSAbzZ/DbLupZjEAzcNOZbOI36L1Uv1j/BFt96zKKFG0bfojvDuzG4nWd3/hEZmUNyj+WU0qt0CfnSjhdZ3PEUACeU3JQy09kYWMHrdTcRiLXhNlVz1oh/kmfWV4LLSpSNXQ/yfv1FtIeWIwkWpuX/gPnlj5Jjzr4PW1YitAc+Zn3bbSysPYyPaw/p5d80ljZeSG3PI4RjLVnvVxLNTM77NsdVPEWueQox2cfK9rtZ2Hgd3kiN7jqiYOCo4ts4JO9qAFZ0PsInLb9F1iGLVsnF+ZW/3tXz+ULdTwjGk8uwJdZqrqz+MUbBxGbvct5ofFh336eUfo0Z7tnElBgPbv8TnZF23bhzK85hsmsSETnCX7f+nWA8qBtnkczcNv56LKKZdZ4tPLnzNd04DWOcFdw0Vn2p+c+ON1nVtTVtPIDbZOfu6ZdhFCQWtq7nqZ2fZFxnT1w+ci4nlEwipsS5dfmzdEUyl/lT4fuTj+Oo4jGE4jFuXPwMrcHU/b37ghGOXP4x73xMosS7DZv41coFg1IONooSfzv6HOaVVBGIRbn6g+dY15n9dTHUcfH4aTx04nnYDEYWNe7k/NefpNG3/9sb9jeGiecAIBKPc+snb/HH5Z8C8O3ph/OX+WdglvpXeDBYkBWF2z5/kzd2bsQkSvxr/rnMKc5+rnF/osHfw9cXPoMvFmFOURW/nr1vM5LT4YWa5TyydTEA9xx6zj6PwgRY2LKJB7eq5cU7p57NaGf2GWJtHGZXxMdYZyk/mKg/UnJvPLj9NdZ7arBLFu6cfDWmNJYwoXiY3218kGA8xCTXGC6vOitl7AbPRp6vexGAy6ouYbRDfx78orb3+aRtAQICV1XfSJk1uZfVE+3kiZp7iCphxjhm8LWKG3Uzp2u63+Gj1n8DcFThNczIPUN3n9u9H/B2wy1EZB/F1mmcUXk/DqP+NKnO0Bo+qL+U9Z33IysRiqxzOaHyRca4L0NIMfFnT8hKjM7gZ2xo/ymL6o5kdesNNPtfJq74Mq6rB09kFVu77uHT+mNY1nQZ9Z4nicT1idneyDGPZX75I0zLvxVJsNIRWsH7dReysesBZCWaFC8IAocWXMuRRbciILLZ8wYLGm8nKicTPIcxnwtG3IPdkEd7eAcv1d1JRCeuyj6Ri0bcjIDAko63+bz9zaQYURC5vOqblFtH4I15eGDbn3RL86Ig8s3RN5BnyqM51MKD2x9OSYDKbcXcNPZyAP7bsIAlHavSflenls7hxOLZyCjcvf4xOsKpS/QaJuVU8v0JZwLwjy1vs7xze8Z1NAiCwM+nfY0R9nyaQz38ZOULxLMo8+tBEkT+MPs8RjsLaA15+fbiZwjGkn/fvmBWYSW/m6N+1se2LOWBTYv7dfvZwiIZeGD+ecwqrMATCXPle8+ytSe76+GrgGMrR/H86ZdSZLOzqaudc157gnUdBw651sOwnVI/wxMJ8633X2ZR404kQeBXc0/k0gkzBvuw+g2KonDHF+/yxOYVSILA34/+2qBNmegOB7nw/UfZ5u1gXE4hzx53Ja5+9OoE+LJ9J9cueoyoIvPtCcdw08T5+7ythkAXlyz6B55okIur5vCjKfqkKRX+vPE1nq39FIfBwsOH30SlLXOf8MLWldy1XhX9/GLKtcwtSG1NoigK9215jI/avsBtdPKHGbeTZ9IXZ3VGuvjZ2l/iiXmYlz+X60d9XZcEb/Vu4K9b7kEmzpllF3JSSXL/Z1QO8+C2O6kPbqHQXM43xvwGq5ScUd7k+YTXG36NgszsvAs4uuha3X2u7XqexW1/BRSqHccwv+QOXRFRTA6wrvN+tvU8DSiYRDfTCm6l0pF5zKGiyHSHl9Hqf5NW/ztE5d2lZ5NYQJH9FIrsp+I0TQJ64+PpoS2wgBb/m/SE91RQi+Ra5lBkP40i24kYJX2LoT3hjzaysu1uWoKqA4PLNIaZhXeSZ9H3k9zpW8QHTT8nroQpNE/kpPLfYtXx72wL1fDszlsIyT6q7DP5WsUvMOhMkPqk9WXebn4MAZGrRv6Usc4ZSTGd4Tbu3XQnvpiHQ9xzuGbkd3S/+22+7dy94TfElTgXVV7AaaWpDdkf2v4Crzd9iE2y8vvpt1FqTW1hFIpH+O7yP7PD38TUnNHcO/1bSGL6lw1FUfjl2ud4u2kFeSYHjx7xXQoyTEPaE1s8zVzx6b8JyVG+OfZYvjkuswVaKtT6OrnwowfpiQY5tXwyf5itP1ihL3ho0xJ+vfI9AP445+z9PtddgycS4tIFT7O2s4Viq4PnTr6MKmfm6+Crggafh6vfeYHN3e3YjUb+ftzZzK/Qf5kfihi2UxokNPo8XPD6Uyxq3InNYOShE8874Ejn3cs+4InN6gi3wRxtFopFuf6T59jm7aDE6uThoy/ud9JZ5+/iu0ueJarInFI+iRsnHLPP2wrHo/xg2TN4okGmuiu4ZVLvJpm817yaZ2vVDPodUy7IinTWB1r5w6ZnALiw8ri0pBPg3ZZFfNT2BSIit4y/NiXpjMkx7t/6DzwxD5XWCq6q1vcB7Qi38dCOvyATZ2buEZxYnJw9VRSFF+v+Rn1wC1bJwRXVP9YlnTW+ZbzR8BsUZKa5T9UlnYois6Ttfha33QcoTMo5l+NKf6FLOpv9i1hQdx7bep4CFCodp3PiiBcZ4Uw93lBRFHpCK9nS+Ws+rT+GFc2X0+B9iqjcgVF0U+a4iEOKH2Ve5ULG5d+B2zILSbQhidas/8yGYipcl3No6VPMrfiIMbk/wmmaCsh0hT5nU8cdLKo7klUt19Pk+y8xOXWJ1W4sY27p35hd9GtMohtPZCsfNVzFqvbfEdOZIV/lOJLTKv6MWcyhLbyB1+puxBNJ7qkrtFRz3oi7MQoWdvqX80bjb5B1eiqPLDybmbnHoSDz9M7f0xpK7tHMMxdy3ajvIwkSK7qX8E7zy7qfZbRjFJeNuASA5+peYIMntcXOldXnMN45kkA8yO82PUA4nlocY5FM3Dn5amySmTU92/jPjuTs7N4QBIHbJn2NUY5iOiM+7lj1FDE5e2HIWFcJP52qXgv/2vIRn7amV+KnwwhHHvfNuRCDIPJWwzr+vmnhPm8rFa4dP4drx6k91Ld9+RqfNu/o931kA5fJwmPHX8S4HFX4dOmCp6n3Zc5Sf1VQ7nDxwhmXMrd0BP5olK+/+yJPb0qftf+qYph49hPWdbRwzmtPsLGrjSKbnedOv4RjK786byuZoCgK9yz/kAc3qKPbfn34KZw9ct+thPqCuCzz/cUvs7yjHpfRwn+OuYRSW/9mxH3RMN/6/Gm6I0Emu0v59cxzEPuQSfjd+jfZ4GnEbbTxu5kXYRSzb7uo8bdyzzrVWuiK6mM4uijz9x6KR/jlukcIxMNMzRnN10fqz77WsNW7kwe3q/u4vOospuSkNiR/pu45tvq2YZOsfGfstzFLycQuIkd4cPuf8cW8VNpGclmVvrn3B63PsabnU0QkLqu6jXxzcp9wQ2AdL9f/ApkY451Hc0JJclYsrkT5qPlXrOlSifbsgm9yRNH3k/w8FUVhbcd9fNb8HYKxZmyGUuaV3s/s4l9hlvTHncpKjNqeR/i8/niWNV9EnedRIvFWDIKTEse5TC9+kHmVi5hQ8EtyrYdnVZ7PBhZDKSNyrmF22QscUf4eo9y34DBNRCFGR3AhG9p/xCe1R7Cm9bsEo/piFUEQqHSeyokjXmKE43RAYVvP03xQfyn+aDKpLLZO4cwRf8dhKMUTrefVum/RFkomeaXWCZxT+XMkwcgW76e82/TnJCGTIAicXf4Nqu2TCMsBHq+5G38suX9ttGM8F1ZeA8CbTS+yrmel7mc5rmg+c/OPQEHh71v/RWck2S8UwCga+MH4a3EZHNT4G3hge2o7JoAKWxE3j78YgGfrPuCz9sxKcYtk4p7pl2OTzKzsruEfCbFftjijYgbnj5iNgsKPVz5PU7C7V+vvicMKq7ljunp9/23DR7zdoO/N2hf8aMbxnF45iags861PX2BdV7Kzwf5AnsXGEydcTLUzlwa/h0sWPEWD/8AhnzlmC4+efAHnjZlMXFH40aJ3+N3ShQec3dIw8ewHfFy/gwveUO2SxrkLePnMK5hakDy946sKRVH47YqP+fd61QbkV3NO5pKxMwbtWH654l0WNGxW+0uPvIBxOf07klNTsG/ztlFocXD/4RdjNWQ/Km9vvFq/ghdrlyIg8OtDLqDU6s563UAszI9XPkEgHmFm7ihuGHNSxnUUReG+zS+ww99ErtHJTyZdkbZ86I36+d2mB4kpMQ7Lm8Y55altoj7vWMKCFtUr8oZR11Fs0R9J+UztQ9QHa3AYXFw/6v8w6WQdV3cv4oOWZwE4u+IbjHQkE+q20HZeqruTmBKm2j6L08p/mEQmI3E/7zTcyjbvAgQkjin5CdPzLksip7ISZVnrHWzuVq2aRudcygmVL1BsS22K74tsZlnTxWztuodQvAFJsFFsP4NpRf/gyBGfMangHvKtRyEK+35+ZAOrsZJq9w0cVvYyc8rfYqT7O9iMo1GI0hZ4hyWNZ1Lb80hKFbtZymVW8a+YV/p3rFIxvuhOPmq4iu5wMql0m0Zw1oi/k28eSyjexRt136XOn2xnVGU/hDPKf4yAyNqed/mo9YGkB6RBNHJp1Q8THp8tPLXzd8Tk5F7EuQXHcmTBCSgoPFpzP22hZGIjCAJXV19BpbUiYS7/z5Tm8gXmXG4efw0CAu+3fs57LZ/pxmk4pmgGXys/GoB7Nz5FUzBzD+EIeyF3TLkAgKd2LuSjlt5ZG9066VQm5ZTREw1y6/JnicT1P0s2uHDkoVw5Ws1K3r7s5X5XuouCwL1zzmROURX+WIRrFz5Dvb+7X/eRLYpsDp4+8RKqnG7qfD1c8u7TNPoPHEGOSZL4w9Gn8b1D1PvS/asW872PXyfch/NjqGGYePYRnzfVcu2CF/FHo8wtHcELZ1xKueOr0Y+aLZ7cspJ/rlMby+867KRBnZ/735o1PLF1mTpO7fCzOayo/0VNj25dvEvBfv/hF1Ns3fffsyXk4ddrVYXtN8bOZ27hmF6t/6+t77DD30qB2cld0y7BkKH/DODzjnUsaPkSEYEfT7qCfHN6E/2Hd7xIW7iTEksB3x2beoZ1d6SbR3ao/aJnlp7OIbkzdOOWdy3my85FiIhcM/Imck3Jvq5dkdZdtklHFpzFrLxkshuO+3mp7meEZR/l1smcXXEH0l4ET1ZiLGj8MY2BZRgFKyeX/46xruQ2BkVRWN52F7W+NxCQmFn4c6YX3IpBTO1/2up/m6VNF+CNrMEgOBmf/wuOrPycyYV/oMB2HKKg72s60LAbRzHSfRNzyt5gdunLuC2HIStBtnbdw9q27xLXKaNrKLYdwfyKx8gxjSUc7+CTxm/giyT7I9oMBZxe8VfKbLOIKUEWNN5OS3BNUtxY51xOKb0ZgGWdL7GuJ9nU3G5wcWX1jzGLNmr863e9bOyN8yquoNo+hmA8wGM1f9f11zRLZr4zVjOX38obTW+l/KzT3RO4ZIRqx/TA9udoD+tnSDVcP/pMJrqq8MWC/H7jM1llmeYXT+HSKpWw3r3uBbp7oVQ3S0bunXkxLqOVtd31PLhN39M0W9w65aRdSvfvf/E8oXj/io3MkoF/zjuf8TlFtIX8fPvTF/t9elK2KLW7eObES6lyuqn1dfP1D54n1M/iqsGEIAjcPPNI7j3qVAyCyCvbNnDzx28gHyCZz2Hi2Qds6e7ghvf+S1SWOaV6HI+efMEBY5ekYV1nC7/8Um0s/+Ehx3DF+NQeiAON1qCXu1aoD7b/m3oMp1Xqj1rsC5oCPdy/4SMAfjL9VKbm9s2E/s8b3iEUjzI9dwQ3jJ3fq3Vr/W28WKcS/jumXEieObP/a0yO8+A2leheUHksM3LTz/De5qvlozY1m/V/467GbkhNxF6of4mQHGaUfSTnVpyjGxOOh/hvw5MAnFx6DuOc+m0Brzc+RFQJU22fxMml+kby77f8HW+slRxjKV+r/CVGMfnaWtn5OE3B5RgFK6dV3keF/TDdbW3u/g+13tcQkDi85A9Uu/RN7kElqTXd/2Bt2/eQlRB5liOZU/4G5c6LkXSOYbAgCAJO80QOKX6U8Xk/R8BIW2ABy5ovJRRLXQq1Goo4uuwhcs1TiMoePmv+HhEdaySTZOfk8t9RZT8KWYnyYdMvCOuM5ZzsPpEjC68C4IOWf9CjYzBfZKnkvMqbAFjU/qpuv6dBNPD1kd/FLFqoCWzji059u6JiSzFXVqnq9deb3kxZcgc4r+JkJrpGE5GjGS2WjKKBn0y6EpNoZHXPNhZ3ZFey/tbYkxnrLMUXC/Hw9vSTo/ZGuS13V7/no9sW0RBIT47TwSCqSvdii5P6QDcPbNafSNUXuEwWHjr6InJMFtZ2NfPQICndQSWfT594KQUWGxu72/jl0t59918FXDhuKg+ddC5GUeT1HZv43dL+7+EdDAwTz31Ee9C/y/B1ZlEZfznmdExS//R2DRX4omG+88krROQ4x5eP5luTDx+0Y1EUhTuXvY0nGmJKbgnfmJC6PNoX/GbNOwTiUWbmVXJeVd8yu0vat/FW42oEBG6bfFpWIy33xP2b3yKuyMwtmMBh+ekJpIY3mz6nLtiK2+jgkqoT08YqisIjO9S55UcXzmacc2TK2O2+HSxqV8uVl1VdkvKzvNP8Mj3RLgpMRZxYfKZuzIaeL9jo+RIRibPLv6k7V32zZxHre95DQOS0sluxSI6kmJbgGlZ0PALAvOJbKbRM0N1fg+991nX+FYDpBT+k1J5aJBaXw6xvv5Xt3X8GoMJ5JdOK/4XZoG/FlA6h6Ha8oc969ecLfYGs9M5EWhBEyl2XMLPkcYxiPr7IBpY2nY8nvDrlOkbJyRElf8JqKMEX3cmSllt17ZYkwcgxpT9NzHdv4dPWP+hmAg/Lv4hy62QicoC3mv6gW/KfnHM4E5yziCsxXm34t+52ck35nFqqmsu/2vAMgZh+BvHw/DmMcYwhIkd4ru6FlJ9TFESurla393HbF2zzpZ/PXmzJ49wKNYP5wPbXiGchGjKIEt8Zp2ZWX6pbTK2/LeM6e+LEksnMyhtJWI7xm3Wv96mfz2m0cPs0NeP/wOZPqfElG/33FaU2Fz+dod5b/rx2Ids8g2dtVGZ38ad5ZyIAT21ZyWs1GwbtWAYK8ytG8dsj1d/0H6uX8PTGr77gaJh47gOCsSjXLXiJOm8PVU43D55wLpY+9AAORSiKwk+XvMt2TyelNie/n3fGoI4JfLNuAwsaNmMQRH572Bn7PDUoHT5p2cq7jRuQBIE7Z5zeJzFROB7l7kSJ/aKqw5iU07vM6fLO7SxsW48kiHxnXHphkAZ/LMTjNarI4Yrqk7GnmcMOsLRrLWs9WzAKBi4boU8SQZ0L/8TOp1BQmJt/BGMc+ibsraEmPmhVVcHnVV6BUcdiJyKHeL3xQUBVPRdZkqcu+WOdLGi+D4DZ+RdQbkvOmkbifj5qvgsFmdHOExnj0ifZXeH1LG39KQCjcy5hVM6FKT9nJN7ByparaPGrmdHx+b9gXP5PEHUmLKWCosh0BxawueVi1jfNZ0vrxb3629x6PmsbjqCp589Es/Tu1JBjOYRZZc9jN44jEm9jefPltPpTl6IthgLmlvwFg2CjLfgFK9t/m2JykY1jS+5EQGK79322et9NihEFiVPLfoBRsFAfWM2yzv/q7vOM8uswCiZ2+Neyqls/e3NM4ckUm8vwxjy81fSSbowgCFxedQkCAp93LGazN7UyfJyzmqMKZqGg8EjNfzMSu4tHHE+O0U5doJU3m7LL6M3OH8PcggnEFZn7N6f+zlN9lh9POQODIPFJ62Y+bOkbeTqpbCLzikYTlePcveqtARGmfK16KkeXjCIix7nti9cHZbKRhqPKRnLjlCMAuH3xW+z07nvWeKjivLFT+H6i5/Mnn73Lx/WD4yzQXxgmnr1EXJb53kevs6KtCbfZwiMnn0++NfOc7K8ant+2mpd3rEMSBO476ixyzdZBO5aOkJ+fL1cJ1Y2T5jHB3fvsUyaE4zF+tUolTZePmsP4nL7t45Hti6j1d1BgdvDt8b2b6S4rMvdtegOAs8sPo9qRncn8s7Xv0x31UWEt5LTSI9LGxuQ4j9ao5OCMsmMpsiT3YWr4tP0ztvm3YxHNXFR5vm6Moii8UP8YcSXOJNcMpuTot2R82PI83dF23MZCji2+QHc77zb9hWC8h0LzSOYWXK67nc9a/4Q32oTDUMq8opt1YwKxFj5v+h5xJUSxbR5T8/XjAHyRTSxtvICe8AoMoovpxQ9R7rw4ZfzeiMsB2ryPsb7pOLa3X4sv/BkgYTGOxWIcl/WfQcwnJrfT1PNH1jYcwc6OHxKMbMr6OKyGcg4tfZp863xkJczatu+zo/v+lOQjxzyO2cW/AURqPC+ytecJ3bgi62Rm5l8NwGetf8QbTRavuE1lzC/+BgCftP2H9nBNUkyuqWjX7/5m0yME48kZTYNo4PzKKwFY2PYujUH9LOVIezVHFx4FwOM7n0w7c/3yqrMwCgbW9mxmWVf6ErrdYOXyqpMBeKzmbfyx7Oayf2fcaUiCyMK29b0ylgcY5Szi6tHqxLXfrnsDf2zfRycKgsBPp5+KUZRY1LqNdxv7PwsoCAJ3zzoNh8HEio4GHtu6tN/30Rv83/SjmF1UgS8a4dsLXz6ghDgavn/IPM5NqN1v/OCVr7TJ/DDx7AUUReGOzxfwzs4tmESJB074GqNy9C1YvsrY3N3GnV+ovZQ3Tz+a2UXJk2b2J+5asYDOcIBxOYV8a+K8AdnHg1s+pdbfRaHF0SeTeIBafwcPbVWzObdOOg2nsXd9ge82rWKTtwGbZOba0dmR1tZQFy/Wq+KE60efmVGE9F7LpzQEW3AZHJxXcXLKuEAswPP16nSis8vPwm1y68at6VnGBs9qDIKB8yr0ezZbQrUsansVUDNfekr3tT3vsM23GEkwclrZD3WNybd53mOr9x0EROaX/hSTThk+Jgf4vOl7hOLtuIyjOaz4Nykzl+2Bj1jWdDGheANWQxWHlj5LnjU9cdcQiTXR0H0PaxvnUNf1U8Kx7UiCiyLnN5hStohJpe8zqfS9rP+mln9Bdf5fsZmmoxCmw/8MG5pPZEvrZfQEP0ypWt8TBtHBtKK/U+lSLYp2dN/H+vYfpJwDX2o/imkJUr6m4080+fVFLtPzrqDYOo2oHODDprt0R2tOc5/KSPthxJUobzbcS1ynfD+v4CwKzRX4Yz0saH5Sd18TXFOZ7p6NjMzzdY+lJM7nV5yLTbJSG6jj47bU/W9FlnzOKDsWgEdr/pt2ljvAGWVzKbcW0h31ZTXLHaDaUcTZ5WqP8V83v5GWCOvhujHHUGHLpSXk4Z+bs9tn6mPJ3zXP/Z7Vb+OP9v8M8DJ7DrdNPx6A36/+kJ2+wcs0GkSR+45UEyRrO1u4Z/mHg3YsAwVBEPjtkadwROkIfNEIV73zArWe7sE+rH3CMPHsBf6y4jOe3LgKAfjz/DM4rGRwCdlAIBiL8u2FrxCKxziqdCTfmjJ4fZ0ACxo28VrtOkRB4LeHnTEgfbS1vk7+vUkVMvxo6sk4jNnPTt8biqJwz9rXicgxjigYw0mlvZvyEYxF+OfWtwG4atSx5JmTSZUeHtnxFhE5ytScURyRn36fvliAp2pfB+CiEadiN6TOZr/S+Bo9UQ8llmJOKtYnwRE5wov1aqbsuKLTKbIkW4kpisKrDf9GJs5E12FMdCXPmPdFO/iw5V8AzC24gkJLsg+uN9rMp61/AGBG3pWUWKfp7Evmy5af0BPZhFnM5YjS+zCKyd+joijU9jzC6tZvEVcCuC2HMav0WezGzP67/vAqdrR/h7WN82jx/IO43IPZUEVF7i+YUr6EityfYDL0XpgmCEby7GczvvhVxhW/iNt6KiDiDX3CtrarWN90Am3eJ5B1RlX+73Ykxub9iPH5dyFgoMX/OiuaryAc0+8/HJ1zKSNd5wEKX7TcTnc4OcsqChLzS36KUbTTGlrLys7HdfYrcHLp97FITlrDW/myI7n/0iAaOav8BgC+6HiH+oB+mfzc8sswCka2+jawvEu/5O0yOvla+TkAvFD/X3yx1GNKz6s4CZfBQX2wmbeb0s9ZN4gS141SJ4u9WP8RbaHutPEarh19AjbJzEZPA283rchqHQ0Wycjtk9WWl6dqFrPJ09Sr9ffGDeOPpMLmpiXk5e8b+6aYT4WLRx/C4UVVhOIxfvzl4KquS+0u/jBX7bV9ZOMy3qndPGjHMlAwSRL/Ov4cJuYV0hb0c8U7z9EezN5JYahgmHhmiSc2rOBPK9TJMXfNPZHTR44f5CMaGPz8ywVs6Wmn0GrnT/PO6FOfY1/REwly51KVhF0//nCm5e37jPRUUBSFu1e/TUSOc0ThKE4t75sp/rtNa/m8fSsm0cDtU3rfF/vojg9pCfVQasnlwhHZZXe3eut5r0Utdd0w+qyM+3ym9nW8MT+VtlJOKj4qZVxjsHGXZ+dlIy7BkML0/v2W1+mMtOE25nFSif5s95XdH1HjX49RMHN62ddTbOd+InKAEss4Zucnl/RlJc7Hzb8iIvsoskzmkPyrdLezrvOvNAU+QhRMHF76R+zG5PNGViJs6riTrV33ADKljguYUfxQ2lGUihKnK/Amm1rOZVPLmXQFXgFiOMyHM6rgQSaVfkSR8xokMXnyUm8hCAIO82xGFf6LyWULKXJehyg4CMe2Utf1Y9Y0Hk5D9++IpFGvA5Q7L2RG8UMYxBw8kVUsbboAXyTZu1MQBKYX3Eah9TDiSpDPm79PUIekOo2lzCu6BYAVHY/oWiw5jPkcV3wjAJ+3P0lnONncfpRjCjPcx6Cg8ErDv3UnH+WZC3eNWH254UnCcf2S9/HFx1JuLcMX8/FS/Sspvwu7wcalVSqZfLr2dTzR1CQVYF7BVCa7RhKWo1lNNFKP2cHVo9TM6v2b38IXza5Mv2ufRWM5oWQycUXm7jWv9TpruicskpGfTD8VgMe2LWGzp3Wft5UKoiDw69mnY5WMLG7dyTPbeke2+xvHVYzhhklq1vnWz984oCYbacgxW3j0pPOpcORQ4+nm6ndfwBfp/4z2QGKYeGaBt2o2c8fnqqXQ9w6ZyxUTB8/HciDx8vZ1PLt1NQLwlyPPosDa9wdoX/Drle/RGvIxypnPdyenJkh9wXtNG1nYsgWjIHLH9MwzutPBGw1x73r1AXXtmKMZYU/dN6mHWn8bT9ao5cLvTzgDi5RZsKYoCvdvfQkFhWOLZjLBVZU2fqe/kbcS2Z7rRp6fsiSvKApP7nyGuBJnhns609z68707wm0saFbL51+ruAyzlNxWEIh5eatR9f88rvgCck3JPaubPYvY4lUnGJ1c+n+6SvdVnU/SHFyFUbAyv+QO3dJ5jedlNnc/AsChhT8n3zIjKSYa97Cy5Toafc8BAmNyb2dC/l1pfTk7/a+yrvFodrR/E394KQJG8mznMqHkTcYVP4fbdlK/TSzaG2bDCCpy72Rq+RIq3HdikiqJy120eP7GusZ51HTcQlxObaCdaz2cWaXPYTNUE443sazpEtoDHyXFiYKROcX34jBWE4w1s7j5/4jLycRpjOtERjtPREHmo+a7iOj0aU50HUu1/VDiSpR3m/+i2yJwaulVWCQ7jcFtfNGRLFgCOL74dApMRXRHu1KO05QEiSuqLgPgg9YPqQ3oT3ECOKF4HiPtFfjjwYz2SoIg8I0xKvFd0PIlGzzJfqd6uLjqSEbYCumM+HhgW7KvaSbcOvk0bJKJ1d11vFS3rNfr74n5JeM4vnQCMUXmrpVvDojQqMqRyy3T5gPw21Xv0xgYXDP3H8w4hhkFZXgiqitLtBfjTL8qKLY7efyUC8izWFnT3sI333+FSPyr8zmHiWcGLG6q5XsfvYasKFw6fjr/d8jA9BgONrZ7OvnxEjW7+N1p85hbkp7ADDQ+ad7OCztUEnzP7NMHxDUgEItwz2r1M3997FxGOjPPP0+H+ze9R3vYR5U9n2tG9Y4oK4rCHze+SkyJM7dgPEcVTspqvQ9al7O2ZwcW0cT1o1Ir07V9PLTjeWRkDs+fwTS3vv0QwPLuFaz1rMMgGLh0RGqRzUv1TxBVoox1TOIQ9xzdmHebn8Qf91BkrmRuQfIxhuI+3m9RzeRn51+gW2JvDa5necfDAMwtvhmXKbmM3RZcyoq2uwGYkPsNKp2nJsXISoQ1bTfRHVqCJNiYVvRPRuRcnWY+u0xj973UdNxEJF6HJOZS4voOU8o/o7rgz9hMvWul6Ask0UmR6zomly1kZMG/sJtnoxCl0/88m1q+RjiW2irIZqzm0NLnyLUcQVwJsKb1JrpDyaTGJLmYW3ofJtFNV3gdS1vv1CWN84puxmEoxRtt4rPWPyUtFwSBE0u+i0EwUx9Yw+rut5NiHEY3J5WohPHd5ifxRpN7BI2iiXMrVIHZB61v0hrSLz9PdE1gdp6qXH9i51MpCZYkiFw3UhU3LWj5lO2+1CRV3W4VJxbPAuD+LS9llYE0igZumahm/l+o/Ywt3t5NESq2uHYJEv+y4V06w+kzs5lw+7STsUgGlnbs5NW61BZbfcGVY2YxM78CXyzCT5cODMHNFiZJ4q9HnYXLZGZFeyO/X3FgeF/ujVE5eTxy0vnYDEY+aazhloVvfmUM5oeJZxqs72jlugUvEY7HOblqLL+ae+KgWgoNFELxGN9e+DKBWJTDi0fw3amDS6590TA//lJVdV85djazCgeml/afmxbSFPRQZsvhG+OP7tO21nU38OxOdaToj6eciUnK3oIH4KPWtSzp2IJJNPB/EzKXywECsRD/3qZmGy+pOoFCiztt/OKOlazp2YxJNHJNwttQDxE5wlM71ekyp5acrDsWE2CDZzWre5YiInJB5VW6x1wX2MzSTjXrc1b5DRjE5BeIha0P4Y91kmuq4IiCy3SOJ8BHzb9EIc4o53GMcSaLoXyRnSxp/gEKMSocJzMx9xtJMYqisKnjZ7tI58ySJyiwzU/5PchyiB0dN9HsUT1Ai103MrVsMWXuWzFK/e+skC0EQSLXdirji19kXPGLGKUSQtEtbGo+G184dYbMKOUwvfgBCm0nohBlTeu3CUSTyarDWMnhJb9HwECDfwEbuv6ZFGOSHMwv/SkCIlu977DN815STI6phKOKrgbg49YH8EWTPSVn551IuXU0YTnAW02P6B73lJyZTHJNJ67EeaE+tdDoksoLMYkmNnk380Xnlym/h0k5Y3bZKz244/mMJOm6UWdik8xs8tbyTvMXaWM1HJY/lmOLpyKj8PsNr/SaiF1UdRgTXKV4YyH+uCGZtPcG5TY3N45XvWvvXbsAT6R35f9sIIkivznsdEyixMdN23ipJrkFY3+i0uHmd0eoNnT/Wr+EDxu2DerxDBSmF5byz+PPwSCIvLp9A3ct+eArMdd9mHimQK23myvfeR5vNMKckgrum38m0gB4Rw4F/Grp+2zoaiXfbOMvRw7+5/zl8ndpDHiotLu5Zer8AdnHxp5m/rPlcwB+Mu3UPs1ijysyd699FQWF08qmMadA3+cyFYKxCH/ZpIp9Lq8+hgpbdiX6p3YuoDPiodSSz/kV89PGhuMR/lOjeiKeXXZCWvukt5reoT3STq4xlzPLTteNickxXqh7DICji06i1JrsxxlX4rxS/y8UFA7Jna87i73Ov5rV3Wp7wkkl39NVsS9u/QueaAN2QxHzin6QRHAj8R51+o7cQ655KocW/lyXBO/s+TdNvpcAkcmFf8ZpTt3PG423sbn1IroDryNgpCrvD5S7f4QoDp6tmB4c5tmML34Vq3EKMbmDLS0X0+lP3ecoCkYmFdyL0zSFqNzF6pYbiMa7k+IKrIcys/AOADZ2PUCt942kmBLrNGbkqbZHn7b+AW80ud/0kNyzKbGMIyIHdmW1//d41CECAiKruj9hmy+ZsAiCwHkVV2IQDGzwrGZtz3Ldz5ZvzueMUpVsPFP3HOF46r63K6vPwSya2ODZxqL29OXsPLOLy6vVl52Ht7+BL5pe2KXhe+NPxyIaWd29k7ea9I85FQyixE+mnoWAwOsNq/iyvXf2THvjqrFHMMpRQEfYz+/X9b78nw1Guwr43hT1Bf6XK94dtFnuGk4ZMZ6rxx8KwM2fvk7TATTPfU8cUzGS3x+tVnceXreMf67O7uVoMHFgMqk+oicc4qp3nqct6GdCbiEPnHAuFkPvMlhfFbxdu4knNqsN4X888gyKbZnHMg4k3qnfyIs1aon93jlnYjf2/zzsuCLzk+WvElNkTiybyHGlfROKPb/zS9b3NOIwWLhlUnJ5NxMe2fHBLkHRFSPnZ7VOfaBtl33St8acgylDP+grje/TFu6kwJTLeRUnpYzrCHfyepNKBC8ecQFmSV/h/3HbO7SGm3AaXJxWep5uzBcd79AU2oFVcnBK6ZVJy2NyhHeb/wLANPdpVNqTFeo7vB+z2fMmIDC/5KeYpf89PxVFZknLD/FFd2I1lHBEyR91x1q2+RewvfuPAIzL+ykFttTTi8LRGjY1n00gsgJJdDOm6EnyHcmeo5mgKHFk2de7PyU7UrMnTIYSxhW/QI71JBTC1HR8h2bPP1LGS6KVaUX/wCyVEojtYE3bd1B0xD1VrrMY51YtmZa3/oLOUDIpPCT/Koosk4nIPj5u/lVSWV4UEj27SGzxfsoWz6dJ2yi3jWZOvkrsXm34NzE52YKpyFLCsUUqqXyh/nGickT3s51aejIFpgI6I1281pRMljUUmHN32Yg9WvNfQmlIKsA55UdRaSuiO+rj8Z3ZZSCLLW6+Plq1G/rbPgiNproruKBKdX+4e+1rROV996Y0iRI/P0QVVj1fs5zPW/tGZFPhuvGHc0h+Ob5omB8seXXQS7+3H3osU/KK6QoH+d6iwT+egcLXxkzmjjmqqO03Sz/mpa3ZjXsdLAwTTx3c8+VHbO/potzu4rGTzz/g5q9rCMdj3JWYb/vNyYdzTFlmG5mBRFSO85tVqn/dNybOZXbhiAHZz9sN61nf3YTLaOGO6dlNBUqFmBznP9vUHqKbxp9Afpb2Rxp80RDP16qjKL+XpaAIVIuXmBJnVu4EDs9Pr8QPxyO81qj62l1ZfQ5mKTWZf7/1AyJyhHGOsczJ0597HpNjvN+iPtTPLLsIq5Q8QCGuxPi4Vc2wnlB8CQ6DOylmg+dDuiL12KRcji66Nmm5rMT4sl0lUNNzL6XUlizqa/AvoC34BZJgZW7JX7AYkvt0o/EeNnb8DFBHYFa4ksv5uz9bN1vbriYSr8dsGMn44ldwWnpnKaYoMl7/09Q1zaC2cWzv/hpG09bxLWLx9Gr1vSGJNkYV/Isip9pi0Nh9D51+/elBAGZDEdOL/4Uk2OgOfUGD9xnduMl5N1FmPw6ZKKva700q44mCgfkld2AQrDQHV1HrTyaWhZZRu1wKPm1/XLcUeGLJpdgNObSHG1jX87nusZxccjY5xlw6I20s71qiG2MSTVwyQp1O9X7LB2mznmeXH0+ROZ+OSDcL29IboBtFAzeO+RoArzd+jieanYWNKjQqoCvi47XG1OX/VLhp/AnkmuzU+Nt5v3l9r9ffE7MLqrhkpNqv+uf1A1OSNYgifzz8bGwGI1+21bGgIfvhBwMBs2Tgb0edg91g4ovWel4/AEdqarhuymy+MVW9Z9/52QKa/d5BPqLUGCaee2FxUy1Pb1IbsP8y/wyK7YObARxIPLJxGQ1+D8VWB9+fNviiqZdr1lDr6yLPbOPbkwbmeBRF4cHNiwC4cszhFFp6RxT3xsctG2kJecg12Tm38tBer/9W0zKC8QhV9kKOzlJQ5I+FeK9ZfVBePOL4jP2gn3eswBfzU2jOZW6B/kQhUAnlJ20qcTil9KSU213TswxvrAeXwc1h+Ufqxqzt/hxvrBOHwc2svGT/T0VRWNapEtNZ+efqzmLf5n0PT7QBi5TDjPzkjKmsxFjfqfYfjnNfRY55nO6xbO/+M1G5A5txNGPybtWNUY8pyo62bxKObccolTGu+DksxtTz6/UQiW6gue0cOrpuRpb3ZYa1gj/4Mg3NR+HxPoiiY9KeCoIgUZH7E4qd3wRgZ8et+MKpyY7DNJ7RuT8AYHvXn4jojOgUBJEZBbcjCRa6wmtoDiT7X7pM5UzOVYnl8o5HdAnN7PwLMAoW2sM7qA0kW+5YJDuH56vVgk/bX9PdhlmycFTBCYmY91N+rpm5h1BoLiQQD6bt9TSJRk4rVTPf7zR/kpGIHZo7ntGOciJylHebsyORRtHAJdWq0PDF2sW9tkdyGa1cWKWSiWdr+l5CvXHCMZhFA6u7GviivabP29PDCEcu14xTj/kvaz8Z9CxjtSuXb05WhY/3rvyY0AE41UjDbbOO5pDCUrzRCHd+ntx3PVQwTDz3QCgW40eL1NGMl0+YweyS5L61AwVtQT9/XaOSjB8ccvSgz5qPxOP8bb1KCL85cS42Q/+X2EGdx76xpwWbwcRlo/Qzer3BMzvVzMvXKg/ttaBIURReqFWNsc+vnJu1cG1B85eE5AhVtmKmuTP3k77drJKFE4uPRBJSX/LLulbgiXlwG93McE9PGfdJm3pDO6JgPpKOpZGiKHzarlrVzMk/RVdQtNO/nPZwDUbBwjR3ctZZVuKs7FANyqfkXoRRTM6q1nnfxBetwSS6GePWz2J6wmto8D4NwPi8n6W0TFIUhdrOn+ANf4Yo2Bld+J9eCYhk2Udn9y9obDmRcORLBMFGbs6djCjbyojy7Vn/lRa9hck4A0Xx0dlzB02tpxIO964/sMz9I3Ksp6AQYXvb9YRjqW2Ayp0X4zRNJqZ42dr5O90Yi6GA0Tmqs8H6zr/rqtyn5l6IQbDSEd5MvT/Z7N0iOZniVkvbSzv0568fln8SBsFIQ3AbtQH9TNkRBfMRkdjh30J9QP9ziYLI/EK11/DD1vTG6ccWzcEoGNjur2OLL71dkiAInFWmvhC/1vBp1iTy5JJDcBgsNAQ7WNKeeqZ8Kpw3YhYGQWRF184+m8oXWBycWzUDgH8nXsAHAteOn4PDaGZTTytv1yf7xu5vXDtxNiU2J3W+Hv6zYXDHew4kJFHkN0eegkEQeWfnFt6qGZom+sPEcw/ct/Izdni6KLY5uG126h6wAwF/XPUJvmiEqXklnDdK36Nxf+KlmtXU+3sosNi5dHTqrFxfod1sL6o+FLepb0KRbd5WvuzYgYiwqxerN/iycyu1gTZskplTy7L7zIqi8Fqj+sJwZvmRGcnqDn89m7w7kASRE4rTj4H8qO0jAI4pPAophSdlc6iRLb71CAjMLThWN6YusJmG4FYMgpHD8vX7SZd2qmM4p7pP0c127vB+SE+0FrPoZFJOsgJfVqJs6FKnHI1zX51iMlGcTR2/ABSK7WeSa9W3ewJo9f6bDv8zgMjIgr9hM01MGfu/+1DwB16noeVoPL5/AnFs1tMoL15IjvNbiKIdUbBm/Wc2zaC06HXy3L9BEFxEomtpajuD9q4fEpe7szomQRCpzv9zQnDUyba2r6f0+RQEifH5PwcEmv2v0BXSz6qNdV+FQbDTE9lEgz8522iR3Ex0nwPA8k79rOeheecAAjv8X9IeTiZ5DoOb6W6VMH7W/rrucbiMbqa71XJxuqznUYXzkASJbf7taX09XUYH8wrUSsU7zemnGQEcWzwTu2ShMdTOsq7sHupWg4kzytVjfqFOv40gHYosLo4rUash/ZH1vHbsPCRB4LPW7azr6p3VU7bIMVn5eiLred/ahcTlfTfC7w/YjCZuO0R9pt+/9jNag32zqBrKmJBXyLemq/e6Oz5bQE+4/10M+oph4pnAhs5W/pVQg911xIm4TPs+NnGoY31nC89uXQXAnbOPH9TpRKD2mt6/R7azLwrzdFjeUcuyjlqMosTVY7KbxZ0OzyXsk44pnkCp1d3r9V+sVR9Cp5XNxG7I7nxb1b2V2kALFtHECQl/wXR4t1n9XufkTSfXlJMyrjnUwnrPRgQEji7UL58DfNau9uBOzjmEPJO+76lGGqa7j9bt7WwL1VDjX4aAyMy8c5KWK4rMyk5VMT8l90JMUvIggxrPywRijZilAkblXKh7HI3e5/BG1iAJDsbk3pbyM3UH3qGh+9cAVLjvIMd6fMrYPRGN1dDacTltndcTjzdhkEZQlP8ERfkPYdiHcZkaBEHC5biKipJF2G3nAwo+/+M0NB+Jz/9cVr15kmhjdOHDu6yWtrffmLJs7zJPo8yhfoebO36BrDNf3Sy5GeNW/TQ3dP5DV4w0NfdiJMFMW2g9DYHkUrTbVMZY51yAXW0We2NugeqisL5nMd0R/fGe8wrV3+fLzk8JxfXFWDnGHGa6ZwDwUWt6H8eTS9TzfVH7MnyxQNpYq2TmpBKVUL3akH3G8NxKtU/48/ZN1AeSbaUy4aIqlUi82bgKT5aq+lQot7s5rUJNNgxk1vOacYfhMlrY4mnnzfrB7608e+RkpueX4otG+OPKzC8ZX2XcNP0IRufk0Rb085svB2Zcal8wTDyBuCxz2ydvE1NkTqkex8nVYwf7kAYMiqJw19L3kRWF06smMLto8OfNv7BjFY0Btdf0klEDNxXq35vUm+zZldMosvatd9cXDfFavdqrdlF16kxaKjQFu1jUpt6Mz6vMngS/msh2nlAyC7shvegtGAvxUatKjk8uSW9o/3Hi4TwtZwoFZn1CGZEjLOlQ444s0Cdn3ZH2XeIQjUTsjeWdquhljHMublNp0vIa30K6IjswinYmuZMV83E5xMauBwCYkHstBh2Lo0i8g20JFfvo3P/DbCjUPZZAZC01Hd8FFAocV1Do1B/nuScUJUy35480Nh9LMPQBYCLH+X+UlXyELUvSmg0kqZDCvL9SUvgiRsNYZLmD9q7v0dz2NSLRzKINk6GE0YUPIwpWvKGF1HX9LCVpHZ17M0YxF390K3WeR3VjxuZchknMwRvdQZ3vraTlNkMeE3NU4/QVKXo9Z+Wpv+f6nvfxx7qTlpdYqxnlmIqMzOKO5H0AjHNMoshcSlgOsbQzWcyk4dii+QB81vF5WpHReOdIqm1q7+aHrfqipT1xZrlabl/SsZ7mYGfGeIBKWwGH549DQeGlOv258+kwM6+KMc4iQvEor9b3fSzl9ePUz7CgcQPbvfvSi5wZLpOFa8er98a/rv1k0LOeoiBw52y1R/jZratY29k7Ad9XCRaDgXvmqa0tT21axeKm1MMlBgPDxBN4dP1yVrU34zKZ+eXh/ffgGIpYUL+Fz1tqMYkSP5o5f7APh3A8xt/Xqw+Pb06cO2C9ppt6Wvi4ZQsiAteO67tw6fWGVQTiEartBczJ770bwH/rlyCjMCtvDNUOfYP2vdEe7uHTNtXSRus1S4eF7V8SksOUWYqYmqMvvAGIylE+aVdJ+fyi1C0mK7oWE4j7yTMVMtGVbH0EsKTjLWRkRtmnUGKtTlruj3Wx3qOWSGflJZfQFUVhRadKfKa4z0+yTwLY7nmBULwNq6GEape+Ef7Wrt8Tkz04TJMod16iGxOJNbOt7evIShCn5Wgqc3+RsXUhGFpIQ8txdHvuRSGExXwU5cXvk5vzQ0RhYDw+Lea5lBW/R67rJwiClXBkCY0tJ9DZ/StkOX2GzmaaQnX+XwGBdt/jtHkf1o0zSm7G5P4QgJru+wnFknsJjZKTse6rANjQ+S/dzOjUvEuRBBMtoTU0BZMJUpl1EiWW8cSVKKu69MvpcwtU258vOxcQ0RnZKQgC8xIvPova309Jpie6JlBoLiQYD7KkM3WJWhCEXS9m2YiMKm1FzMxVSeQbTdmXzs8foWZ7X2/4klBc3w4q3TFeXKVmTZ+tWdKnGe4AY11FHFcyHgV4aEtq8t5XXDVuNm6TlW3eDl6v7Zsqvz9waGE5Z1VPQgHuWpr63DkQMKe0kssmqL36t3/6DqHY0BFVHfTEs97bw73L1LT77bOPOaBV7OF4jLuXqaXSGybNodLhHtwDAp7dvoLmoJcSq5OLBjDb+eBm9eZ6Uvkkqh29m6G+NxRF4bmEqOiiqjm9nmYVjkd5tV59EJ4/Ivts55uNnyMjMzVnFCMdZRmPURMVnVxyVNpjXN61Am/MR64xl+lufUIJ6kMeYF7BsYg6IqWIHOLLxJQijTzsjZVdrxFXopRaJlBmTVbx1/o/pTO8FaNgZXJucgk9JgfY3KWSpwm51yPpiIW6Q0tp9r0ECIzP/7nuDPW4HGBb29eJxpuxGMcyquDvCDpCqV37jbfS1vEtWtovIhbbjiQWUZD3D4oLnsVoHJNyvf6CIJjIcd1EefHHWC0nAzE8vvtpaDmKQPCdtOu6bSdR7v4JAPXdd9ET1O+NLHGcQ455JnElwJbOe3RjRudcjFnKxx+rZ6f31aTldkMB43PU335FxyM6n0NgVr76srCi61WicnImcrzzUPJMJYTiflZ0faR7HHPyj8IoGGkI1lIT2KobIwoixyZERpnK7ccUzsYimmkItrDOk1kAdGbixe/tpsVEsvTXPLxgHOXWPLyxEO82rcxqnT1xWvk0HAYzdYFOFrf3fRLP9ePVFoPXalfTHBwYc3Wn0cx1iaznfes+ITbIWU+A22Yeg1kysKSljnfqhqb4pr/wo9nzKbLZ2d7TxV9X9r6/eKBwUBNPWVG4bdHbBGJR5pRUcPH41EreAwGPblzGTm83hVY735rSO2/CgUAoFuUf61UPy29Pmoe5l6rwbFHn7+LN+rXA7hJTX7C0YwfbfW1YJRNnVMzo9frvNa+mJxqgxOJmXkHqeel7IibHd2VXzipP3YOpYbOvhhp/AybRyLFF6VsBNOXv0YVHphQV1QV2UOPfiiRIHJ4/XzdmRdfHBOM+8kwljHclW0tF5TArE1muWfnnJZFhRVFY2aH2dk5yn4tFciVtY1vP04TlLuzGSqqcyXPfZSWaEBRBmeMCcszJ17SiyNR0fI9gdC0GMY/RhQ8jicn70hCPd9Dcejb+4MuAiNNxLeUln+CwnbPfR+gaDJUUFzxCUf6jSFIF8XgjrR1X4/Xr+3BqKHJeT779YkBmR/tNBCLJPXeCIDI+/2cISLQF3qEjkEzWDKKV8W61HWFj5wPEdYjjtNxLETHQFFxBczB5Nvg451E4DUUE4z1s6PkgabkoiByRaNP4rP0N3eye3eDgkFz1HvZJW+pJPEclzulMIiOrwcLRhapA8O2mzP1/R+RPptDspjvq45O2VRnjQZ0Tr/V6vlD3ea+zbTaDmTMr1JfzZ2sytwRkwoy8CmYXVBFVZP6z5bM+by8Vrhg7izyzjRpfJ6/sXDtg+8kW5fYcvjFJ7dO9e9kHhA9geyWXycxdR5wIwD9XL2FN+9BoLzioiefjG1awqHEnFsnAb488ZdBFNgOJ9qCfv65Rby4/POSYAZkI1Fs8vX0FrSEfZTYX54+cMWD7eWjLp8goHFU8hknu5J7C3kKzUDqjfAZOY++HC7yYULaeUzEHg6hP9PbGp+1r6Ix4yDU6mVeQ2YVAU+jOK5iJ05gsztHQHGxmg1cVFR1TmLoP9OPWdwE4xD0HlzFZpCQrMp8nREVHFJyGqENgN/S8TzDeg8tYzFhn8gtAU3AFbeENSIKJKbkXJS2PxL1s7lbL8BNzv4koJLdl1HuewB/djFF0Mzr3Zt3P0ur9Nz3BdxAwMarwAcyGqpSfW1ZCtHZcQyxeg0GqpLToLfLdv0JMQ1T3B2zWkygv/hinXS19d3TdSjCUumQqCAIj8u7GaZ6LrPjZ3n49spwsUnGYJlDhUj1TN3fepUssR7rOwyoVE4y3sMPzYvI2jMWMdZ0CwOrOp5KWi4KUULjD0s6X9BXwucdhFm20hxvY4l2p+5mOLlQfqMu7FuOJ9ujGuIwuZuaqZO3D1o90YzScUqqe/4s7V9IdSZ8BlESJ00vVakVvREanl8/CJBrY4m1idXd6+yY9XJTw9FzYupmGQFev198bN4xTX2Kfr1lOVzh928a+wmE0c914lXD/bf0ionKyMG1/4xuTD6fY6qDO18PDB7C9EsAp1eM4Y+R4YorM/338xpAouR+0xHNHTyf3JNRetx82n5E5eYN8RAOLP676BG80zJS84iFhnxSMRfnnBi3beSQmKTsC1lu0hXz8d+dKAK4flzlTmAktwR4+alF96S6q7r0P6LqeOjZ46jEKEmdVZG/BpFkonVZ2BEYxfWbYG/Xzabvq/ZhJVPRRmyYqmkq+Wb8FwRv1sKxLJctHF+nbI23zraIt3IBZtDIz97ik5YoiszShZJ6Ze44uMV3d+SQA41ynYzXkJi3f2vM4UdmL0ziKSsfJSctDsWZ2dN8HwOjcWzFKydsIx+po7FFFR5V5d+Ewp/4NFEWho/PmhC+ni6KCJzCbUrci7G+Ioo0896+xWc8GYrR1XEskmrpMLAhGRhb+E6NURiRWS5PnPt24ke6bMElFBGO11HoeSFouiWYm5F0PwKbuh4jpENhpeZcAArX+T+kK70haPtV9CibRRmeklh3+5Ae/WbIyK0/t4/w8hbVSlX001fYxxJX4LrcFPRxbqPYtf9a+OK3IaKS9gnHOkcQVmfdbM5clTy09HIMgsd5Tw1ZvfcZ4gByjjZNLZwC7X0B7g2pHIYcXjEZB4fmdfbdWmlc0mok5JQTjUZ7Y3vcsaipcMfZQ8sw2an1dvFyTPHp1f8NuNPHDg8ReCeCuuSdSaLWzpbuDPy4fOCeDbHFQEs+4LHPLwrcIxqLMLR3BlRMHrrdwKGBDVyvPaPZJs04YEpndJ7cuoz3kp8Kew3kjB+5h/tjWxUTkODPyKpiV3/cRnC/UfklckTk0r5oxzuwNxnevr5LtE0qmk2vKbmpSjb+JVd1bERF3ZVnS4cPWJUTkKNX2csY5qlPGReUoi9pVQntsGlHRZx0fElOijLCNotqm38/4aYIcHJp3PBadEZo7/EvpjNRhEm1MdSeTxo7QFuoDXyAgMjX34qTl4XgXW7tVYjop70bdvs2tnfcQVwK4zIdQ6tAXLtV13oGihHCYj0iUnlOj2/MH/MH/AgaK8h/EZEwt0BosCIJIQd6fMZtmISs9tLZfQVxnApEGg+imMveXALR4/kUwkqyON4gOxubdDsDO7n8RiCYrYqucZ2E3VBCOd7KtJ7nMn2MaQbVD7a9c3ZWc9TRLdqa61azoss7krCnA4QWnISCyxbeS1pB+mfyYQvVFaFH7+8RT2EVNdE2g2FxESA6xOI3ICHZbK73TvIh4BgFPntnFkYXqvUtzm8gGmovFBy1raA/3vrdSs1Z6uW45oXiywKs3EASBGxK9nk9u+wJ/rHeip2xhM5j4xgT1c/9t/SIi8cHPen5t1JSDxl4pz2LjnnnqtfLvNV/wZUt2L0oDhYOSeD6zeTXLWhtwGE3ce9SpQ4KIDSR+v+LjXfZJhxUPvn1SVI7z742qpchNk47EmGW5ubcIxqI8s0PNptwwLrPZeibEFZmX6pYB+2ah5I0Geb9Z7XnrjajorSb1u5pbMIVCiztj/HutKrk9JYOoaGX3ql2iomlu/Sy4OoVIFaMcU6g/RrMj3MwW7woEBI7IT55CBLC88xUAprlPxazjy7m2+zkARjqPxWVKFk5t7XmKmBIgxzSBMntyRrU7tIzWwNuAmBAUJd/aPKGP8YQ+QMDIiLy70343wdDH9Hj/AEB+7m+wWtJnjgcTomChKP8/GKQRxOI7ae/6Xtp4t+0kcqwnATHqu+/SjSmynUquZR4yEbZ1/V5nn0Ym5qlz4bd0P5qy1xNgq2cBgViy7dDMvHMQENnpX0FHOJnc5pmKmehSM9JLOt7WPc4Z7jm4DG56ol2s6daf8CQKIscUqSR4YVt6gjEvfyYOg422cCdrujNbVmnuEh+0LCMYS51N3RPjXeVMc1cRV2Reb1iW1Tp74uji8ZRac+iOBvo8vx3gxLKJVDvy6YmGeDlRHRoIXDbmUAosdur9Pbxeu27A9pMtREHgzllqVv3ZravY1tN7f9WvEk6sGssFY6egALd98vagjjI96IinrCg8sEY1N7555pFUOFObah8IaAv6+bBxOwC3zDh6kI9GxZdtdXSE/eSZbZxTPXBl/8VtO/DFwpRaczimpO/Zqo09TXSEfdgNZo4tzm6yzZ5Y1rmNqBJnhK2QSTnZvwB82amW9o8vzjwLviPcTV2gCRGBIwvSx6/pURv9D8ublVJU1BSqpyvSgVEwcUiuPtne7FUfniPtk8kzlyQtj8hB6gJqxl3Lcu0JWYlR61MzRhNzzklarigK9T5VuT3OfZUuYWz0PgtAqeMcnCZ9wVa7TxUuFTivwJJGia4ocTp7VIGS0341Trv+OM5MiEe3EAm+1au/aOgjFKX3WSdJKqCo4DHASDD0AcFQetPoitw7AQFvaCGhaHIpXBAExub9CID2wHuEY8lm7pWOU7FIRUTkHlqDyZnEIuskCszjUYhT508uK+cYi6m2q+fodp9+mXdmnvqSscmrTyoNooFZeapN0TrPSt0YgCPy1XN3m287gTQm8WbJxOH5MwBY2Z3Z9HxKziiKzbmE5Shre7ZnjNegTSpb0p6Z3O4NSRA5rUwVzS1q7bsqWxJELhqp/g7vNQ3ceEurwcjlY9T9vDUEDOUBDi2q4Njy0SjAf7cPPhkeaNwx5zhcJjPbejr5sK7vzgj7ioOOeH5Uv50dni5cJjMXjx86/VoDhddrNiArCjMKyhjlGhp9rO/Wqzfb48vGDli2E+DjFvWmfEzJ2H7Jai9qU7c3O3/kPh330s5tifWzt99pD/dQF2hFRGCGO/NggzU96jGOclRiNySXvPfEBo/6O0xypSbRGzxqhnascyJGUV+QttW7KhEzQ3d5nX8VcSWKy1hMnimZcLcE1xKWvZhFF8XWKUnLPZFt+KN1iIKJEnty5jEme2kNqMS0zJEsSgKIxBrpCap9gIWOK3RjNPgCzxONbkAUcnDn/DBtbCqE/U/ibTueQNcNvfrzd16Br+NilBQjLtPBZByPy3E1AJ09d+lOF9JgNozAZVFHnrb7kkvhAA7TOFzmGSjEafa/nLRcECTK7Oo2GnXGaAKMcKgZQe3FYm+MdKjTt3b49AUeo+xTkAQDXZEWOsL6ilzNU3ajZ3VKpXieKY9icxEKCpu86cma5nm7tiezrZIgCBySq8av7M5+DvusvDGJfdQR3Ify9txC9V7wedvWjC0B2WB+4sV8aftOfNHsMrf7gpPKxwOwqHnHgO6nN/jayMkAvFKz7oD29QTIMVt28Z6H1/U+295fOOiI58NrE6XScdOGhLJ7oPHyDvUt7pyRyZ6JgwFFUVjQoBKekyrGD+h+FjarD4JjSvpnEtXHLepxH1OUnQXS3ljaqfoNzsobnfU6q7rVdcY4K3AYMxuUr0s8LKekMYwH6Ah30hpuRUBgnDP197PRowoBJrj0M9NxJcZ2v5o5He3QtyOr8WsZ0Vm62cpav9oaUGk/HFHHS1MjNUXWwzGKyWX6Fv8byEoIu3EMLh37JIB23zOAjMN8BBZj6u9flgN0e34HQI7re0hiskApE0K+Bwj2/AhQEA2TkEyzs/5DcBKPfImv42LkeHZTcfZEjvP7CIKLaHQd/oB+76SGAodaCu/0P4+s6JOAMsf5ADR5X9R9KJc71FJlk/9jZJ0eyxF2NRvZEFhKTKccX21XiWdDcJ2uWbxZslJpU8/lrb6Vusc4yjEeo2CkO9pFc6hBNwZggku9bjd40mf1prjU62GHvw5/hhGaADNy1fgVXdkTz3JrHiUWNzElzsru5IxzJkzPrcRltNIdDbC6K7VNVLaoduRTZc8jpsh81jpwmbBxOYVUOXKJyHE+ac4+QzyQOLFyLHaDiTpfD8vbUp8/BwqumjgTURBY1LiTTZ36Y2kHGgcV8dzc1c4njTWIgsBVk2YO9uEMOHZ4OlnV0YQkCJxR1fvS8EBgbVczzUEvNoORecUjB2w/WzytNAU9mEUDcwr6vp/WkIf1PepN6aii3pftW0M97PS3ISIwMy/7SUcrEw+zbLKdAGs8ajZHe3imwgav+vCttldjS5EZjcgRtvnUuAlOfeJZF9hMRA5hk1yUWvW/Zy2bVe3Qny1fpxFPx1zd5Y1+NVNZbtefKtbofQGAUsf5usRWUWJ0+NVSfIEjfdnc43uAeLwJSarA6bgmbWzyfhRC3j8T8qjiHbPjWzgL38ZZ8FIv/l5AEPOJR9fg67gAOd7Sq2OQpDzcru8C0OX5LbKSeq53jvU4jFIJMbmT7oC+CX2R/VREwUogtoOecHK5O99yCCbRTUTupiOUPKko3zwOm1RATAnSHFyZtDzXVI7LWExciVLn1/fDHJN4odEy63vDJJoY7VBJ5UZvasX0RI14etMTzzyzmzJLETIK63r0zen3xHS3mr3c6mvAG83Okkg10lfXW9rRe6JnECXmJbKeH7f0T3lce0H/uDl7At1bCILAiYms54KGoWHebjUYOXmE+tlf3jH405UGGhXOHE6pUp9hg5X1PKiI58Pr1AfgSVVjqTzAezthd7bzyNKRFFhTeznuT7ybyHYeUzJ6wAzjYffNc07hSKz9MIbzk0Qv1RR3BQWW3k+3WpYos493leMypi+B7wmtfKdlVdKhLdxJS6gdEZFJrvTlfC3rM9GVOuu83beJqBLFbcylxFKuG6ORgTGOabrTjLojjXRHGxGRGGFLzkZ6Ig10R3YiIFFhS7an8kXr6IlsRkCixJ7co+yPbMMbWYOAgRLHWbrH2BP8kGi8CYOYh9uWrKjXEI+30+P9GwC5ObcjCtl7tCqKQsjza0IJQZLF+QMsztt7LWiTjJNw5L+AIJYgxzbjaz8POdY7BarTcS2SVE483ojH+2DKOEEwkG9XWxNSldsNooMi+6kANPteTlouCgZK7aojQoMvudwuCAKVDlVIV+tLNikXBIGRiaxnjY6tEsCYRAvHdt8a4inaB7SMvJah18NEp3qu1wXq8UXT2+dM2VVuz0yOCsw5VNrUMv7q7uxJpFZu1yohvcUxxernWdja+z5R3e0lyu0LW7YMqPBEq3R90LhlSKjbAc5OlNvf2LlhSPiMDjS+Plnttf3vtvV0hgbGvzUdDhri2RUK8tJW9W3m2smZRRpfdSiKwiuJt7ehUmYHWFCv3si1t96Bwsct/V1mV4naMUX7dtzaw+XQXpTZm4IdNIc6kQSRKTmZs7Zaf+cYxwishtSkSVEU1vWo58YkZ+pM+O4y+7SUBEorf45x6pe4d/jUN+oy2yRdNbtWZi+xTtedy97oS/RlWmdhltxJy1sDbwGQZ52HSdL3IW33qTZMefYLEAWzbgxAt+ePKIoPk3Eqdus5KeP2hqLIBHt+Qtj/TwAsrjuxOL+3zy4KknEMjoIXEaURyPGdeDvOIx7LviwpChZyXbcB0OP9a1p7pQLHxYCIL/wZoaj+Pkrs6oSotsC7uvPZyxKZ6Eb/Byg6/YYj7Ik+T/9nuuV6LROutWTsjXLrKKySg5AcoCHFeMyJCeK5xbuBqKxvMeQ2uSmzlqGgsN6bXtyyq88zi/GZsLsi0Zs+z0MTlY8t3iZ6Iv6s19Mwt3AsBkFku6+NOn/v2zL2xqyCKmwGE+1hP+u7m/q8vVSYkVdGgcWONxrmi7bem+gPBOaVVFNgsdEZDvJJY+9bH75qmFVcztSCYsLxGE9tzG7yVn/ioCGeT21aRTgeY0p+MbOLKwb7cAYcqzqaqPF2YZWMnFQ5NPwHd3g72eJpwyCIzC/LnoD1Ft2RICs61L6n/iCewXiEJYnZyMcU976/U1GUXeW03vR3ag+xia4qrFJqwqRBy85Mzkn/mRuCjXRFuzAKRsa7Up8bGxJly1Rl9mDMR31A/VxjUvZ3qlksLau1NzTiOcKhby+l9XfqWSgpikKL/00Aiuz6Nk6RWAOe0EcAFDgu0Y0BiEa34fU/DkBuzs907Zj0oCgxAt23EAk8DghYc36LxXF9Vuumg2QYgaPgBUTDGJR4I77284lHs1cC223nYTJOQVG8dHv/nDLOZCjHZZkPQLvvad0Yt+UwjGIeUbmbrtDipOVF1sMwCHZC8Ta6wskjEctthyIJJnyxZroiyQ/1EbbpiEh0RRrojiQTHlGQGOVQRWdbffoPyVJLJS6Dm6gSYbs/dZZyqkvNbK3pSa9gnpyjZiNr/A14o5lJ4SGJisTKXvR5FphdjLSrmdJlnb3vd3QZrczMqwbg49a+l9tNosTcQpUMf9w8cGVwSRQ5vkz9vjSh6WDDIIqcUa2+hP93x4GvbhcEga9PVu/Jj21Ysd8zzwcF8YzKcR5br/YnfX3yoft9vvJgQCuzn1Q5dsiIqDRR0eFFVeSYMgtl9hWftmxFRmGMs5Bym7vP2/uifTthOUapNYex+2AaXx/ooDXcg1GQmJ5bnfV6venvVBRlF/GcmkFYtDZhozTBNR5TCqV6T7SLxmAtAgLjXclKc4Dt/rUoyBSay8kxFSQtjytRahM2Spptzp6IxP00B1YCu0UoeyIYa6UzvAYQKE2op/eEP7qFQHQbAkYKbfr9n7tFRXOxGFP31nZ5fg3EsFpOwGpJHuepB0WJEOj6NtHgC4CEzf0XzPZLs1o3G4hSKY7855EMk1HkNnztFxKLrMxqXUEQyc25EwCv71GisdRZnEwiI1EwUGRXzadb/W8lLZdEMyV21YS8wZ88QcgTSOnyAAEAAElEQVQgWiizqT31tf5kdbtZslNmU6syKcvtjhlA6j5PQRCyKrdPzVHP5bU9a9MqmHNNOVRYS1BQWJdF1lPr86wJNNMV8WaM16D1eWqtOL3F0Ylye7/3ebYMXJ8n7C63v9e4eVD9JPfE10aq58aCui1DRnE/kDhj5AQKrXZaAj7erNm/LwAHBfF8a8dmmgM+Cq12zhi1b4rkrxJisszrNWp25JxRkwf5aHZjd5l9YDOw/V9mVy/Ko4sm7NNLy5eJMvsU9wgsUnYvAYqi9Kq/szXcQVu4C0kQmeBKL17Ssj1TclKfG5s8KjmttI3EYdDvadVmaGukYG80BNYTlYPYJDdFluRMb0PgC2Ri5BgryTElT5Vq9H8IQJ5lGlZDYdLy1kS2M996FAYx+Rj/V1SUmhCGwl8QCL4JiOTm/DRl3P9uO4i/8zqioTcBE7bcf2KyfS2rdXsDUSrAXvAMknEmitKNr+MSYuHkrKMerJajsJqPBWJ09fw6ZVxWIiObmlFuCyxA1vEZ3VVu932gS+h2ldt1+jxhd0Y8la2S1spRF9hEKK7fkzYxC+I53jUOo2CkM9JFYyh9Obk3fZ4uo53RDrUPujdZT60Css99ngmHjRWdO/FEUwvJssXRiXvmmq5G2kMDN0byiKJqHAYTLUEfazobB2w/vcG0/BJGOnMJxWO8WzewxHsowCRJXJGY2vjw2qX71UrqoCCemqjoiokzBlTQMlSwqKmG9lCAPLOVI0urB/twAGgL+ljRoYokThhA4hlXZD5pUW/i/WEaLysyCxNlLK2Zv7fQ/Ds1MUE2qA200hnxYhKNTHRVZ4zX+jvHOqqxSqn7OyNyZJePoZb90cPGDGV2UOezQ5r+Tr86qKHafqhu6bo2azW7fpldy76lKrP3BD8gGm9OKypSFIWuHnV6j8N+MSZj5t9YkX34O64iFv4QBAv2vIcxWZON8fsLoujGkf8kBtMRoPjwdV5BNNE+kAm57jsAkUDwdUJhfVKniozU8aFaP+zecFtmYZIKickeOoPJZvAltnmIggl/rA5PJPmhXWlXWylaQ+sIxrqSlmt9nrX+lcTkZGKbZyomz1SCjMwOX3I5H2C8Uz2f64M1eKM9ujEm0cR4p3pf0AYopMLURMtKNn6esG99nofkjkJEoDbQTmtI/5jTodKexyhHoWqD1NZ3slRkcTLJXQqw6z46EDBLBo4pVe+H7w4RdbsgCLtERi8fBOV2gMsmTMcsSaxqb2Z56/57ATjgiefy1kZWtDVhEiUunTBjsA9nv+CVxEVzRvXEATVo7w3ea9yMAkzPK6PE5hqw/azubKA7EsRltHBIXt/Hg67vaaQ97MMmmZiV13tbJlmRWa4Rz14Yx2sPryk5IzGJmV+Wsu3v3OTZTFSJkmfKpcxSmvKYN3q0crw+8ewIN9MZaUESDIy062dOtezVSMdsnX3snmajZcP2RDjeRXswIUzSIZ6+yEYCsRpEwUyBLbkMD7uV2ulERYHgG4QjSxEEK27Xrbox/3Pccje+jkuJRT4HwYEj7wmMltRz7vsLgujAnv8oBvNxoITwd15LJKg/RnJPmIwTcdhU5XpXzy9TZjV2i4w+JxRNLvsKgkRRgrxrmeY9YRBtFFtVcqlXbncYi8k3jwUU6gPJU4qKzKOwS3lElRANQf2HvvaCk6rP02nMocJaDcAmb2pSOTWR6V+bsc9TvZZ2BhrpiWYun+/u88yesDmNVia4VM3Blx37RvSOLtLK7f2kbi8eeFslgBMr1BeABUOkzxPgnATxXNRUQ1uw94KvrxoKrHbOGqW2uWgJuv2BA554/ifxZZ49eiKFQ8RSaCARiEZ4p04lIdrb21CA1kQ+4GX2RFP8vKLRGMS+n94LEzfzIwrHYNqHbPkWbxM90QA2ycQkV/aitt73d6rxmfo7VyeyPFNypqRsG2gM1uGN9WASzYy06+9fe/hX2sZhlpL7db3RNtrDOxAQdfs720IbCMV7MIkOSqzJ5LbJvxCFODmm8diNyd+bpmbPtx6DQXQkLVdFRWqpPlWZXVGiid5OcDm+iUFKHve5J2S5G1/7RcSjKxAEN478pzGY9ceIDgQEwYo97wGMltOBCIGubxIJvpZxPXfOrQiChXDkSwKh5B5NAJOhbI9JRvoiI81WqS3wXopyu/qC0KhDPAEqE328+rZKItUO9TzZ4ftSd/1Mfp6w+0VpQ5py+5REpn+jZxMRneyqhhyjkxG2MmD3YIZ0mJozChGRxlA7LaHsVeaH5vex3J4QPH7atoVYP1gBaS1Kn7ZuG1BrofklozGKItu8HWzzpHZe2J+oduUyPb8UWVF2tasd6NCsld6q2Uyjr/cT0/YFBzTxDMdjvF2j3jAOBsN4gC/b6gnEopTbXcwsKBvswwEgLsssaasF4PgBJp5LO9T9HFWcfXYx7fY6VVHGvpjGA6zrUdX1U93VGHqRfd7gUW1Gprkzq+C7Ij10RLoREZngTN/fudWnPtwmpxmTud2vku0xjgkYUmRba/1q+8Foh/7Y2fqASnCLLWOwGpIz3E1B1Wy83DZLd1pRW1DNipXa5+tuvzO4CIBC20m6y7uD7wBKYlKRfqY6GFpILLYDUcwnx3mjbsyeCHn/jBxbjyAW4ih4DoNpRsZ1+huCYMKW+zeM1vOBOIHuHyHL3WnXMUiluBw3AKrQKBU01X93QJ+c5phnYpIKiSs+esLJ5E/18xTwRLYQiiUTr0r74cDu335vaH2edQF90jjKoZLK9kgj/pj+A3K8U33ZTqdsL7eW4TbmEFWi1PjT2/lMTvjhbvbWpI0DsBksjHOqL0na9ZsNDs1Vr1ntXtFbTEtMMfJEg2z26o8V7Q2m5pbjNlnxxcJs6undAIPewGmyMKewCoDPWmoGbD+9xVkJ+8EPGgZvlvn+xKT8Ig4vqSSuKPtNZHRAE88NnW1E5Di5ZitT8nuvRv4qYlOXOgJrRkHZkFHvNwR6CMdjmESJ0U59r8X+wk6f+sAb5yrqn+35OwAY60yfDUuFhoB6PFX2ZHFMKkTiUToiar9XhTXzei1h9RgLzLmYM4iXWkKtAJRZU7+UtIfVmGJL6piuiPpAKjTrG8t3RRoSx6RP+noi6kM2z6xPrD0J2x23ObnnUlFk/IlysMus3woQiKjE15EmIxkKLwTAZj0FUSdruifkeBMR/xNqvPtPSMbBmwQmCAZs7j8gGiaA4iHs+3fGdey28wAIhZeknGbksKgG/pF4HTEdMisIIk6T+lAORJKzcyYpB5tBbd/wRZNV9LlmlWCF4t1E4snClXxzNQDdkQbdlgCrZMdpUEeYauff3tDO2c5wO3KKGeaCIFBmVY+zNZx+ZGCZtSgRl10Gs9ymXq/Nvch4jkjcG5qCXfs0d10SREY71OPc6evo9fp7QxQERjvVY9rZD/6g6TAxV30ub/f2/bj7CzMSCZvN3YMzTnIwcGR5NQCr2gbOv3VPHNDEU/sSpxeWDhkSNtDY3KOWLMa7syc6Aw3tplLlyEPqh/J3KvhjEdrD6gOt0p7X5+35oiE6EtsbYd83wtwUVG/cpdbsZ363hlXxhUU04TJmbg9pSzwUC83pP7M/FsAfV/uWisypz4+OxMM435SavHcmHvy5KWK6o42J5frk1RNVialLp4yuKAq+qJoxcupkK0OxBmQlhIgJi0G/fSEYUfv3bKbUAqpg+BMArObkiUhJ+/TeD4SRTIdhyCJ+oCEIIhbnzQCE/Q8jy8mCnT1hNIxFEkuAMOEUIiOD6MYkqX3RwYj+6EB7Ys69X6cPFMBprAbAG03O+JlEG1ZJPUe1339PuE3qy11Y9hOK6/dU5plUotKZgnjmGHMxCAZk4nRHUpOZQrN63raF0pML7ZpqC2dHjEosanxvSu1FlhwkQSSmxGkP71upU7s/1Qb6h8BVJe6ftb6BJZ6jEomIHd6B3U9vMC5HtYZrCfroCYcG+Wj2D2YUqi9iq9r6njHPBgcJ8dy3bNVXEdpb2jh3sq/iYGGHR72pjHL1nQymgza9I9dkw2XKftxhyu0FtO3ZcRr3bXv1QfVBUGHLnrhq2ZISS15WL0zNIfVlo9iSfh9tCULpMriwpFG+t0fUjGeBWZ9URuUw3oQyOc+kf211R1Ti6U5FPCOqw4HLlEwcg/FW4koQAQN2Y3JGVSM9NuNI3TK9rIQJRtUWG5tJv885Hm8jmjBkt5jT+3YqSpxo8BU11rHvE4n6G0bLKYiG8aD4iYYWpI0VBAGL5Shgd6ZXD9bE9xWI6AtvbEa19OyP6vcjOkzVAPgiNbrLtd9by3jvCaNowWFQ71vai8veyE0Qz67EObo3REEkz6S+VLWniIHdL16t4dQxACUW9XiaQ9kRuuJdxDP9i8CekARx14tp/T4SxxEaUfT3D/Ec4dC2t3+I53bP0Ml4Ok1myu1qe9DmnoMj6zmtQL2P7/R20xXquy1XJhzQxHOlRjwL9NW7BxpkRWFz99DLeO5IZDxHDXCZXXs7H9EP2U7YfRPf12ynoijU+dXfo9KW/YtAa+KhpT3EMqEpqN4cSy3pf3ONeBaliVMUhc7Ewzg/BfHsjqjbMYtWrJJ+iborDfGMyAGCcfW3cukQS4202I3liIIxablGemwm/T7eUHQTEEMS3RglfeIbDKs9oibjFKQUozY1xKOrUZRuEJwYzPoTlgYDgiBgtKg9rrHwxxnjrWbV5D0YWpQyRiPqwYi+KtxuSk88d2c8a3SXa7+3J6o/f147X7TzZ29oGfZUpXaA/ASp7EhTRteugdZwelFLcYJ4+mJ+fLHMM62L9yHjCbvvD7X+fRPZ7Mp49hfx3E8Zz5FOdT+NgR5CMf1Rp4OBcYnn56buoSF6GmjkmC2MdKkvP6vaB77cfsAST08kzPYe9aI5WDKedb5uQvEYZsnACId7sA9nF7YnyijVzoHNeGr9SNrbel/RV+LZFvYQkqNIgkhZL0rtWsYzW+LZnCgXlmQgnq2J/s7CNGV2f9xHSFbLS/k604gAOhOZpFxTkW72Lxz3E4yrPapuY/JLn5bttEg5uvPZNdLiMFbpH2Mi46mVffdGYI8ye6rsZCikltkt5qN0l+8JjdQZzfMQdIjwYMJoVq2cYuFPUJT0CmQt4xmJriKeQpCktSYEoimIZ+I7j8TbiMaTfSediYxnKuKZY9QynvrEM9eoEs/ujMQzdaZSaxHpSBOjXQNtGXo8LZKZXGMOAM3BzNmvPUvtvTHkrkxUROoCQ4R47qeMZ57ZhstoQQF2+rLPEg80tIrhwdTnOS3Bk/ZHuf2AJZ5r2ptRgAqHi4KDwEYJYFPiIhmTkz+gvZS9xf7OeFbZsyd56aAJi0bY9o3I1iceIiWWXIxZeHFqaNlFPLP7HBrxLM0gRNKyO+n7O9WHdY4xF2OKcZpdu/o79QV7GmmwSW7MUvK1p2W7XEZ9n1VfgrQ4TfrCJE3YYjfqZzw14mk16pfZFUUhmCCTVktm4hlNlKYN5oH36+wtJNNMEBwochfxFGRRg0EqxWhQvTRD4eTRlbD7OwtFtyHLySU3g+jAnLCdCuj0eWovC4FoI3EdyyWXSf3N9Xo8YXfGMxXx1Fo70hFPrUWkPU0ZXbsGeqI9hOPpxyOWWlUS0pShHxSg0OxGQCAsR+nJYsa7hopExrN+X4lngrj2RIP0RDJnZjNuL3EPbQ/78Q/g+EhBEBiVSEgMJYGR1ue56SAinjMKtD7P4YznPmN1grVPO0jK7MAeZfah09/pjYRoDqpCgTGugT2uWr/6xjxUMp51iX6tyl70d0LvMp7BeIjuhLm11o+WClo/WzriqT2s802pY7oyCIu6oln2d+qU2QG8EU1YlJzxVBRlV5k3FfHUysSphEWx2A7i8UbAhNmU3odTkT3EI8uBoUk8BcGIMdGjmk253WJRhVFaxndvGKViDGIBIBOM6s//tu/q80wmnhapEINgRyGOP5rcx7m71K5vHbSr1J6yx1M957qjbcgpMrzaudsRSU0a7AY7dskGZM56apWE5iyIp1E0kG9S+wN7U24fkSCedfvY42k1mCg0q9WD/sh6Oo0W8ky2xPYGNhM5JkHytg4RL0/Y3aq2ubt9v46SHExMSwiMVrc3D/hnPmCJ58r2g09YtGmXsGjo9HduTtxMSqzOfhH8pINWFurvHs8q+74RZq1sVtnL9TVhQkkWxFMTFjkMduwGW9rY1pDW45lara49rFP1d8LubFNehoxnKuLZk8h45ugIiwC8CSseR6JfcE+E483ElQACBqzG5PnuihInmBANpRIWaWp2i3kWopj+O4uGPwXiiNIoJEPfJ2ENBDRCHM2qz1PN8Go9rntDEIRd31sgZZ+npmxP7vMUBAGHSX1h0F4g9oQmLgrFewjrKNdzM2Q8XcY8JMFAXInhieoTO+3c7cggHCrcJTDKRDw1gVF2xEh7YeyNpZLW49kQ6NgnSyXYfZ/a+RUrt4/LUX+voSTkGZOTjwB0hYO0hQ78CUYAU/KLkASBtqCfRn/mSV19wQFLPFfvYaV0sGDLEBQWbe5Rb/7jcgb2mELxKM1B1YqkP4inJxqkOxro0/Z2Ec9eZDz39PAsyqLUrj0MSzNkO2NyjI6EvUyhOXWs9rAuyMpKKT3x1Pr19kY6K6WYHCQYU6sVWr/gntDIjtVYpSs8CsW2IytBRMGK2aBfqo9EVgJgNiWP8kw6Hq3Mvh/GYu4rNOIZjyxHkdM/MLTPHIttIy7rzwa3ZiCeu5TtOl6esNsCS8/LM6OlUqInOBjvIaTj9SkKEm6jei9JVW7XSu3emIdwPLUdjvYClpl4Jnw2s8h4qvEa8cyeABZb3RgFiagSpzXUnfV6e6K/+zwr95PASHs2DCXiaTEYqXaq99/NB4nAyGIwMj5X/S0Gutx+QBLP1oCPRr8XAZh6kBjHh+MxtvaoN5yhRTwTWdic/jF0T4W6RDnIaTTjNiWPcOwttKxBodmJzaA/5zsT6v1aqT3736MpkSWxSWbcxvSm5rBb8JBJWNQR6UBBwSgYcRvdKeM0C5r8NOX4ruhucZEe+mKl5Iuqk6dMYg5mKZl4+yOasChVmV3r75yEIOhPigpHV6v7ME3XXa5BUZQ9hEVDl3hKhhGI0kgglsjQpomV8jAkvDojEf0JQVqLQjCqb6mU0ctTy3jqeHnC7t/doyMwMkk2bInfvTui//DLpGy3SjZsid7idOX2XRnPDIRyV6k9mB0BKbWqBLApmD0BlASRskQv+b6W2/vfy1P9HQbaRF4jnju8HYTjsQHdV2+gkbANXQM3vWmoYbef5zDx7DVWt6sZk7HuAhymfSMNXzVs7m4npsjkmq2U2pKVwoOFTbuI58CS4T2tlPrDZ7Gv/Z2yIu/y8OxNxrMxQSTLrAVZenhmqWhPZHUKLYVpt9sZTl9qD8Z8hBIm9PvS4xmJ+9NbKWVUtGv9nZkV7XqQlSDRqDoWzmxMTzzl+A7keB1gwmAaOjZKetAystn0eWqEOxLVn3luNSaIZ2QTipJscaN99+F4EzE5OSuptUik8vLcpWxP0ee5q9yeQoCknXepTOQhu3J7UZbKdk201xXtIZRBiATqtQvQkIUKfk9o5fa6fbZU+mp6eRZZHOSYLMQVZUj5eU5OTFVa15m+ZeNAglYhXtU+sMr2A5J4HozG8es71Zvw5NziIWNwDbBlfxHPAerv3Ffi2RryEJFjGASJYos76/UaE1kV7eGVCVqpvSRDfFsWiva4EqczosalKrVrD3uHwY1JTO7Zjcgh/DHNeD+ZeGrl1ZRWSpqwKJWiPZOwKJGls6bo74xGNgBxRLEASUrfhrOrzG6ajZChF3SwsdtWKbU5vAaTcRoA4chq3eVmwwhEwYFCmJBOVtMouTFJ6nkUiG5PWp61l2cKS6VMXp55GUzkYff5m9ZEfpeXZ3pi4TDYcCT6p7Pp8yxPENWGLDOkGvrTUqk/xCHavXTnAJfaBUEYkuX2yXka8Tx4Mp4aZ1rT3ow8gAKjA5J4diac98sdrkE+kv2HzrD6mUvsQyfbCeCJqD1WhZaBtbTyJiw/+qPMDuCLhRLb2zfCoa3vNFoxiPolXz0EEj1p2YzKVOPV391pSB8fTMQ50sTF5CgyqrDBliIunLDYserYJAFEZc3KRcCiQywjspotNUvuFOt7E8v1+1ujstrHa0ph+h6LdyWW65PKuKw+RA1SecYXNDmuPnBEY/K8+KEGyTAB2H3M6WBIjBmVZX1CIQgipoRlUixFjElSX3SiOn2i5kQPZzRFv6kl8dtHZH3bH6uk3rcj8VTL1fMqlGJ92H3+huKpp7A4DGorSzBNjAZnIjYQyxyrXbv+WO/GLbpN6j58vVxPQ57Jnli/f+yPchP3Pl904MdGFlrUz94dGfipOdmiNDG9qCvcd3uqrwoqHKpnrS8aITKAbQ8HJPHUIDB0Mn/7C0P1E++vLGx/76evW9vX9Xt77mYf319x6ZdnOp6+/0p9O77eHcFQvar2RD9/nozXUbrlGdbt07azQ3/f+/fH2TLw10Qvt7YfK2dD8Vk99I5o4LG/PvMBTTyHMYxhDGMYwxjGMIYxdLBfiOf9999PdXU1FouFOXPm8MUXX+yP3Q5jGMMYxjCGMYxhDGMIYcCJ57PPPsvNN9/Mz372M5YvX8706dM5+eSTaW09eJRiwxjGMIYxjGEMYxjD2A/E849//CPXX38911xzDZMmTeKf//wnNpuNhx9+OCk2HA7j8Xj+528YwxjGMIYxjGEMYxgHBgaUeEYiEZYtW8YJJ5ywe4eiyAknnMDnn3+eFH/PPfeQk5Oz66+ycmiOqBvGMIYxjGEMYxjDGEbvMaDEs729nXg8TnHx/04PKi4uprk52aD09ttvp6enZ9dfXZ2+wfAwhjGMYQxjGMMYxjC+ejAM9gHsCbPZjNl8cEwaGsYwhjGMYQxjGMM42DCgGc+CggIkSaKl5X9NjVtaWigpOXimCg1jGMMYxjCGMYxhDGOAiafJZOLQQw/l/fff3/V/sizz/vvvc8QRQ3v28TCGMYxhDGMYwxjGMPoXA15qv/nmm7nqqquYNWsWhx12GH/+85/x+/1cc801A73rYQxjGMMYxjCGMYxhDCEMOPG86KKLaGtr484776S5uZkZM2bw9ttvJwmO+hNiYtRXeABnjQ41SInPHInHB/lI/heiIIAy8Mel/eYxuX/2I6JtT+7b8Si9W19IFCFiSnafQxs1F88Qn03cniPy4koMMKXcjpxyO+rxK8jIShxR+N859ZnW1z6/rETTLyfFckFbP9W8aiFxfNnMs04cu+LPInZwoSg+9R97fd/6sdpnT13w2v396W9PSfw+gs42dv82+tuXlVhiXf0BfbuWpxjZKBNPuz7sPs/7GpMUm8UYSe2aF3s5cjLWi32kXb+fBh9q99L9MTozIqu/eW+/s4FEOPHMEoWDZ8BjaA/ONJC/+375Rm+66SZ27txJOBxmyZIlzJkzZ0D3N8adD8C6zoPHpH6kKw+ATT1tg3wk/4tKuxuAHd6OAd1PidUFQGOgp1+2V2zNUbcX7Nq39S3q+p5oAH8slPV6BWZ1vZZQZ1bx+eZcAFrD6eNzTWpcRyT172ASzVglGwDdEf3tuYzqteWJdqAoStJym+RCEoyJmORz0WEsAsAXa9ElnzZjKQD+aIPu/q2GCgCC0Z26y82GkQCEopt0lxuNYwGIRrfuQcD0YTBNByAWTrZ+G2qIRdRjNBhnZIyNRtcDu7+LvRGX/URiqqOIxTgyabmixAnGatXlhmTLO39UXdduKNPdvjfapC5PnAt7wxNVNQFOQ6Hu8p5IO7D7XNRDV+I8d5vyUsZ0JK6ZvDQxoJLO9oh6HyhIXG/poF27hWZ3xtg9od1rSiy9W09DQyCxvtXVL6ShIdANQGniXjiQ2OFVv7MqR+bvd39hU7d6/xrlSn9+HEhY36FypiqnG7M0cHnJA5LKTy9UH16r25p1H44HIibnqRnkrd0dQyrTOy5HfXhsHmBCPMKh3hxq/dkRtozbs6kPtZ3+fSPMdoOFXJMDgLpAe9brlVsLAGgMZrdOqUX9fltC6b/fIrMa1xZKv918k0oGOiL6L21uUwECIlElgi/WnbRcEERyEuSxO5JMHu2GYkQMyEoUfyx5Hw5jNQC+FMTSZhwDgD+6TX+5aQoAgeg63eUGqRJRcANRItGNujG7Yk1zASNyfCfxWE3a2MFGNPQxAAbzMRljw5FVAJiN03SXB6MbAAWjVIRRSiaHwVgDshJGFMxYDeVJy7XfzmGq0t2+J1IPQI6xQnd5V7QRALdJn7h2RlRimmfSJ66w+/zNTxPTGlZjCs36BFdDe7iLuCJjFAzkm9xpYwEag+q1WG5Nv929UedXr80KW0Gv1tOg3au0e1dfsdOvkcGBJV7heGxXYkJ7XgwFrO9UzzPt2XowYFW7anM5vXBgxd8HJPGcmFeIURTpCgep8/ZPBmyoo9TmJNdsJabIbOnOnugMNPYX8ayyqzfHhkA30X4ot1c51Jt/nb8TuZflcg2ViQdAXS/Ia1mCeLaGuojKmV8gSixqfHMGQqkRz65oFxE5kjIuPxHXHtYnnpJgIMeoftcaAdgbuQnC0J0gEHtCFCScRnW5R4eYOo0qWfFF63TL7XaTRjy36u7bapoMQDCiTzwFQcBkUglXJEHAUkEQ7RhMswCIhT9OGzuYUJTI7oynJT3xVJQ4kegaAEyJjO7e0L47q3GK7vJA4ru3GUch6JT2vZEdADgTLxF7w5PIZrtMycRTUWR6ImpGNDcF8exKkMpckz4hkBWZzkRWtMCcmni2hdV7UpElPdnRrq1iS0FWZdeGxEujdi1nC+0FtXIfiWOtRjzt+0Zck7bn60xsb2CJ5w5vB3FFwWW0UGx1Dui+eoP1Xep5Nik39Tl0oGFVm3rtacm7gcIBSTzNkoFJeerJsqq9aZCPZv9AEATGuzWSN5SIp/o7DDTxLLQ4sUgG4opCUz+U28usbiRBJCRHaQt592kblYnMRW8ynnkmF2bRiIxCW7g7Y3xJ4qHZFEz//ToMDiyiBYD2cOrjKdiV8Uy9vVyT+jbclYJ4apmqrkgy8YTdhKMnmjwgwmooRhIsKMTw6xBXu3E0AIFIioxngiyFYzXEZf3fzZwgnuHISt3le8JgPhqA6BAmnrHIMlD8CGIBkmFi2thobCuKEkAQrBgNY3RjAgniaUuQ+L2hkX7tt9gbXi3jqUM8FUXGE1Uzni6djKcv1kFMiSAi4TLqE0vtvEtFPLujncSVOJIgkWNMXbptTVQJijJkPLVrS3vJy4TmRKm91Jo9gfTHQnRG1D7dyn3MeO4mnv2T8azzq6X7EQOc8dSeDeNyCvdLP2k2UBSFzYlS+4SDhHgqisLKYeLZN0xLfHHaF3kwYLxbvWFpvSlDAVrGc4unDXkA2x5EQaDS3n/ldqMoUWZ1J7a3b+X2fSGegiBQZFEfls1Z9HlqD8PWcAfxNJlZdbvqb9Gapiyfl3gId6TIeALkJshpV6pyfCKj2Z2CeOYY1fKsR6ePUxBEHLuynjVJy23GUQBE5Hai8eT+W4OUi1FS9x+IrNfdv9mk9piHwp/qLv+f7SVK17HwZyhK6kzxYELLxhrMR+8SV6VCKLwIALNplm62EiAQWQvszh7vDX+C9NuN+sTVF1GJp9NUnbztWDtxJYKAhNOYXM7TXlZcxuIkYRpAOB7EH/cAqUvt2rmbZypMm6HclfFMkxUFaE5cL6UZMqMatB7PYkv2hK0+oN5jco12nEZr1uvtidrENqr6iXhq99GBznjuSTyHCtqCfrojIURBYHRO/3yfQx3NAR9tQT+SIDAlf2DbCw5Y4jljjz7PgwXjtIznECq1j3DkYhIlQvEYdf7ugd1X4ga5s7/6PBM38J2B/Uc8AUoSD6xsBEb55lwMgoGYEqcjnF4IpWV2WsOpiaeW8UxVaofMxHNXqT1lxlMVpGi9fnvDmegN9Or0eRpEOxZJJa6p+zy1cvta3eUW8+GAgVi8lmhMv5dUg2ScjCDmg+InHlmWNnawoBFPYxb9ncHQJwBYzEfpLpeVyC5hltYvuzd2ZzyTiWdcieCPqS8UeqX2nkS202ksRRSSxQvaOZOqv1M756ySA4tk143Rzt10ZXZVMKRe15l6PLVSe0kWpXNFUXZdtyW9IJ7aPaJiH8vkiqJQ149EMa7I1CYynlUDTDy3JIjn2Jz+aRHoD2jJm2pnLpYBFNkMJWhl9vG5hVgNxgHd1wFLPKcVqG/Tazpa9tkS56uGXaX2IZTxNIgi1U71xrXdM7DK9iq7minUepP6Co147nPGM7F+XS+Ja3EviKckiBRb1P00ZxQYqQ/i1jSkUntYd0TaUgrz8hIlzlQ9nu5dPZ5NKDpZWK3EqpGQvaGVaLVewb1hN6kl3lTE02pUiWcqgZEoOjCbZgIQShCxVBAEcUiX2+V4G/GoSrC140wFRYkRCn8GgNWiTzxD0S0oRJEEFyYpWbGuKDKB6HYAbKbkUruqaJcxiA7MUnKmSHvZ0Cuzw+6+4NT9nVqZPZ2wSL0O8k2pCWVnpIu4EscgGMjNIBjSrquSLDKe3liAQFx1Syjqhaq9r/2dbWEvwXgEEYFyW9+V4S1BL1E5jlEQKbG5+ry9dNiWEBaNdg094qk9Uw8GaMRT404DiQOWeI7OycNhNBGMRYeU2GYgMTZRam/we/BGsvEp3D8Y5VRvpgNtqbRb2b5vFkh7o6qPxLMikSHxRAP0RANZr6cRz2xK7bC73N6UgXgWJh6cbWl6PHNNBQgIROQwvpgnRYxKPFP1eLqMRYhIxJUo3ljyvnJMasbSG23UtVRyZqtsj+gLjLRMXSqBEYDVopK0YDg98YTdmcRYeGHG2P2NWOL4JeMURCn9gzscWYWieBEFNybjVN2YXcIi02TdfrtQvIm4EkDAiNUwImn5bmFRle762stGjo6wCHaX2lNmPKNaGT11KVArtednISwqNKcXDCmKsjvjmQXx1F4Wc41OzFKyD24qaALEvvZ3ltncGMW+Z+i0MnuFPRdpAH0so3KcOl83sPs5MRSgVQ21Z+rBAK0tccYA93fCAUw8JVFkSoF6c1p1kJTbc0wWSmyqKnAoCYxGahnPgSaeWo/nEMl4Wg0mCsxqtqDen/3voZXoWkPZEWjtgZitsr01lDrjaRSNuwQZqQRGWrapJ9qRMJr/X4iCRE5CgKRXbs9oqZToDfRFanT3r4laMlkqBaObkRV9D1Wt1BwKf6Kbld0TWiYxHl2DHB861xXszsJmY6MUShBni2Vexv7OVMKiQERTtI/ULZVr7REpFe27Mp7JNkywR6ndmMJKKZxeWATZZTy1ayBTmb0r6iEsRxARKTRnLjm3JK7Z3vR3wh6l9iEiLNpfivY6XzcxRcZmMFIyhBTtB1vGU1YU1iSslKYNsJUSHMDEE2BGgcrcVx1EAqNxiTe0oVRu195kt3v7hxCmgnaTrAt0pRXaZIuqRL9VfR+2t8tSqRfl9n3OeGZQtmsP2bZwe1qLqEyWSk5DLgbBhIK8y8x7b6QTGO1pqdSj0+epWSqF5S4i8WSHAq23MJDCUskolSKJuUCcYETfSN5sOgRBsCPLXUSi+iKkXccrFSIlMoSRwDNpY/cn5HgH0dDbQHb9nZqwKFV/J0AgqgmLUvV3asIifUW79rLg0BEWAbsV7Sa9Mr6y63zpS6k9mx7P1kTWP5OivTmoZUZzs8ok7kt/J+wWF/XVSqmqn6yUtD75EQNs6K4lI0Y68oaMol1WFLYkEjfjD5KM5/aeTrzRCBbJwLjcgf/MBzTx1JTtB4ulEuzZ5zl0MjMjtVL7APd4lthcGAWRqBynOahfJu7V9qw5GEWJiByjObhvFk3liQdJQ7AXxDMxHaUj3JOVl2dxgni2ZZhelG/KQ0QkqkTpjqb+PLtM5FMQT0EQyE1kk1Iq2zVLJR1LJNhtqeTR6fM0iDaskprR8uoo27Uez3C8hZiOZZIgCLsFRin6PAXBiMV8BKBmPTPBbL9O3afvnyhy38+t/kDY9w9QAkjGqUimw9PGynKAUHgpANYUxFNRZIKRDQDYjBmslEz6inbt99LLeKpWSqrwSM88PhDvIqqEEBBTWylFNQ9PfVIZjofwxtRzOyvz+Azl89awet0WWbIjhNrLouZMkQ38sTDdUXUsa8U+Es/+No/fXxlPjXhWD6Eye4O/h0AsikmUqHIOnUlKAwlNhD05vxijmHnsbl9xQBNPrVdhY2cbwZj+bOcDDeOGoKWSNnKsNeTDGx243lNJEKlICIx29kO5XRJEKqwJwdI+ltvLEus39WL0Zq7JiSnh5dmehZdnUaIEqD0kU8EgGsg3qzf4dOX2XRnPFKRSPcb0AqNMynaNeKRStmtTb7w65XaD6MScIKb+FH6euwRGKZTtsDvzFwx9lDJGg9F6NqJhLIrSQ9D7h0GfiBaPbiEceBQAi/PWjNmiUPhzIIIklWMwJI/BBAjHdiArfgTBjCVFRlPrq9XLeCqKsssCS8/D0x9r22Wl5NAhll0RTQ1fiEFM7o9UFGXXi06eSb8cqBnHWyUbNoO+6h2gbZeHZ3orJW0UbTZldthdau9NxrMpqO7DZbRhN1iyXm9P9HupfT9ZKWnJiKE0lnJTV2JUZk7efiFhQwEr2tT79Iz9UGaHA5x4ltmdVDhyiCsKr21PPx7vQMG0PJVsL22txxPJfkb4QCLHZKUsoYz8sq12QPc11qU+SFZ0JpuT7wvGuNQH5Iqu9LY7qVBlU49ng0efYOlBEAQqEuP2tngzr1dsKUBExBcL0BpKTz7LrSoh3O7XV4wDlFpUUljr354ypsisxtQH9cvdBeZqABoDG3R7KPPMqh9nS3CN7vpu03gA2oNLdZc7TZMA6Azpe3E6zLMB6A68g6IzAQnAZjkeEAiFF2Y0kxcECYvzhwBE/A8T8vxq0MhnLLoWX8f5oISQTHMwmOenjVcUhR7vXwGwWU5MSVK7A28BYDcdgqDTvxmNd+FNiI8cie9/T/RENhGVfUiCBcf/s3fW0VWcWxv/zXGLu0FCIAnuWrxoqZe6UnfXW71111t3A1pKSx0KFHd3AiEJcdfjMvP9MQmXr2dOcgIJ0ttnrSzWYt6ZeeecMzPPu/d+nq3zFx41f9fhus6K9aHFdrnkofm381dUuopwi040go5wrXKk8qBdXojEG5RrSAFcPhdFDpnkJhlbFlLkWgubxrXuayhJEjlW+X5NNgVvOt78bOhsPrJ6wlq3jTyrTJaan1dHg3q3gwONzcfrOPN0SZJY2/Q+6B5+4rSlXFoiP/ea36V/dzi9Xn7Lk0uShsT7l8B0BP7WxFMQBC7v3g+AT3ZtPO5RimOBjPBouoVF4xZ9zC/Yd7yncwinJmYAsLBIueauvTAyTk4BLi/b3z7Hi5Hnvaz8yBYu/SPl6FKutZwaV/AdkPqGy9extU6Z2B0Og1pP1xA5QrijvuXvvGdo96ZxgSOBGSE9EBAocRZS71aO1HaxyN1/chq3Kt5XicYeaAUDNl8NFS5/Aptslk3cK117sHv9yXKCeSwApfZliq0zY0yTAKiw/a44v1DjWDSqaLxiJfWOxYpjtNp0zKbpANTUPdnq80FnnIIx9N8AuGwf4Kj/V6vCpPaG170Ja9WFSGINam0fzJEfthrtdDgX4HKvQ8BAWMhtimMkSaTKOguAKPP5imMq7YuQ8GLRZmLS+vdhL7bJn3OcaQRqwT9iWWCTrZxSzMMVj59nkxcZqeaBitv3N25t2t4djUrZZ3BPw3YAMkOUa1QB9jTuxSt5idZFtxjxFCWRXQ3yc6RXWLeA45pR7Kik0lWHVtDQI1S5T70SNtXIZHlgRJeg9zkcKyv2ISKRERpPnOHorY9WVhzAJ0mkh8SQaAo76uMFwv6GKgqstehUakbGHdm1tzfcPh+/HpSf9WekttwF7O+Cn3P3UOW0k2AO4dROypmO9sbfmngCXJTZB6NGy56aStaWtU8U7ESGIAicnSZHI+blBbaTOdaYlCxHsBaV7MfXgb6qY+LkF8SO2mKqXbajPt6o2AwEBLIbyo6ozjNCZ6FbiLxy3lQTOIL4V/SPkK9ja21wBLp304txZ33L43uFyS/kfY37cfmUyx7MmhBSTDJh3tuoHJFMs/RALWio81RS7favodaodHQy9wMgz7rBb7tJE020PguAQttav+1Rhr7o1ZF4xEYqHf7G7dGmCQhosXn2Y3X7X7NK0BFluQCAKuvXitcAEBF6P6DH5V6Dw7ko4Lhm6C1XYwx/CRBw27/CXnc3koKyvyPgca3CWn0JSA2odYOxRM1GpWq5Bk2SvNTUPw1AaMh1aDTKop0G53LcvkLUQigRpjMUx5Q3kfxY82mK20usfwKQaB7vt02UvBQ1fc+dLCP8trt8Nkrs8vOqi2WI4vFzrNsA6GpR7jEvSiJ7G+QFVfdQZbsogJ318nl6hylbRjXjoK0Yq9eOQaUn3eIfwf0rtjTdqz3DUoO2UpIkiY3VMvEcFKVcN9salpXLi/kxsZlHtL/f8ZoW7WPiWyfbR4M/moIQI+PTMGuDt57qSCwvyaXW5SDGaGZEfPCLh5MVkiTxyS75+Xpl9wHHrLTgb088w/VGzusq13t9slM5bfd3w5lNxHNN2UHK7EfWZ7y9MTgmhTCdgRqXnc3Vwaed24pYYwjdw+KRgBXtEPWM1FvoEyGnH5ZXHFm0dlCkvIrcWNN69LIZfcLTUSFQ6KgIqs6zV5gcmd1Zv6/FyF2CIZ4oXSReycvexsDX0/zi3tugTDx1KgOdTTJxzGncpjgmzSKnu5WIJ/yXgBRY/dPlgqAm0TwOgBKbPyHUqkOJNI4EAkc9oy0XAzKpcnmVSzw0mmRCQ64DoLb+qaBIpN50EaaItwANHsdc7LU3I0kd65vrcS7GVn0lSHY0+lFYIr9CULVuP9No+xqv9wAqVSRhIbcGHFdlnQlApPk8VCr/OkO3r4Y6p0wcY81T/c/jzqPRk4uAhniTv5F9uWMnLrERvSqUWIN/mr7AthURHxG6JMJ1/ilOr+ghzyoTxq4hysSzyJ6P3WfFoDLS2Rw4ctMc7W9ehAUeJz8/eoSlow5gP3U4ttbJ4/uFB0/Y8m0VVLsb0ak09Aprndz+FR7Ry5oq+bkyOi6rzfv/FT5JZEX5sSGeC4vl58/EpPYhzO2BeXlyuccZnbujVv3t6RHrygrZXVOBQa3h4qw+x+y8f/9PFpjRU07dLCzI4WBD+5iLn8hIsYQzKCYZCfg5v2WrmGMFrUrNuAT5QfZHB6fbmx+Yy8rbJ93eHEk40nT7wEg5krGxRlkIowSzxkhGiEx4twQR9cwK6YJGUFPlrm3Rz1MQhEMv3JbS7VkhTcSzcWdA66VmAtAciforUs2DAChx7MHl848+dzLLxLPYvhGv6E/cEs2nyvvbliIpGM3HNRGgCttvimRbr+lMiGEUIFFlDWyDFB5yGypVBB7vfqy2WQHHHQ6d8SzMke8DOjzO37HVXIMkOoLat61wO37GVnMt4EJjmIQ58lMElanV/UTRSl3DywCEh96DSqWchnV7y6h3LAQg2nKJ4phK+x9I+AjR9VRMs5fY5GhnrHEIOrU/If5vmn2YYn1nc5o9zTxY8fwF9mw8kguzJow4g3Ikak9TdD4jpCdqhXOAbCVW5ixHhYoeoS0TtZ1NZSu9QjNaHAdytHVbU1lMv4jgCVvzYrRPeCp6ddvbFG6szsfmdRGtt9AzTDma3RZsrymmzu0gRKunf2TH1fuV2OrZWVuGShAYn9ixBDdYWD0uFhXJz9qzuyi7Ovzd0BztPK9bT8L1xmN23v8J4tk1PIoxyWlIwOe7Nx/v6RwTnHUo3X5iEE+ASclNdZ7FLUfljhZj4uXzrCw/gEf0Jyxtxeg4mXhuqM7D4XW3ef9+EWmoBRUljhpK7MGr7ZtfYEHXeVrkF/LOVuo8e4f1bBoXuBQj1dwNvcqA1dtAsUM5WpjelPLMte5QNJIP18UToUtGQqTAttVve5Q+A5M6Gq/koNThvz3GOAitKgSXr5pq53a/7dGmU1Ghw+7Nw+ZRXsxEWy4FoNr6TUCRkUoVSnjI3QDUNryEKAZXoqE1TMIc9RkIRryuZVhrLkdSsHc6Grjs32CvvRXwojWejTniPQRBH9S+9Y3vIIpVaDRdCDFfHnBctW0O4MOsH4RRpxx9qjiUZvePdgIUNxHPRMupitsPEU+FNLskSeTb5BdgqkW5vvO/afY+ATsNNUfns1pMs8uLrXRLF0yawOTdJ4nsapDvu95hrRPPfFsZ9R4bBpWOzJDgI5eH0uyRR1Zb15yFGRWb2WIHpmDRvFg/JbZrh6ZdFxbLz6iB0clEGwK7DxxLLCjYh9PnpUtoJL0jj426+3iioKGOPw7K3/dVPZTvu47C/wTxBLimpxx9+WbfjhOqnWRH4fTU7mgEFbtqysk5QboYjYrvgl6todBWR3Z9YKueo0XviEQidCasXhdbqo++rjfdEkuSMQL3YWmttsCs0dMjTI4etCXd3pyy21q7Pyii3vyC3NnQcoS0R2gPVKgodZZRFaB9pkaloVuIvHgJlG5PNKZhUofgEh0U2ZXPmdYU9WyOaB0OQRAOpdsLrav9tqsELQlNadsSm79ASKOyENm0vTxAuj3cOBGNKgavWEmdI3ANZ4jlCjTqVESxkgbrewHH/RVa/SgskV+DEILPvQ5r9cWIYvtkVVy2z3DU3QuI6EwXYwp/HUEILirm9ZUduo6I0IcC7idJPqqbREXNJP2vcPuqqHWuByDWNMVvu91TSp1rNyCQYBrrt73eXUS9+yACapJN/vWbNe5CGjzlqAUtySbldF9zOUdXSz/F7U6fgzybTGZaIp47DtV3tpxmz7cVYfc5MKkNpFlaj/w112L3Dk9HEyRh84o+NtfKdd9HUt8pSdKhLMyYuPZJVy8/RvWdzcTzREyzn5XW44Qxs+9IfLZ7MxIwJintmJjGH47/GeI5OimVruFRWD1u5uxTfpH+nRChNzI2SVYKzss9MaKeJo2OUfHynDoy3a4WVIxqUrcvKzt6Zb8gCIxpqp9qLuRvK/5b5xl8ur1nWBoaQU2Fq5bSVmySIPg6T7PGRBdLl6axgaOe/023K98vKkFNukUeE6jOszmClW/bpDinlKZ0e4FttbI63tKcbv9TcXucSRa6BEq3C4I2KJGRIOiICPsXIEcKvb7gF0YavSz0EVQR+DzbsFZdgOg7Oh9dZ+PbOOofBWTzemPYCwHbXCqhruElJMmBXjcYk3FawHENzhW4fUWoVWFEBBhXYVsAiITq+mDU+pOwEtsSAKIN/TFo/P0YC21rAIg39kWvkIZvjnYmm3qhU6gvtXsbKXHI9016SABiat2DT/IRrYslRq9szeOTfOxpkA3ye4W1nEptdofoEdo1qF7lW5rqO/u3ob5zX2MJVq8Ti8ZAVmhg+6dAyGksp8RRh16lYWj00auRyx0N7KkvQwBGxx2Z0CkY1LrsrK+U7ekmJrUeTT4WqHBYWVWWD8BZqX//NHuj28U3++Qs0tW9jm20E/6HiKcgCMzoMQCAT3dv7lBl9YmCs9LkG2he3q4Txkqq+UHTvOLtKLR7nWdTRGFFRXaL7SYDYVBTneemmgNBfxcGtY7uoalAcOr2zJA0NIKGGnc9JS0YxMN/bZX2NLQuMMq1ZuPyKXvCNqfbA9V5Jpv6oBa0NHjKqXH7R5+TTANRCzqs3jJq3f6q/1jjMNSCAbu3lDq3f41tlGksasGEw1tAvctf/Q7/FRk1Opfj8gb2YzUZT0enG4Ak2alreCngOCVodH2wRM1BUMUievdirZqO6FM2z28JkiThaHgRZ+PzAOgtd2IIfaxNERi3JxurTa5pjQh7tMV9m8l4IFERQKn1ewBizcrEtDka3VyT+1cU2GTxWCdLABsla7ON0iDF7QesO5CQiNWnEKZVNkgPJs1+0FbQFMU0kmZODTgODqvvDCLN7hN9bK+TiXG/iOAJW/MidEBEl6DI7V+xrCnNPiS6C8YgVfQtHq8p2tk7IolIfcelv5eU5OCTJLLCYju8JWew+CV/D6Ik0S86kdTQE2NOHYnv9u/E6nGTHhbJ6CTlhhIdif8Z4glwbteehOkMFDTW8Wdh8JGnkxUTkrti1ugostWzqbL4eE8HgPGJ3VAJArvryim01nXYeUbGdkUtCBxorKLIdvSpzwGRnbFo9NS4beysa/tn2Su8E3qVlhq3lVyrcrcfJfRvepE1R1Ragl6tIzNEfoi0VufZvUlYsadxb0AiHKOPJ1IXg1fykmNVFlY1C4yK7PtxKAiIdCoDySY5rZmvkG7XqAwkmuQVd3Md4P/fbiTOdArwX7ue/7/dTKxZTv+WWucqzlGv6USIQU7JtyQyEgSByLDHALDaZuIOUDcaCGptJpbo7xDUSYi+XBqrzsPjWoXXvSXoP0fD47isstm7IfRhjKH3tDntV1v/FCBiMp6GQa8s1oFmUZFcfhBIVGR176XRvRMBLfGWM/22O73VVDm3ABxyIfh/5/DZKLVvBaCT+RS/7R7RRZFdjrykWZSJZ45V3j+Qmh1gzyHiGViZu6dR/g1nhGS0WA/pk3zsbpDfD8HUd+63FmH3OQnRmOhiCT5yubFaLrsZFHWE9Z2HbJSOXs0uH+8Y2Sg1q9mTT4xoJ/zXerDZivDvDFGS+LRJVDSj50BUx6Gs4H+KeJq0Oi7Jkh9eH+9Sjo78nWDUaJncSb65TxRPz0i9icExcvH9og6MeobqDPSPlM+zrB1slbQqDSNimqOobVe361Qa+kWkAh1d5xmcn2e6JR2toKHeU0+ps0xxjCAI/7VVCpBuj9DFEq1LREQk16o8ptkQPM+qfM81q9sLFOo8AZIOqduVjeATLLIJfIVtPl7RqjjmvyKjbwOKjAAM+qGYDFMAkdr6ZwOOCwS1Jo2QqLmo1GlIviJs1RdhrToz6D+37VMAjGFPY7Dc2ObzO5wrcTgXAxoiQh9ucWy17VtkUdFgjFplElDSKJP5aNN4dGr/NHqpfRkgEa7vgUnrb4NUbF+PhI8wbQphOv80fbF9J17JjUUTTZTOX60uSdJh9Z3KxLPGVUmFqxQVKjJCAhOHPQ3yfdu9FTX7AWshDp8Ti8ZEqrl1ItnsOtEnPD3oyKXL52FbXT7w32xIW1DjsrKjTralG9UO9Z1un5c1FXLGoVmc2RFweD2sKJPPM+kEqe/Mbahhe3UZakHg9M5/f9P4PwsPcLCxjlCd/pDV5LHG/xTxBLiie3/UgsCa0gL21HScwOVEwdlN6fZfD+5tF4V3e+C/6faT01ZpZeWREeZBR2CrlBXaGb1KS53HykG7MkE8HD2DrPPUqbSkW+RIS/MLWfH8zXWeAQRGEIytkhx1K7Jvx6Ngm9TcyabCuQuH1z86HW8aiYCGRk8eDQrp+DD9AEyaNHySPaCnZ7hxwmEio4UBrwUgIuxhQI3D+QdOlzIZbgkqTRKW6O/Q6MejUqe07U+TgSn8TfTmK9t8XkkSqa1/EoAQ8xVotYG7wciiIjn6G0hUJEpuym0/AZBgOU9xTLFVXgwkKZjGQ8tqdjjcRmmQYmS32l1KnacStaAhzaL8ktzbKCvVU81dMaqVlepe0cu+Rvk50D0kOBulnqHdglKKH4l/5876Atyil2h9CKnmtrelXFW5HwmJ7qGJ7dKtaEPVQew+DzEGCz3COk7RvbI8F6fPS5Ip7IRpk9kclBmZkEa08cRQ2HckPm7yM784sy+m42Tc/z9HPBMtoUxNlV/OL29accLUPnYURsR3JsZoptbl4Nf8E6NfffNKd0NVIUW2ug47z9imlfu6yjxq2qGL0bAYmThmN5RR6qhr8/7NKbXNNQeCtmXSqjT0CpMJxPrqPa2OzwxJRafSUudpJM/WslF/c+Sn5faZPREQKHMWUxkgMtocicpu2ISo4LcZre+MRRONV3IfEpIcDos2jih9N0Ai37rMb7tWHUKsSW6xWdD4i992QRBICJGJUWHDF4gK1k6yyOhCAMob3mux1aVW2/WQ/VBV7X34jkClrlLHYon6nNC41W37i12MznROm88HUN/4Fm7PDgTBQnjoXS2OrbX/cpioSLkTUal1Hh6xDr06jqgms/7DYfeWU+mQ1e5K9Z1e0Xkoiq2UZpckkZxGWXgUyEZpb4PcfKCTKUtReASwo162yMsMDaxU32/NwSW6MKvNpJiSA44D2FIrizF7BtEm0+Z1sLM+D2hbfefqSnnRPTAy/YgU1M02SsNj2kcENL9YJl+j47p1qKJ7Xr78rJmUnHlCKMedXg/f5ciL6nPS/v6iotUlB1ldWoBaELiySfNyPPA/RzwBbu83Aq1KxaKCA3yfc2KkoDsKGpWKqzLlh/qLW5bh8AZOMx4rJJnDGBmXhihJvL1rZYedJz0kml7hibhFH18eWHfUx4vSWxjU1Ht9bkHbu2BlhCSSZIzC7nPzR9nWoPcbHdMPgF9L17QqbNKqtAyMkF/AC8v9OwIdjkER8u9iW9126tx1imNMGvMhwcaKKmU7oq4hfTGqLdR7qtjT4N+lSBAEuofJ9X+ba35QPEa3ULlOc0ftN4rkNS1UJpYH6mfj9Pp7oSZapqNRhWPz7KO44SvFc8SGXIlKsGB3b21R4Q4QHnovanUSXm8uldXXIklt9289lrDZf6KuQRYkRYY9ilod2B7FK9ZTVCtHRmNDrgvYqehA7SsApIReraioz679EAkv0YYBhOhS/bbva/gNl9hAiDaBeKO/6OeAdR31nlL0KsuhLleHQ5R8rK2eD0CfcH/iClDlqmBXvVxjOiBCWbwEsLxyBQADIwe0GMUsdpSzs2E/AgJDI1vv5LKwbCNu0UMnUxydTcFFCp0+D7+WyM+P8XGBxVCBUOlsZEmZvAidlNCyLVQwqHA08mOhXGd7dqfAdbRHi+y6CuYXycGP6WnHrktOS/h4zwZK7A0kmEIOlaX9XdHodnHfCjkjdHFmX5IsRx8pP1L8TxLPzMgY7hogr+CfWLuYEmvDcZ5Rx+Ka7oNJModSYm/gw93rj/d0ALi9lyz2mJu/nYPWjukmJQgC12XIL6yZuRuweY7ev/XCVNmHcG7BRty+tvXoVgkqzksZBsB3Bcr2QUoYF9cfs9pAiaOKTbWtp/mnxMu/7aUV63F4ldXoAMmmJLpZuiIisrwq8AJgTMxkANZWL1NUt2tVeoZETgJgdZV/RBJgQMRZqFBTaN9OucO/9CEz7HT0qhAaPEUctK7w255gGku4vgc+ycG+uk/956COID3iHgBy697E5fUXcGnVcSSG3wtASd0LeFqwTFKro4iL/hJBsOB0raaq9r4TNjvidG2isuZ2AEIt1xFiuaLF8SV1L+IVK9Fr0okLvUFxzIHaV/GKdZi1GSSHXua33eYpIr/hRwB6RN7it12UvGyvkVP5vSMuUuxWtLFGVsv3jTgNncq/a8reho3Uussxqi30ixirOM+VlYuQkMgK6UW8Qblzj9VjZUONTPTGxYxRHNOMP8rk+2BARE9iDcoK+mZIksTPJfLi7sykkUFH8BaXbaPeYyfeEM4pMW2vKZxbsAGvJNI3ohNZYf51tW3F5zlr8Ig+BkSmMCi64/qTv7lLvq9PS+lO1gmQZq+wW3l7pxxxf3DAWIyatneOOpnw9LolFFkbSAkJ46HBLd8HHY3/SeIJcEPvIfSPSaDB7eL+FfNP2JdKe8Cg0fLgADni9O7OtSdE//aB0cmMju+Cr4OjnhMSu5NmiaLB4+Sb/KMXlI2N606sIZRat42FZW2Plk9LGohepSXHWnZIXNAajGo9k+JlwvtzcctRTIDeYZkkGmJxii5WVLUcmR0bKz+AllUsDxhN7R7ahxh9PA6fnQ01yt/V0KgpqFCTb9tNicO/DjNEG0NmqLzYaCYch0OrMtE9/FwAttfM9LsfBUGgZxPByW2Yg8PrTxoTLdMJ1fXFJ9nYX/uC4jxjLFdi0vXGJzVwsPqeFnuz67TdiYl6H1Bjs39LfeObAcceL3i8BVRUXwm4MBomERH2eIvj6x2LqLLKEeFOkc+iUuiCVO/cSql1DgCZUY8rksY9tR8g4SXWOIJoo3/KLq9xKVZvKQZ1GBmh/qn8Msd+iuzbUaGmf4S/Wh7+u4gZHDkJncp/nm7RxZrqpQCMblocKWFl9Wo8kpdOppQWbZRcPjd/VsiZkSnxowKOa8a2uhwK7OUY1XomxCkr8v8KSZKYUyCTnXNThrXZRskj+g5lWy7qPLRN+yqh3u1gdtNz8bpM/3KK9sLu2jLmF+1FAG7r2fpneyzw4tZl2L0e+kcncmbq31vN/mfhAWbv244AvDxqKhZdcN3POgr/s8RTo1LxyujT0Ks1rCjJZ2a2sjDi74LTO2cxMCYJh8/Di1v86+iOB+5sinr+cHAHeY3Bt5JsC1SCwLVNUc/Pcta0OUr5V2hVaqZ3auqCld/29H2o1sTkhH4AfNf0AgoGZyTJ17CuehflzpY/K0EQmNQU9Zxf1nId85DIQZjVJqrc1QHN5FWCitExEwFYVvmH4vHCdNH0CpcFJKurflU8zsBIOV2e3bCMRo+/wXrP8PNQCzoqXXsodfi3to01DifK0B9RcrG39iO/7YKgIjPqCUBFhe1Xahz+wiBBUNMp8iUEwUCDc9mhlHMgmAzjiQx/BoC6hueprLkdn+/4dwKTJAmr7TtKK6YhitXotL2IiXynRZN5u3s3eVW3AhLRlisIMfinpkXJS3bNEwAkWM4l3OBPqBrcuRQ0yt9xj8ibFee2rVYuZegZPh2NQip/U42sls8MHUOINsZve4kjlzzbLlSoGBbl3y0JYEPNKuw+G1G6GHqG9VMcI0kSSyuWAzAudmyLUcnV1Vuwem3E6CPpH9E6EfmpKdo5IW4QZo1y/elfsau+kOzGYnQqDWckBba6CoQl5XuodDUSpbcwIeHoydLM3A3YvW4yQmMZE9dxNkpvNEU7p3XqQUaY//d9rLGjuozvDsi1nY8NmnBC1Jt2FOpdTh5YKZesXN1zEMMSgm/p2lH4nyWeAOnhUTwwSCY/z61fSpnt+EcCOwqCIPDYoAkAfJ+7k21Vpcd5RtA3KolxCV0RJYn/dGDU8/SUPsQbQ6l0WplXcPQLjHNTBqER1GyvK2RPfdtNws9LkV/4Syt2UukMrswjxRTLgIgMRCR+KWmdsI6LHYpW0JBnK2K/NT/gOJ1KxynRMmFcUhl4QTI0ajQ6lZ4yZ3FAT88R0acDsL1uBY0eBXW6sRvJpj6I+NhS+5PfdqMmgsww+Rhbqr/w2y4IwiGik9/wAzaPv59qiL4HySGySntf9b8RFWozTboepEa9AUCl9TMqGj9TvJ5mhFquJCzkLkDAZp9DcdkoGq1ftChQ6ki4Pfsor5pOVe1tiGIVWk03YqO/QKUKrMj1+Mo5UDkDUbITYhhJSoRyZLS4cSZW9x40qjDSI+5THLOn5n1AJME0lkiDvyCjwLaaGlcOWsF4KIp9OBo8FWQ3yGRwUKT/dvjv4qVX2AjCdP71qpIksbJSrjkeFTMxYN1mduM+Sp2l6FV6hke1HCFcUCaTo0lxp7Qaiaxy1bGqUiYuZyQqK/aVMLdQvncnxvclXNd2BXXzYvfclIFoVf6R6LbA4fUcqn2/LiP4UoG2YkdNKYuK96ESBG4/AaKdkiTx5Eb5t3NOWk/6xyiXaPxd8NyGpVTYbXQJi+D+Qcf/84f/ceIJcFWPAfSPSaDR4+axNYF7Of8d0Dc6gXO7yMXoT25cdEKUF9zeS74RfirYyYGGjokk6VRqruoqk72P96/Ce5Rdq6INIYeiDUcS9cwITaRveCo+SeTHouD3PyNRjnrOL12LW2w5chuqtXBKtCweWlDWMqlvTrdvrd1GrVu53taoNjE4Uj7/yiplP80UUzc6mTLxSV7WVy9QHNNMNLbV/oZbdPht7xNxCSo0lDo2U2b3XyTEGAcRaxyGhJe9tR8qnqNLxB3oVNHYvfkU1H+iOCbCNJXE8AcBKKp9gnrHEsVxh8aH3U9CzC/otL0QpTqq6x6gtOJ0XO5j135XFO3U1j9LSfkEnK7VCBgID32QxLhFaNSBa/1E0cGBymvw+ErRa7qSFv2uYu92l7eC3NrXAUiPuFvRt7POlU2x7Q9ACBjt3FL9GQA9ws/FoPYXMGyp+QkRHymmPsQZ/aNsVk8d2+tkEjgi5nTFazpoP0CR4yAaQcuwqNGBLp2lTYupYVFDMKr960ibkWcrIrsxD7Wg4tS4wCKlZvxWshYRkd5hXUizBEdcalyNLC6TRTzNi8+2YH9DGZtq8lELKqZ3anu09K/47uBmat12UswRTEnqOEX3m7vkRcYZnXqSHnpse4Ir4beCbDZUFGFQa7i///GtdexorC0tYFa2/Jt7YeQUDCdIHev/PPFUq1Q8P3IKGkHFgoP7+T2/Y1s5Hm/c338MRrWWTZXF/Jzfuj1PR6NPZCITErshShJvdWDU8/zUAYTrjBTYallQcvS96y9sqq/6vWQ79W57m/dvfvHMK1qPpxUS2YzhUT2J0YdT57GyorL1yO3kpnT7yqpNNHoC20klGRPJsHSTRUaVgb+DU6Jly5xtdetp8NQrzzFabqu4rmYBXtHfQSHdMpQIXRIu0crOuj/8tlu0cWSEyTWBW2o+VzxHM+E52Pgzje58v+0aVQhdI2VSmV//Lg6Psq1UXMhNRJkvBETyqm7B4W7ZV1avH0BC7O9Ehj2FIFhwe7ZQWjGF6rpHEMWOFSjaHX9QXD6G+sa3AA9Gw0QS45cRHnoHghDYi0+SRPKr78Tu3o5aFUHXmE/QqMIUx+bUvoBPshGq60Oi5XzFMbtr3gEg2TKJML0/aSyyr6PKtReNYKBXxIV+290+O9vrfgNgUKSyN+i6mgX4JC8ppm6kmJSVxisr5cVP/4ihmDX+/d+hWVQk1y+OixmrOKYZzdHOYZH9iNApfz7N8Io+fi2VI5dnJgVfF/lT8QY8ko+eYSl0D2vZ0kkJ3x6UhaHj4rKIM7Y8x9bgFn18ul8uRbm62wg0qo6hAtuqi/mzJAeVIHBbz46rIQ0WTp+XZzfJHdBu7DmMBPPxU3Z3NJxeLw+ulAMAl2b1ZUi8fwOH44X/eeIJkBUZw019ZSLx2JqF1LsCK4FPdsSbQri5l6ysfn7zUpwngL1Ss8L9l4Jd7K/3r/1rD5g0Oi5Ll7/jj/atPOpob7+ITmSExuMSvfxYtKXN+4+N60mULoRqdyNLy4MTKalVaqYlyIT1p+LWSXpmSBqp5iTcooellS1HVg+JjCoDi4xSTKmkmtLxST7WViun5XuGDSNMG4XNW8/2Ov85CoKKgZGyT+Wmmh8UrZP6Rl6GgJpi+wYqHP6fTaShN/Gm0YDIntr3FOcRZz6dcMNQRMnJ/ppnFMcIgkBK5DNY9MMRJSsHKmfg8bX8+xMEDaEh15IUvxKz8WxApNH6McVlo7Da57V7FsHrLaS86ioqqq/E5ytCrU4iNupTYqM+R6tpvVarpP4l6hy/I6AlPfoD9NpUxXE1jjWU234BBDKiHlesFa1x7qDMvhxQ0T3Cv6vS4dHO7uFnY9T497zeWf8HLtFGhC6ZLpYh/tcreljXZKE0IvoMxbnavFY218rEb1T0BMUxACurVuGVvHQ2dSbNkhpwnMPrZFmFbAM2OQhR0aqqHdS4G4jQhnBKdHB2SF7Rxw+F8j04PSX41HwzGj1OfimWF5sXtoOo6NfCHZQ6GojWWzinU7+jPl4gNNd2nt25N2khLbsEHAt8vHsDxTbZPumGnkf/OZ7IeHPravIaaok1mXlw8NjjPZ3/h3+IZxNu7Tuc9LBIKuw2nt9wYohvOgrX9RhCounEsVfqGRHPpKRMJP5rudERuLTLEEwaHXvry1lRHnzbSiUIgnBIVfrtwXWt+mv+FVqVhrOTm6yZCoMXGU1NGIZGULO7IZ+cxpYN4gVBYHKc/CJdUNYy2R4cORCz2kS1u4YdAURGAKfEyFHPVVV/Kl6zWtAwLGoqIKuSlc7ZI2wiBnUI9Z7SQwbihyNEm0C3UFmlvKXmM8V5NEc9i6wLqHf5ZykEQSAz8nEENFQ5/qTSrlweoBJ0dIl+H70mDbeviAOV1yKKrS88Neo4YqLeJS76GzSadHxiBVU1N1FedREeT/CdqQJBktzUN/yH4vIxOJwLAA2hIbeSFLcck3FKUPV41dY5lDe8DUCnyBewGJRftKLkZl/1vwFICrmEUL2yN2RztLNzyBmKvp0l9k1UOHehFnT0jrhI4Tw+NjX5uA6MPAdBoY5ye91KbN56QrWR9AwbpjiP9TUr8EgeEo2dSDUrG6hLksTSymZRUeBUPMCyyg04RRdJxjh6BWEa32yhdFri8KDrLFdW7qHCVU+E1sz4+LZ7d/5ctAWHz00XSwyDotLavP/hECWJj/bJ13Bl12Ho1UdXKxoIm6uKWFZ6ALUgcOsJEO2ssFt5p8k+6YH+f2/7pD01Fby/XX63PzV8IqHHWcX+V/xDPJtg0Gh47hT5ZTczexvrSguP84w6DrK90lgA3tm5lvITwF7pjqZaz98L95Bd1zGtTMN1Ri5MleseP9h39Gn9qUl9CNEYKLLXsqqy7UT2rOShqAUV2+ry2dcQnEgpUh/KyBjZfLlZVdsSxsQMxqDSy8bYLfRvl0VGcg3n0oqlAccNiBiGUW2ixl3JnobtimMGRU5EK+godeaRb/Mva9CpDPQNl1PymxSslQD6Rl6OgIpC21oqnf5ipnB9Jklm2Tt0d4Cop1mXTqewqwHYX/0MPoWaUgCNOpz0mM9Qq8Kwu7dwsOaeoIVDRsNokuIWEx56PwIGnK7lFJePp7b+BURJ+XytwelaTUn5RGobnkGSHOh1w0iMW0Rk2MOoVMotIf+KRudaCmrkcoP40NuIaupnr4SC+k+xe/PQqqLoEn6n4phKx0YqHGsR0JAVcb3imOZFQlbYWZg0/tGtnMbV1HvKMKhD6BnmH6mUJOmQhdKwqNNQK9g4yaIieRExMvrUgARcFhWVYVDpGdaCqEiSJBaUy4vdyfGtC2zybaVsq8tBhepQ9iEYfFcgp7XPTB6Cro2iIEmSDqXZL+w89KhFQItK9pJrrSJEq+eitOBsoI4EzUGEc1P70NniH/0+1nh563JsXjf9oxM5K+3va5/kE0UeWDEfryQyuXM3pqSeeMb4/xDPwzA0IYVLMuXODQ+umo/Te3TWOycyzkjtzoATyF4pKzyO01K6d3jU88quw9AKKjZVF7C5uuCojmVU6zgrRfYwPBKRUYwhlLGxcnSpLVHPsxLl6MGf5ZuwelomN0aNgTGxshChuY4tEJojQ1vrtlMTQGSkU+kZ2iTmCCQyMmlC6B8h+8auqvpZcUz/iDNRoaHYsYtShz+xDNMlkx4ik5Ot1cq1nt0jbwBUlNqWUONUjtKmht2EXp2I01dMfr0yQQUwaNPoEv0BAlpq7T9TWv9awLF/hSDoCQ+9i8T4pRgN4wE39Y2vU1I2lrqGV6lreCPov8rqmymrPA+Pdx8qVRTREW8QH/M9Om1m0PNxevLIrboeCQ/hpmkkhN0TcKzDW0x+vRzJ7Bp5P1oFMZAkSYeinamh52DW+otpSu1bKHNsQyVo6RNxseK5mv1b+4WfjlbBYinftptSZx5aQcfgyImKx9hv3U2FqxS9ynBI7KaEJRXNoqJhLYqK9lnzybcVo1NpGRvTeur152KZQI6I7kWMIbzV8QC51nI21eaiQuCclLand9dV55Jvq8Ks0XN6cr827384JEniw6ZF9yVpg7FoOyYStrGykBVluWgEFbf0OP7Rzp01Zcw5IC+UHx0UeMHyd8BnuzezraqMEK2Op4YHLkU5nviHeP4FDw4eQ6zJTG59La9vaT2idLJCtleS06bf5+5kT23HRBnbgtt6jkIA5hftZWdNx9g9xRlDOaupLdz72Ucf9bygs5wuX1W5nwJbdZv3n95JjposKN1KvTu4fvI9w9JIMyfgEj0sKGud8DbXra2t2UqNW1kUBJBoTCQzJAMRkWVNaUolNIuMdtVvocalXBPZLDLa27CBapd/j3eLNupQG80N1d8pHqNf1BWAwEHbSqqc/sKfUF0XOllkIdKumjcV0/pqlYmMyIcBKKj/GKs7cNQ3xDCcTpHPAVDW8AZV1pkBxypBq+lMbNRXxER9hFqdiNdXQF3DS9Q1PB/0n83xAyAQYr6CpPgVWMwXtOkl6faWcaByBj6xDpOuH6mRrymmtEEmIfuqn0KUnITrBxNvPktxXJl9JdXOLagEPVkR1yoeZ3O13E0qM/R0zEq+nPbdlDh2oxa09I9UNoxvXqT0ixiLKYBgqDnaOShyBIYAhLLB08jG2iZRUWzLquX5pfJibGT0QEK0Ldsb2bxOFpXLtaDNvrrBoDnaOTq2B3FBktXDMTt/bdM5+2HWHB1RXF2Zy866EvQqDZd3VS5lOFpIksRrO2Xif15aH1Is4R1ynrbM54XNS5GAs1J7MCAm6bjOpyOR31DLy5vk3/RDQ8YSZ1a+j443/iGef0GY3sCTw+XV9rvb1zE7Wzmd+HdAv+hEpnXOQgLuWvnzcRcaZYTFcHon2dbjwQ2/4DpKs/dAuCbjFNSCwPLy/SwqUfakDBadzFGMjMlAQuK1PcoWQi2hb3gq3UJkEvnRgeDsvARB4KwmNe3sgsWtRj3TzMlkhXTBJ4l8U6Bs7t6McbFjAfijbCENHuUSjHhDIhkhPZGQ+KV0juKYWEMyGSEDkJCYX6ocsWy2VtrXuIICm79KP1zXmfQQmeQuL38en+jvydk98gYENFQ61rOn9n3F80SbTiXKOBYJD9srbsLtC2zAH2W5gLjQmwAoqHmQkrqX2uTXKQgCZuM0kuKWEx76IBbzJW36CzHPICH2V6IiXkCtalt60u7eSXb5mbi8uWjViaTHfKTYh70ZeXVvUe1YgoCmSVDkT3CtngI2VTwKQJfQ8zFqYv3GZNf/TKljCypBS9/IS/22i5KPxeXvAtA9dBxmjb9NU75tN3sa1iMgMKJp0fJXHLQdYGudnHIe2YKo6Mfin/BKXtLMqaSaA7eAPGgrZnll8KKiWQcXYve5SDHF0j88OLP1fFsFPxXL5zi/U9tFRRuq81havhcB4dAi90jh8nl5Zpvcq/v81AFE6dvuIxoMvsndytqKg+hU6hMi2vnVvi2sKM1Hq1JxT7+W631PZlQ6bFwxfw52r4chcclc3JS9PRHxD/FUwNTUDG7pK68GH1q1gIUHA0dJTnY8PngC0QYze+sq+fdG5dTpscTD/SYQqTexp67i0Kq5vZFqieLqbvJL4N9bf6XBfXQuBnd2n4RGULGkfA9Ly9tmUSUIArdlyC/a7wvXBl3rOSl+CCnGWOo8Vr7In9/q+Ms7y9GsheWrOWANXGIwNHIwnU2dsPscfFc0N+C4sxIvQkBgQ80qDliVbYgmx1+OChW7G9aR3eDfrjTG0IW+4XLEcmHZG3hEl/98Ym7BoA6jxpXDhuoP/Labtcn0j5Ejmntr36ew8Xe/MYIg0D36OQyaFJzeQnZU3IxP4VzNSAx7gPjQ2wAoa3iLvOpbghIcHQ6Vykx46B1ER7zSpr+oiGfR6/q36VwAdfaF7CufjsdXhkHTlYy4b9Cq/UliM8qsP5JfLwuPMqOewKLzJ1JuXwOrS+/ALdYToe+p6NtZ5z7Imkq5leigqOuxaP17cG+u+ZFy5z70KjMjY67y2+6TvPxYJC8aBkVOINbgb/siSiJzCj9HQmJw5Ckkm5QJZYG9kMUVsifrBSmB61olSeKjvO8QERkW1Y+MkNSAYwGK7BXMLZKfR9d2OSOoKLQkSby65yd8ksgp0VkMiExvdZ/D4RG9PLtDbrQwvdMguoQE/j6Dwbt7l5FnrSZab+HW7mOP6liBkN9Yw9NbFgJwd++xJJmPzvbpaLGrppynmt5rDw4YR6eQ8OM6n46C1e3iqgXfcbCxjmRLGP8ZfyaqE7ic4B/iGQD3DRzF+d16I0oStyz5mQ3lLSuIT1bEGi28PvIMBGDW/q38lHf0HpdHgxijhWcHy2Tk4+x1bK32707THrglayxpliiqXFbe3PPnUR2ra0gcl3eRU28v7PoVh9c/MtcSBkd1ZUJ8H0QkXt77Y1AKea1Kw83dZFuiH4tXkmdtuTShR1hXRkUPQkLio9w5ARXuKkHFZZ0vAWB55UryAnQ96mTuwvCosQDMKfxMcc7xxs6Huhn9XPIRbgWyNzr2WsyaSGrdxayt+tpvu0kTzag4WSSzs/Ybimwb/Makhp5NRvhVAGyqfIJq51a/MTp1JH1j30ejCqXetYW91f8K+BkIgorE8PvoHPkKAlrq7L+yr+LCVq2WjgckSaK84SNyq65t6ko0ioz4H9BrAkf66pwb2VMlk/VOodeRGOLv2SlKHtaV34fVk49RE8/w+NfRqP5/atsneVhS+iQ+yUWiaSC9Iy7wP5e7jJWVnwEwJvZaLFp/0dGqyp+pcBViVocyKf4yxTmvq17OQfsB9CoDZyUp15BKksTXB2fJ5DRiID1Cuwf8DNZWb2Vn/T50Ki0zUpW7Jx1+3Hdy5uGVfAyOzGJ4VHBm63+W72BDTQ46lYa7spStoVrC57mryLNVEakzc1uWcs1rsMhtrOKTJt/Ox/qdRpgucN3rkUKUJB7a8CsOn4dhsZ25JvP42hVZPS5uXT4Pt+hjQnJXrs7qOCHV8YTb5+OGxfPYWV1OpMHIl1POJ85kOd7TahH/EM8AEASB50dO5tSUdFw+L1f/MZd9tce/R3NHYGRCKrf2liOA/1o3n/wGZWHJscLEpEzO7twLUZJ4cH3HpNz1ag2P9pUJ7qzcjeyqO7qa0uu7jiXBGE6po54Pcpa2ef/bMqZhVOvYUXeQ30v8+5QrYVBkFqdE90ZE5J2c71v1kLwi9Wz0Kh17G3NZUbUx4LiMkG6MiBqGhMSXB78OSITPSLoQk9pMsaMgoNBofNyFhGmjqHWXs6zCP4KqV5uZEC9HF9dXz6HC6W9F1NkykqwwOWK7vOwZnL46vzE9I28jwTwOUXKztvRubB7/yLFZl06vmDcR0FBu+4V9NU8iSoF/W1GW8+ka+zVqVTh29xb2lp1OZeOXiAHU8ccaNtdW8qpupLjuSeT+65fSNeazgAbxAPXOzWyvuAUJDzGmyaRH3O03RpIktla9QKVjPWrByPD41zFo/DvObKr6mGrXPvSqUMbEPexXSypJEovK3sQruUg29aF3uH/P9Tp3JX+WfwvAlIQrFWs77V4bP5V8A8DUhHMJ0yqXIKyv2cDexmx0Kh0XdfInwc1w+dx8mi8Lnc5OmkCsoWV/ybXVu9hQsweNoObmrucEFe20e128kS0r9C9PHUOSqW0elkX2Gj7cvxSAe3pMJVR75ERRkiSe2vYbHklkbHwGExKyjvhYLWFmzibWVxZg0mh5YcjpxzXiJkkSD69dQF5jLYmmUF4eMe1vKSgSJYl7lv/KypKDmDRaPps0nS5h/qUsJxr+IZ4tQKNS8fb4MxkQm0iD28UVC+ZQYu3YDiXHC3f0GcmQ2BSsHje3rJjXYfWVweKR/hOJ0pvZ31DFO7s7RuQ1PLYLpyX3QkTiya2/Ih6F+bdRo+PBnnLK/MvcVeQ0lrdp/1hDGFd3kesZ3973O42t1G0244b0s9CptGyty2F5K92MovURnJcsW4Z9kT8Phy9w+viClOkYVHoO2HJZXb1WcYxFE8K0RDla9mvJHKxe/5pQvdrItMRrAFhROY9Kp38Eu1vICLqFnIKEyILS1xVN5YfF3EqYrjN2XzUryl7wI9mCoGJw7DOE6bJwibWsKb0dj2j1O06kcThZUU8Bcl/y7RU34hUD24mFGIaRGfcjek0aHl8phbUPs6NkKMV1L+D2+oumOhqS5KXW/ivZZeeQXX4mdY7fAYGk8EdJiXhWsRVmM8qsP7K57Aq8Yh2h+v70iH5RUXh0oH4m+Q1zAYEhcc8RrvdX1JfYN7O9VhZfjYp7QFFQtKfhT/Jtm1ALWibF36F4rl9KPsYjuUg196B/xFjFef9WOhert4F4QyJjYycrjnH5XMwulAnstISpROsDt2acV7yISlcN0boIzk2aFHAcgNvn4d2ceQCcmzyGZFNw6e5Pc/+k0tVAojGSy9LGBrVPMyRJ4rmdv+ASvQyJ6sJpiX3atP9f8VvRTtZW5qFXaXi4T3AesG1Fsa2eF7fLJQ739RlHsjm83c/RFnybs50f83ejFgTeGnUm4fr2j/Aeb0iSxJNr/+Sn3L1oBBXvn3o2fWMCt849kfAP8WwFRo2WTyedR9fwKEptjVy+YA61zhMj4tGe0KhUvDnqTCL0RnbVlPPcppZ7V3c0IvQmnhwoR0je27Oa3bUd85J/oNckzBod22uL+S4/uEhjIIyJy2JcXHe8ksgzO35us6n8hZ1PIdUcS63Hxgc5/u0klZBgjOKCFFkd/lHuz632cD8r6VTi9FFUu+uYWxT4HBG6CM5MktOD3xZ+h8On/JsfGX0qScZO2H02fin5VnFMj9ChZIQMwCd5+ankA8XI7Klxt6BXmSl37mNzzY9+2zUqA+PiH0OFhoO2lWTX+9s0aVRGhie8jkEdTYPnAOvLH1SMaCaEnEuvmDdRCQZqHCvYVHoRDk9g316DNo2s+N9IDn8cnToFn1hHecPb7CwZQV7VHdjdHS9A9IkNlDd8yK6S0eRV3YTNvQkBLZHm88iK/5240OsCEgpJEjlQ+xq7q+5vinROon/cJ6gVhEeltuVsr34FgN5Rd5Fg9leFO30NLC17GpDIDDuD1BB/wYbdW8eSctm+akT0ZUTq/VtE7m3YyJ6G9ahQc2bS9YrzL7YXsLxS/p2el3ylorcnwC+lv1HjriVaF81pCf6R1WZUOKv5vlg+3lVp56BXB243CvBD8XJKndVE6kK5tHNw6e58awWzDsrK4ruyzsCgbptR+eKy3ayq3I9WpeZfvU4/KqLY6HHy/A75em/IHEWyuf39NCVJ4uGNv2HzuhkUncJlXY9vSju7tpLHN8h1pvf2G8PA2La3Jz0Z8O72dXy6W66df2XMaYxOPrrGAscS/xDPIBCuN/Ll5PNJMIeQU1fNjD/mYve0rY7vZEC8KYRXT5Fr8j7L3sT8gpZ7V3c0pqRkMTU5C68k8uD6X/CI/pGwo0WsMYTbusvE7dVdi6l1tb3v+uG4v+dpGNU6ttQe5Kc2ttLUqjTckyVbzXxfuJYDjcGR7Qs7nUqULowyZw0/Frfs1alTaZmRJvfI/rF4MaWOwHWLk+ImEKePo95Tz4/Fyn6cKkHF+SlXAbC6agkFtly/MYIgcEbitWgEHbnWHWyv85+jRRvFmFjZqmdl5WfUuf1LH6INGQyKls3L11S+SZ37oN8YkyaO4fGvoxYMlNtXsaP6VcV5x5onMzB+Jjp1LDZPDhtLz6fOGbj8QK0yExt6DT0Tl9Ml+gMs+iGAl1r7D+wtO5195edTZ1+ApBCtPRq4vAcprH2CHcXDKK57CrevCLUqgvjQ2+mVtJrUqNcw6QKbYftEOzsr7+Rgk4dp57Ab6RXzBmoFI/p61z42lD8ESKSGnEvXMP96S0mSWFn+InZvJWHaFIbF3KZ43iXl7+HwNRCjT2NQlL/Ixy26+KX4IwBOiTmDOIN/+09JkviuSBYU9QsfTFaockelCmclv5fKAruLO12IThWYTH6RPw+36KFnaDdGRA0IOA6gwWNj1kHZaeLqtGmYNIEdAg7H69m/HBIUjYwJXGeqBJvXxQu7ZOeJGV1GkWrxjyS3BW/tWUqVy0pncyTXdGu7qj4YzM3bzoqyXPRqDc8PmXZcU+z2poyd0+dlTGKXv21bzG/37eCFjbLl3WNDx3N2+slliP8P8QwSiZZQvph8PmE6A1sqS7hlyU94xbZFtE4GjEtKP3Sz3rf6Nwob647rfJ4YOJlwnZFddeV8uFc55Xu0uLTLEDJD46j3OHhlV3CWRoGQYAznpozxALy2ZwG1QXpzNmNQVFfGxcrp///s+y2ofQxqHVelyW0qZx5cSIOn5XMOiexD37AsvJKXrw76RxeboVVpubSzLOT4o3wRJQ7lOth0SyaDI09BQgooNIrUxzM2Via8v5V+htPnP8fe4VNINvXBK7mYX/KyYsq9d8SFJJoG4pNcTcIWfwuwCENPBsXK6fQD9bM4UK8ciQ3R92RwwneE6HriEWvZUnYVpdYfAnwaMgRBTbhpChlx35EV/wsRpnMADVbXOnKrrmNX6RgqGj/Bp5DmDxaSJGF1ruNA5fXsKhlDZeMniJIVg7YbnSKfp3fiOhLD70Wr9leQHw6Xt5zNZZdRaV+AgJbu0S+QHnGXYsrb6a1iddkdeCU7McYh9It5UDHStr/hN/KtyxBQMzbhMbQq/xTmvoYV7GlYgoCKSQl3KUYpl1Z8R62ngjBtNOPjlOsxN9euJce6F62g45wkf5umZswqmI1H8tIztAcDIwI7AuxpOMCq6s2oELimy/RWI4lf5f+BzeekizmRCfHBRfHWVu1jXfU+NIKaO7JOD2qfw/HOvsVUuhpJMUVyddejs/7ZU1fG1wdk+6lH+56GrgNaY5Y7Gnl6qxxdvKvXmOPej/3xDQvJqa8mzmjh1VOOb51pR2FxwQEeXCkvtG7sPYRrep18oql/iGcbkBERzSeTzsOg1vBnYS6Prl7YqqDjZMS9/UbTPzqRRo+LW1f8iNvX/pHGYBFtsPBofznF9dauFeyvb39lsUal4vF+cn3m3INb2FJ9dO1SL04dRkaITGRf3xNcyvxw3JwxFY2gZm31PtZV+fchV8LE+MGkmROweh3MPLiwxbGCIDAj7VxUCKyu3sLeBv8oZTP6hvemX3hffJKPmQWzA/7ez0q6GL3KQL79AOtrlKOuo2LOJlqfhNVbx8Iyf3N2QVAxJeFutCojRY6diu00BUHFmLiH0atCqXbtY1PVx4rnSrJMoGfkrQBsr3qRcrtyZyi9Jo4B8V8TY5qMhIc9VQ9yoPaVoLw7Tbo+pEW/Qa/E1cSF3oxaFYbbW0BR7RPsKB5KUe1TNDiW0eBYHvRftXUO2eVnsK/ifOod8wGRUMMYusZ8Sff4RURbLmnRm7MZDa6dbCidTqN7F1pVBP3jPyPBcrbiWJ/oZE3Z3Ti8ZVi0nRka9xIqhVrRencRqyveAGBQ9LXEGPxFKjZvDQvLZHuloVEXkmD0rw+tcBaxslJe8JyeeA06hetx+Zz8UCy7HEyKP5NIvXLkb0fdTjbXbUUtqLms88UtlBtIfJYn/55OjRtBmrnl9GuxvZKfSuQGE9enn4k6gAn/4fBJIm/tk6OV0zsNJ8UUuM5UCXvrS5iVJy+uH+p1eptT9IdDlCSe3PYrIhJTknpwSlzbrJyCgSRJPLrxdxo9LvpEJjAj4+h8Ro8W3+fuZM6BHagEgTdGnUmUIbj2sicTdlSVccuSn/BJEtO79eLBwS03SDhR8Q/xbCMGxSXx1jjZfmhm9jbe37H+eE+p3aFVqXlr1FmE6vRsqy7lpa3Ht6XmWZ17MS6hK27Rx4Prf8HXAZHm/lEpnNupHwBPbvv1qKLZWpWah3vLKfMfizazuSa/Tfsnm6IOdTR6a99v+IIgQWpBxfXpTecsXkmJo2UHhs7mJMbHyuf4NL9lRfwlnS5EI2jYUb+TLXXKAqYwbQRTE2Rbmp+KZ2P3+kc0NSotZyZeB8C66gUU2/0V7OG6BMbF3gDAysrPqXTm+Y0xa2MYFfcAANtrZ1JiV67NzQi/mk4hZyDhY135/TS4/c8HoFYZ6RXzOp3DbgTgYP0H7Ky8HZ8YXNmFThNPUviD9EpcR0rEM+g1XRClRioaPySn8nJyKi8L+u9gzT3Y3dsRBD1R5ovpHr+QrrFfEmocE3StX4VtAZvLLsXtq8Cs7caghDmEG5SjIpIksanycWpdO9CpwhgR/wY6hbaZouRlaemTeCUHCcb+9FZoiylJEgtKX8PhayBW35XhMf5RSkmS+Kn4A3ySl8yQQXQPVSYr88vmUe+pJVoXy6lxyobyXtHL1wWzAJgQO55Eo38rz2asqtrMPms+BpWOizopH+9wfJz3Kz5JZHBkFgMjg2tX+kvxRnKt5YRojFzVZXxQ+zTDJ4k8vfNnRCQmJ/RiRExwBvWB8MPBrWytKcKk1vJAb2VB1tHi54LdLC7Zj1al4vnBp6NRHT86kVNfzSPr5AYed/Q5hWFx/qUbJzuKGuuZ8cdcHF4Po5NSeX7k5JNWqf8P8TwCTOrcjceGyg+W5zYs49e8o+t+cyIi2RLGS8PlB/SHu9ezuCjnuM1FEASeGjQVi1bP1poSPtvv7+XYHrin1wTCtAb21pczM/foFhR9Izpxbor8sn9mx894WhH9/BVXdRlPiMbIAWsZvxYHrj08HIMisxgUkYVX8vFJbssdigAu7nw6epWOfY15rK4OXI8aZ4hjSrys/p1ZMBu3qNzhakzMZOL0iTR6G/i91D9aCZAe0oc+4aOQEPmx+H3ldHr4FLpYhuKTPPxW8iJehY5FqSGjyQw7A5BYWvY0Tp+/24QgCPSPeYQoQ3+8opXVpXfgCtC1SBBUpEfcJSu90VJpX8imsktwtkG5rlaZiAm5nB4Jf5Ie8ymhhvEYtT3a9GfS9SEh7B56Ja6lc9QLGHXB92iXJIn8unfZWXk7ouQkyjiagQmzMWr9Ddmbsaf2fYqsfyCgYWj8y1h0yv6fm6s/pdK1B53Kwpj4h1EJar8xO+p+J9e6HrWg5bSk+1ArRE231S0nz7YTraDj9KRrFF+c5c4SllTIZSbnJl+ONkDN5sLyxZQ6ywjVhHJ2knIbTgCP6OHLppKSs5MmEqlr2dR8V30eKyq3oULgui6Bj3s4bF7XIUHg1emnEqZtW7Tt+4KN7KwrwqzRc0+PqW3a96+oddl5eZec9bi1+1jijf4LiaNFldPGk5tlondLj5Fkhh+duf3RwOn1cOuKedi9HkbEd+bWXh1Ty3o80eB2MeOPuVQ6bGRFxPDO+LPQqvzvwZMF/xDPI8TVvQYxo8dAAO5a9iubyjvG6Px4YnKnDGY0me7es+oXSmzHz0oqwRTKv/rKdkOv7FhKfmPgtodHiki9mbt6yud4c88SKpyBbXaCwR1ZE4nQmThgrWBmXtvqU8O0Jq5Ol+fyQc5C7N7AnXYOx3XpZyAgsKxyK7vr81scG6kL45wkufXgl/nz8AQglABnJE4jXBtOpauShWXKqXyNSsP0lCsAWF75ByUO5ZKF0xKuQq8yUezIYUON/7EEQWBywp0Y1aFUunJZU/WV4nGGxdxGmDYFu7eSleUvKvdqF3QMi38FsyYZu7eYtWX34pMCCwPjLWcxIP4LtKpIrO49bCydToOrbap1QVARZjyVrrGf0T1hfpv+suJ/ISHsDrTqttXK+UQXu6vuJ7fudQCSQ66gd+y7aFSBjaQLG39nb1Ob0f4xjxBjVI6Klti3sLXmSwBGxt2v2J2ozl3KknL5WCNjriJan+o3xuGz8VvpZwCMjZtOpM7/OJIkMbfoS3ySjx6h/egVplyz2eBp4McSWfB2fsq5mDSBid6vpcuocFUTqQvjrKRTA45rPv8HB+RuQZPih5BmCc6e5uv8ZdS4rSSbojgvpW090GvdNt7c20QUMyYQazg6ovj67sXUuR10DYnhsvSOEdf8e/MCat0OuofHcmP340v0nty4mL21lUQbTLx+yhmoj2PktSPgEX3ctHge++qqiDWZ+XTSeYTo9Md7WkeFv9c3dIzx6NBxTOzUFZfPx4w/5rK+7OhqA09EPDhgLL0j46lzO7l+6VzqXUfXXvJocEGXfoyIS8Xl83LX2nnY29ghKBhMTx1An4gkbF43j27+Oag0dyCE6UzckSVHCj/IWUqhrW1k+byUYSSboqh2N/J+TnB94LtYEpkcL6cv/7N/bhD2ShOI0IZR7qrmu6LA5zCoDVyQIouDfir5hTKHciQwK7Q3/cIHIyIy6+CHimQ2RBvBxHi5O9KC0q8UvT3Nmkgmxt8ByMbyOY2r/cZoVUbGJjyGgJp86zJWVbyiGEHVqyMYnvAGWpWFaucW1pXdi7cFE/gwwwAGJczBrM3A7atkU+kl5Ne9i6ggZDoR0ODazsbS6ZTbfkJATWbUv8mIehhVAOshgGLrQjZVPA5ARvhVpIaepTiuzLGdxSUPAxLdQqfSJWSc3xiXz8bPxU/jkZwkm/owKNK/E5AkSfxc/AE2bz0x+iRGRiufb33NSvY0bEcjaDgv+XLFiKgkSXx1cBYOn4NUU2dGRp8S8DrLnVXMKZSFGJd0OgODuuUX9oKy9exukFPyV6YFF3nMs5YzM1+ua76l21S0quBFPJIk8cru32n0OskIjeeC1KOrk1xZnsOcJlu4x/tN65Co2Le5W/mtcA9qQeD5wacf18jb53s3MXP/VgTgtVPOIPYE79jTVji8Hm5e/OMhg/hPJ04n0dL+EexjjX+I51FArVLx5tjTGRibRL3byaW/f8vPuW3r1X2iQ6/W8J/RZxGlN7GzppzLF8+m/ih7mx8pBEHgucHTCNcZ2V5Tyu2rf2h3ZwG1oOKJfqejV2lYXr6fV3cdXf/6M5P70zeiEzavi3s3z8LpC568aFUa7sps8tIsWM3isuAib1emTSVEY2K/tYj3cwKr1gEMaj0z0mSiMKdwPltqA7dMHR41jMyQDJyii7dy3sHlU47CnpN8GUa1iXz7Ab4vUo5WDo2aTGdTFi7Rzpf5z2JXMJ/PCB1Jn/DTkBD5ufhZCmxb/cbEGLKa6j0F9tb/yOKSR/Aq9FUP1XVhaNzLqAQ9ZfYVrCi5Aac38ELAqE1mYMIsoo2nIuEht+51NpScQ2HD57i8bWsO0BEQJQ/V9uXsrryfjaUXYvPsQ6uKoG/cRySFXBRwP0mSyKmbybryBxDxkGSeRM9IZUuk/Mbl/F50Fy6xkVhDL06J9e9y5BFd/FD0BOXOHIzqMKYm3KOoml9aMZdtdStQoeLs5JvQqPzT8IX2fL4pkMVik+LPItYQrzivRRV/sq5mPSpUXJ56KaoAwh+36OHFvR9h9znIsKQyNrbl6F+utYT/7Je7a13aeSLR+tb7jNu8Lh7a9hUu0cOQqG6MiQ2unWYz5hZu5JdiOa3/UM/TgxIxBcKBhkruWv8dEnBh6kAGRQdum3qkWFKyn0c2ymUQt/UcRa/I42dY/kX2pkN+nXf2HcmoxJPHxzIYVDlsXPTbbP4oyEGvVvP2+DPpFd2yk8XJgn+I51HCpNUxc+oFTO7cDbfo49YlP/PutnV/K7V755AIvp54EZF6I9ury7hi0TfHjXwmm8P5cNQF6NUalpTm8Oim39v9s+4eHs8zA+Tark/2r+aHg1uP+FgqQcWL/S8gQmciu6HskEdfsBgRk8XlqbJy8dld35Fvq2h1n2h9GA90l4UdP5Ws5M/ylo3xR8UMYlKcbIf02r7PqHQpEzKVoOLm9BsI04ZS5Cjm0/wvFD/7SF00V6begoDAyqpFrKlaqnAsNZek3k+4NppqdymzDr6ET8HsfUL8rXQLOQWf5OGHoicodfh7y2aETeXUhKdQCzoO2lbyW9FdOH31fuNiTUMZlfgeOlUYta4dLCo8j4LGXwP+fjQqC71j36ZH9EtoVeHYPPvZX/Msq4rGsLn0cooaZuL2VSvu2xGQJB81jjXsrXqUVYUj2VZxHWW2HwGROPMZDE36jUhj4LSnzVPCqtJb2V79EiDRJfQChsQ9q0gUd9f9wKLSR/BJbjqZR3Ja8mto/qI+90lefil+liL7dnQqE9NTniFM508Wd9avYVG57GJwRtJ1pJr9PQdt3kY+zn0dj+ShR2hfJsefrXgNOY05zCqQ22demDKdrpbAau2Pc+eQayskRGPm3qxrWiR1Nq+TJ3d9hkv0MCgiiws6tS4OkiSJ53bN5aCtkhh9KI/3urBNYo/d9cWHnge3ZU2gf+SRE8Val52b187C6nUxMKoT/+oT2ET/SLGtupjbVv+AT5I4N7UPt/YY2e7nCBZfZm/msfUy6byx5zBu7x046n0y4kBdNWf//BVbK0sJ1xv4euqFjE9pf2eC44V/iGc7wKDR8u74s7imp1zz+fzGZTyyeuHfyuczKyKWrydeTITeyLbqUq7+cw6242SiPyA6mTeGn41KEPg2dytv7WrZNP1IMC2lNzdmjgLg8S0/s6mq4IiPFWcM47n+FyAg8EPhJuYVtq1D0vVdJzEgogt2n5t/bf0qqHrPoVE9uKSTbEP1WvY3HLS1HKW7psv5dDGn0Oi18fLejwOKocJ14dycfiMqVKypXsviCuUOVz3D+nFagpya/7bwU3Kt/rZQFk04l6c+jE5lINe2k5+LP/QjgSpBzbTEB+lk6odHdDC38GGqXP7G8WkhY5ia/Bo6lYUK505+LriZRo+/72iUoR9jkj4lVNcNt1jHxopHWFV6KzaPco22IAjEW85kWNJ8ukU+TJi+PyBR51rPvpp/s6pwFFvKrqakcQ4ehR7yRwtJEqlzbiS7+klWFY5ma/lVlFi/xSPWoVVFkRRyCQPjZ9Mz5mV0auUezZLkY3/dVywqPI8Kx2pUaOkddRd9ox9E+ItISJIkNlS9z+qKVwGJrLAzmZD4lB/plCSRBSWvcsC6Fo2g45yUJ4kz+iuxi+0H+K5AtmAaHjWNIVH+Cmuf5OXj3DepdlcSrYvlitSbFaOYDZ4G/pPzHj7Jx+CIgUyOD9zucknFOv4oX4WAwF0ZVxGjD9y/WpIkXsmeTbGjkhh9OA92DxxFPRzfFqxicfl21IKKZ/peSqQ++DRvvdvOvZtm4xF9jI3L4qouo4Le969wiz7uXD+HAlstSaZw3hx6Qbt7duY31nDtim9x+DyMiu/Cs4NPO26K6m9ztvHo+qZuTD2G8kD/4B0fTgasLyvk3F++prCxnk4h4fxwxmUMjvt7dV/6h3i2E9QqFY8NO5Unhp2KAHy1dyvXLfr+uJGzjkD3iFi+nnARoTo9myqLuW7pXJzHqaf7xKRM/j1AXtW/sWsF3xxoW5egYHBb93FMSuyORxK5bd03FNvqjvhYw6LTubnJWP65nT+zt17ZjF0JGpWap/pcTLQ+hDxbBc/vbtn+qBlXpE2hX3g3nKKbJ3d9iqMFwqpTabk/61osGhP7rPl8lq+sSgfICs3kghS5G83MgtnkWJVtiibFn0W/8MF4JS8f5b5Ords/Ohhv7MyFne5GQGBDzULWVPtHhDUqHWcnP068IROnr5HvCh6izu1fYxpv7MMZKe9g1sRS7yngp4IbqXbu9xsXoktjfPLX9Ii8FZWgo8KxmkWF09lf91XAzkNadQQpoVcwMGE2I5KX0DXifkJ0vZDwUetcxd7qR1hZOJJt5TdQap2H9ygN5Otd29hf8xyri8ayuexSihu/xi1WoVGFk2i5gH5xn3FKynIyox4nzBDYNL3etY+lxVeyo/oVfJKTaMMATk35lm7hV/i9rEXJy/LyZ9lWI5dHDIy6hlNi7/WrFZUkicXl77C7YTEq1JyR9Agppt5+527w1PBl/nN4JDfdQvozNfEqxTnOLfqK/dbd6FUGrku/G7PGn8D5JB/v5LxPraeWBEMC13SZEZBsHLQV894B2WbpgpSp9I9ouavLD8XLWVG5DY2g5pEeVxKma51AbqvN562mBg93ZE6jd3jw0UpREnlk21xKHHUkmyJ4su+5R0ycJEnimW2/sb4qH5NGxzvDLyZSbz6iYwVCldPKjGWzqHHZ6RURz9sjzjtudZ0/5+/hgTW/A3B11iAeHDD2b0U6f87dw6W/f0udy0n/mATmnXEZXcICL5pOVgjSCZwTbmhoICwsjPr6ekJDT56C2vn5+7h96S+4fF56RcXxyaTziPsbFT1vqSzhskWzsXndjE9K570x56JTH58H0as7lvL27lWoBYH3Rp7P+MSj87/7K+xeN5ct/5Q99WV0C41l1uirMWuPTFEoSiK3b/ialZX7SDFF8vXIGwnV+nd+CYRttfncsvEDfJLIPVlnHfL6bAm17kZu3PgyNe4GTo0dyAPdL23xQb2xZifP7HkXgLszZjAqJrD/49s577KhdhMR2gie7PUYoVr/e9Tlc/Lavn9T7Cgg2ZjKXZmPoVP5f34rK3/k99LPEVBxeeq/yAz1b2fo8DUw++C9VLsOEq5N4KLOr2DR+qu/bZ5K5hffS607F63KxITEZ0gyKV9HozufLZVPUeWUo9AR+p70j3mUcH1wNkZ2z0EqbL9TYfsNq+e/ZQAqdESaRhOi6wEE/2L0ivVU2hfi9P43AqsWLMSYJhJnnkqEcYSiuftf4RNd7K39kH11nyPhRauy0CvqTlJDzlFMrbtFO3+WPEqRfT0CakbG3UdmmLLf5cqKz1lbPRMQmJb4AN3D/AVHbtHFhwceocRxgFh9Cjd0fRaD2p8QraxczDeFnyAgcF2Xu+gdPlDxnN8Wfsevpb9jUOl5vOcjAT07bV4H9217kVJnBf3Cu/NIj5tbTLHvrs/n7q1v4ZNEbul6Dmcnt94tqMZl5cq1b1LlamBifF/+3fuiNpGfj3KW8Z/sRehUGr4YcR1ZYYH9R1vDlwfW8ez2+QjA28MuZlxCxhEfSwk2j5tLlnzJztoyUszhfDfhSqINx+ddtqhwPzcu+wGvJHJxt348O/Tk9bH8KyRJ4v0d63lug+yZPblzN94YezpGzZE3ETjWaAtf+4d4dhC2VJRwzcLvqXbaSTKH8tnk6WREtK2TxYmMteUFXLn4W1w+L9M6Z/HmyDOPi42FJEk8sP4X5uZvx6DWMHPcZfSNSmrXc5Ta67lg6YdUuWyMi8/grWEXHrEIoN5t56KV71LqqGNsXBavDbykTQ/PWQdX8Gb2r2gENe8OvoFe4a0bJe+oO8C9W99BROT2btM5I6nleqivDv7E3KIFGFQ6Xux7PykmZQGBw+fg37ueodRZSo/QLO7NvBu1gr9jjauSl7Ifw+ptoH/4UGak3eZ3zZIk8UPRO2yqXYxeZeS69KdJMPqLBayeamYdvId6TynR+jQu7PwiRgXDc5evkUUlD1Pq2IIKDaPjH6JrqHJqVpJE8hvnsbP6NTyiFQEN3cKvoHvEdaiD6BLUDJv7AOW236iw/4bdE7gbVDBQCyaiTeOJNU8l0jAKtQJZD4RKx0a2VD6F1SOXhySax9M3+gGMGmWvRbu3hj+K76fKlY1GMHBqwpOkWJQXNRur57K04gMAJsTfRr8I/7aQouRjdsEr7Kpfi0kdyk1dnydS71/7mdO4h7f2P4eIj9MTL2ByvLLSfVPtZt7c/zYAt3S9kSGRgxXHSZLEi9kfsbZ6K9G6CF7p9yCh2sAkqc5t5eZNr1DpqmNMTD8e7uEfBf4rvKKPOzd9zKbaXFLNsXw89BZMmuC/m3VVB7hp3eeISDzR52zOTlEm2sFgZXkON6yeiYjEvT0ncE1G+9Y5ekWR61d+y7LSA0TqTXx76pWkhRyf6NvK0nyu/nMObtHH2Wk9eWXEtL+NbZJXFHl8zSK+2rsVgKt7DuSRIeNOuuv7h3ieIDjYUMuVC74jr6GWUJ2e9089mxGJ7a80PF5YWpzLdUu/wyOKTE/vzYvDTzsuvXE9oo/rV3zL8rJcog1mfpgwg0Rz64rUtmBbTRFXrPgMt+jj2m6ncE+vCUd8rF11xVy15kM8oo87siYxIz34+i5Jknh4+0yWlO8gVh/GZ8NvIyKI1OCcgiV8kPsTWkHNa/1vJzM0MGH1SSL/3vUWO+r3kWyM58W+92FUKxOwEkcJT+x6GpfoYlrCaYcsl/6KHOte3tr3rEwyEs5ncsLZfmO8oofP8p4kz7YLiyac69OfIUrvT3rr3KXMOng3Nm8NCcbunN/pOXQKPcN9opulZc+QZ/0TgCHRN9E7InBbRYe3km1VL1Bik50MLNpO9I95NKC/ZSBIkoTVk02lbUGbxUeCoCbCMIQo41jUCtfUEty+RnZWv05+o1wmYVBH0zf6IZIsgYUy9e5C5hffQ6OnFIM6jEmJLxJrVE5N76hbwILSVwEYFTODodH+6nlJkphX/B4baxaiFjRc3eUJRTFRtauSl7MfxeptZEDEMK5KvVXxeylzlvPErqdw+BxMjpvIJZ0DK/Z/LF7MZ/nfoxHUPNP7LjJCAqucfZLIw9s/YFNtNinGWP4z8C5MmtYXGe/un88XeUsxqnV8MvRWUi3BG6eXOxu4aMU71LptnJ08gCf6nhP0vn9FXmMVFy77iEaPi3M69eOZAWe2e/TviU3z+TJnEwa1hq/HXUa/dl7QB4uNFUVcvugbHD4Pk1MyeHv02ce1S1J7wuZxc+uSn/izMBcBeGzoeK4+CXuvwz/E84RCrdPBtYu+Z2N5MVqVildHT+PM9O7He1rthvkF2dyyfB4+SeKKzAH8e/DE45L+sHpcXLj4C/bWV5AVFss3p16B5QhT4oHwS+EO7tsov9TfGHI+k5Jarh1rCd8d3MDTO39ChcAHw2YwKCp4KxCb18nVa/9Dgb2KIVHdeHXAjFYjsJIk8e9dn7Kqagdx+gjeGXQPodrAtWB17kbu2fYcNe56RkYP5O6MwDV166o38M6B9wC4o9utDIhQrjlcVfUns5vscq7rchd9wv0fsA6fjY8OPEqZM58IbSzXd32WUK1/lKXSmc83B+/BKVrpbB7AOcn/RqPQ4UaSRNZV/oeddXMA6Bl+PsNiblVMNzej2Pon26qew+mT246mhpxDr6g7FVtJnigoti5ma9XzuJrmnBZ6Hj0j70CnDgm4T4VjN3+U3I/TV0+INpEpSa8QplMWMWQ3rOCX4meREBkUOZ0xsdcq/h4WlH7F8srvEVBxUed76BXmHzl1+Zy8mv0EJc5CUkxp3JnxqGL5hcvn4sndz1DkKCbD0o0Hsu5FE8Ajc3dDDo/ueAMRkeu6nM9pCWMDXjfAl/nz+SJ/AXqVlrcG3BWUUfyKit3cv/ULAJ7qczET4vu2uk8zPKKPa9d+wrbaAjJD4/l8xPVH3Ivd5nFx4bKPOdBYyYDIFD4deUW7i4k+37eBJ7f8IafwTzmPyclZ7Xr8YLGjuoxLFs6i0eNidGIaH449D307X+vxQpXDxow/5rK9qgy9WsObY09nSmr7lkocS7SFr/09lg0nMCIMRr6eciGnpWbiEUVuW/ozH+3smJaPxwNTOmXy8ohpCMAX2Zt5Ycuy42IlZdHq+XDUBUQbzOytr+DONfPavaf76Sm9mdFVfpE+tPlHDjRUHvGxzus0iNOT+iEi8cCWb6lsQ5cks8bAs30vw6DSsr56Px8fWNTqPoIgcG/mxSQYoih31fLS3pmILZjjh+tCuDdTtqBZWbWJ38uWBRw7NGowk+JkBf0HuR9T5lRW0J8SPZ7RMXK6+/P8dyhx+DsFGNVmrkp7lEhdPLWeCj7Le1LR4zPGkMp5nZ5BKxg4aNvML8XPKrbWFAQVw2JvZ0j0LQDsqpvDn6VP4BUDC62SLOOZmPI9aaGygCq/8QcWFp5HYeP8E85E3uopZE3Z3awrvxeXrwqLNpXRiR/RP+aRFklngXU1vxXdgdNXT7Q+izNS3g1IOg80ruXX4ueREOkdPiUg6VxROY/llfLC7KykGxRJpyiJfJH/LiXOQkI0YVzX5S5F0ilJEp/mf0GRo5gwbRi3dL0xIOmsczfw8t5PEBEZGT2QqfFjAl43wMaavXyZL6ui78g4PyjSWWSv5smd3wJwQacRbSKdAK/vXcC22gIsGgMvD7j4iEmnJEk8vPknDjRWEmOw8HoHKNiXlubw9FbZqui+PuOPG+nMrq3k8kWzafS4GBKbwvtjzv3bkM6DDbWc98vXbK8qI0JvZNbUC09q0tlW/EM8jwEMGg3/GXcGV/WQBRNPrVvCM+uXIJ64weY24ZwuvXhmqKwwf2/XWt7euea4zCPRHMYHI88/5PH5/LajM39Xwt09JzAkOhW7181t677B6gmuleVfIQgCD/c+g24hcVS7rDy45Vu8orKiWgnpIfE82FM2fv80909WV+5tdR+L1shjPa9CK2hYW72bbwuVrZCa0T00nSs6y+nAT/O+J7sxL+DYC1Omk2HphsPn4K39gc3lz02+lIyQnrhFF+8feBWrAqkM0UYwo8vjhGgiKXcW8GX+s7gVTOETjFmcnfIEakFLjnUN3xU8hEOhZztAn8iLGBf/OCo05FmXML/4Xly+wGRfqw6hf8zDjE78GIs2FZevig0VD/Fb/kQ2VjxOqW15i603OxKN7jz21n7Mn4WX8EfBmZTaliCgITPiWk5Nnk20seW6wb31P7Ow5F94JSfJpqFMS3kDk0a5dm9b7W/MK/o3Il4yQkYxMf52RdK5qWYx80vlaODk+MsYHDVR8Xi/l37P9vqNaAQN13W5iwidcmvQxRVLWFO9FhUqbul6I+G6cMVxPsnHK/s+pdZTT7Ixnpu7tlwzXems4/k9XyEhMS1hOBPjletFD4fT5+Ff277C6nXSK6wTt2ac1uo+h+OP0p18nSc/E5/udy4p5iOvk/w0Zw0LSnajFVS8MeQCYtpZ6JNdV8Edq39AlCTOT+vL9Vlta//ZXshrqOGyxbOpczvpG5XAJ+Onn1RCm5awo6qMc3/+mvyGOpItYcw9/RIGxh2fMobjhX+I5zGCWqXiiWGn8uAgeTX+wY4N3LXsV9y+4MnGiYxLMvrxyEC5luzlrcv5eM/xier2jUripSFyt59P9q1nZk7bPDNbg0al4tUh04k3hpJnreahTfOOOMJrVOt4ecBFmDV6NtXk81Z265HLwzE5oT/nNvWFfmLHN5Q6Wm/J2TUkmVu7yXWYn+b+yra6nBbHn5E4juFR/fFKPl7e+zENHmWbII1Kwy1db2wyly/iswDm8mpBw9VptxGtj6PGXcnHuW8oGsdH6uKY0eVRjGoLBfZsvs5/Ea9C+83O5v6cl/I0OpWJIsdOZuXfQ71HOeKaHjqBKckvo1WZKHNs5bv8y8ht/LPF7y/aOIBTk2eTFXE9elUEbrGegsafWFN2B7/mjWd9+YMUWxe22ILzaCFJEnWubHbXvMPCgvNYWHguu2v+Q517D6Ai1jiM8ckz6Rl5S4siJKunnIXF/5L72uOjW+hUJiU9j1bl3+dckiRWVnzOwrI3kBDpGTaRaUkPolIQj+2qX8sPRbITwqiYsxkd698yE2Bz7Vrml/0AwEWdriHNouxAkWM9wMyC2YC8oMkMCRwJmlXwCzvr92FQ6bg/67qAtcggC4Oe3v059R4bXS1J3Nw1uBrLV/b+yP7GUsK1Zp7pe2mbWmLmWyt5Ypt8zTPSRzE27sjLrNZW5vHKTvkZ8VCfKfSPSjniYymhymnluhXfYvW6GRrTiScHTj0uZVPFtnouXTibSoeNrIgYPj/1gnYvmzpeWF6Ux4W/zaLKaadHZCzfn3Ep6eHKi6+/M/4hnscQgiBwU9+hvDZ6GhpBxbwDu5nxx3fUOjvupXUscW2PIdzdVxbKPLVxMV9mty/pCxbTOvXgrl4ywX9i83yWlSr7TB4povRmXh9yPlqVmkWle3l/35Eb2He2RPNkH/kF+HnuShaV7mrT/ndknk6P0BQavQ4e3jYTVxAtOacmDGVi3CBEJJ7b/RW17sCRP0EQuLXrpSQaYqly1/Lqvk/xBfC6PNxcfnX1WhZV/Kk4zqwJ4foud2NQGcix7mFO4eeK5C/O0JkrUh9GK+jJsW5lTuEbiuSzk7kfF3d+BYsmmhp3ATPz76TUoRwBTjQN5PSUtwnTpuDw1fBn6eMsKL6fGldgFbpapadH5E2clrqQUYkfkh52EQZ1LF7JRpF1AevK7+fX/PGsLbuHvIbvsXlKAh4rWHhFB+X21eyoepU/Cs7iz6KL2Fv7IY2eXAQ0xBlH0D/mUaalLmRk4ruE6QPbiHlFJ9trZjE3/3IO2lYgoGZg1DWMjntIsZ+7R3Tye+nLTZZJMDz6UqYk3INaYez+xq18U/AqEiIDI05lcvzlinMosOXyVf77AIyPPY2hUcq2RXXuev6z/92gTOLXV29nbpGcMr+566WkmJRbbDbj49xf2N2Qj1lt4LGeV6ELIt39c/EGfineiAqBJ/tcTKwheNGizevink2zsfvcDIpM45aMU4Pe968ottdx9/rvEJE4u1NfLkprXwGK3evmxpXfUWyvJ9USydunnHdcLPJKbA1cunA2JfYGuoRG8uWpFxGub5vI7kSEJEl8s287M/6Yi83j4ZTEznw77eK/lc1iW/CPuOg4YVlRHjcunofd6yHWZOaVUacxOvnk7zUrSRLPb1nK+7vWATAjaxAPDxx/zFWIkiRx77qfmHdwJzqVmleGncVpKe0r6vo2bxOPb/0FgPt6TeTqboHbFbaGV3fP54u8VWhVal4ZcDGj44LzkQQoc9Rx5do3afDYGR6dyfP9LkfXSlTG4XNx66bXKLCXk2pO4KW+NxPegjr+oK2E+7e/iFv0MDyqP3dnzEATwET699IFzC78FgGBq9OuZHSMsmp/R91mPsx9FQmJEVHjuLDT1YodY/Y3buXL/GfxSV7SzL24pPN9mDT+9YuNnkrmFj5ClSsfARXDoy9lWPTFilE6r+hiW83XbKv9qqluU6BLyDj6R84gQp8a8HNohiSJ1Lp2UWxbTIl1MTZv0f/bbtakEGsaikXbibb5eNqocm6m2rEFkf+SbJWgJ840giTzqcSbRrdYv3n4Ne6t/4ltNV/h8MnR8FhDL0bG3UekvoviPqWObH4reZFadxECKibG306fiKmKY9dX/8HPxR8i4qNH6FAu6nyvop3WAWs27+W8hFN00D20Dzem36f4Pde563h+78uUOktJMCTweM+HMaqVScfm2l08v+cDPJKX0xLGcF2XC1r8LGYfXMTHeXJzgsd6zmBUTJ8WxwMsLd/JI9tn4pNEru86kRldgieOVo+TWzZ8ybbaAmL0IcwaeRPRhta/MyUUWGuYseoLSuz1dA+LZ+aYq4+4RlQJNS471y7/hm01JYTpDMydcBVpIcc+Crexoogbl/1AldNGsjmMOZMvJcF88r/361wOHl71B7/kyT6/Z6V35+VRpx037+uOwj+q9pMEu6sruHXJTxyol18KV/ccyAODxmDQnNwF1JIk8cb2Vby+fSUAp8R35j+jzybiGK9cXT4vd6/9kflFexGAJwZM5rJu7RcpkCSJN/cs4b1sOeJ5e/dx3JTVugG1Eryijwe3zGFR2S40gppXBl7EmLjgi/q31ORy1+ZPcYkeRsZ059kgUoLF9kru2fo21e56upgTeanfzS0q3TfW7OSFvR/ilbwMjezLPZlXK55DkiS+PDiTxU0RzytTL2d87FjFY66pWsqsgo+QkOgfPpQrUm9WFJFkN2zmm4JXcIkOInXxXJH6L2IM/mIYl8/GwrI32duwFIAEQxanJd1PhE65hqrOfZCNVR+Sb20WTwmkh0ygf9RVhOta90htvt569z5KbUupcKyjxrkdiaMvoTFq4ok1DiXedApxppFogrRX8olusht+YWv1l9ibVO4WTTz9o64iI3SqoqJflHysrZrFmqqvkRCxaKKYmngfnc3+DgWi5OP30s9YXSUTub7hozg3+VY0Kn8ytKt+Kx/nvoFHcpNuyeSG9Hsxqv1T+9WuGl7KfoVSZxmRuggezLqfOIOyVdGmmp08H8TvsBnNVmIAM9JO45LOyvWnh2NFxW7+te1rvJKPKQn9ebTX+UG10QRo9Di5Zf0XbK8rJFRr5L2hV9Ij7Mhq+PIaq7hq5RdUOBtJtUTx2cgriDO237uw2FbPVctmkdtYTbjOyEejLqR/9LGvN5y9fxuPrl+ARxTJiojho7HTSba0ryXe8cCqkoPcvexXyuxWNIKKOwecwi19hx0X28GOxj/E8ySCw+vhmfVL+XKP3PIxIzyaN8edTvfI4P3hTlTML8jm7lW/YPd66GQJ5+Nx0+kWfmxN9H2iyBObFzDzgJz2v7XHSO7sNbpda5fey17OG7tloc4NmaO4o/u4Izq+R/Txr61zWFgqk89XB7Yt8rmxOod7tnyGW/QyJrYnT/e5JGBUshmF9gru3fofatyNpFuSeLHvTS2Sz01N5NMjeRkS2Yd7M68JSD6/LpjFwnJZ4DU9+VxOT1Du77yldh2f57+NT/LRPbQP16TdgV6hVq/ceZAv856j1lOBQWXi4s730jWkn+I899QvYVHZW7hEGxpBz7i4G+gTHri/dLUrh81Vn3DQJi8iBFSkh0ykf9RVAdXegeARrVQ5NlHhWI+7jf3bBUFNhL4HscZhWLSd2/Q78kke9tX/ytaaL7F5KwAwa2LpH3kl3cKmog7Q8ajWXcxvxS9S6pTLEzJDxzAh/lZFY36nz843Ba+yr1G+nybEXcLY2PMCCI7W8EX+u00R0X5c0+V2RQV7qaOMF7NfocZdQ5Qukgez7ifWEKM41w01O3hx70d4JS/DovpxT8bVLf7Gvy9cxrsH5gFwReoULk/17xX/V6ypzOaBrV/gkXxMjO/L472Dbxhh9Ti5+TDS+f7Qq+h+hJ2J9jVUcM3KL6hy2egaEsMnI69oVzFRdl0FM5bPotxhJcEUymdjLqZr6LF9PntFkac3Luaz7E0AnNbkkmLS+lujnUxw+by8tHEFHzY52KSFRvDG2NPpG9O6g8LJin+I50mIPwsPcN/y36ly2tGp1Nw/aDTX9Bp00q+M9tZWcN3SuRRa67Fodbw16izGJaUf0zlIksRbu1bwxi6ZVFzUpT9PDpzSrp0hPt2/mhd3yhYkV3Udxv29Jh0R+fSKPh7e+h0LSneiVal5beAljIwN3mZjXdU+7t/6BW7Ry/i43vy790Wtks8CWzn3bn2bWk8j3SzJvND3JkK0/lGpZmyp3c1ze97HI3kZFNGL+7OuRasQ7ZIkiblFP/BzqRwZmxI/iYtSLlD8XPY0bOej3Ndxiy7SzN24If0ezArpdJu3nq/zX+CgfS8qVExLvIZh0cqp4AZPBfNLXqHAvhWANPMQpiTehTmAghugypnN5upPKbCtAkBATbfQyfSLvJJQ3ZG3NuxIiJKXffW/sbXmC6xeWVhl0sTQL/JyMkOnoVbwNwX5+9le9xtLyt/HK7nQq8xMiL+V7mHKhvM17nK+zHuWClchWkHH9JTb6RWuXF6yonIRcwo/Q0JiQMRwLu+sbIeUbzvIy9mv0ui1Em+I4/7Me4jSK6d511Vv4+Xsj/FKPkZE9eeuFso9AOYVLeftHFnYc1nnSVyZpvw7+X/nOOz+GRfXmyeDuH+aYfO6uHn9F2yrLSBUa+SDoVcdcTvM3XWlXLPqS+rcDrqHxfPxKZcToQ98T7YVGysLuW7FtzR4nHQLjebTMReTYDq279h6l5NbV8xjRWk+AHf3HcVtvUec9G0w99VWcfvSn9lTI9vtXZLZl0eHjjvpyXRr+Id4nqSocth4YOV8FhXIYpgRCZ14dcw0EsxHVht0oqDGaefGZT+wvqIQAfjXwHFc233IMX/AzMzZxGOb5iMBk5IyeX342e3qC/d17nqe3vY7ABenDeKRvkfWyenwtLtOpeH1QZcyIqZr0PuvrtzLg1u/xCP5mBTfj8d6X9BqxCbfVsp9W9+hzmMlM6QTL/S9EbMmcGp3a90entvzPm7Rw8CIntyfdR06BfIJML90AbMKZQ/EkdGncHXalYq1gHnW/bx74EUcPjvRuliuT7+HBKN/tNErephX/C5bapcCMDRqKtMSr1Y8piSJbKqZx4rKT/BJHozqMCYl3Em3kJbrcSude9hc/QmFtrWATEC7hk6kk3kkCaZ+GNTHNw0oSl6qXfspsW9ib/1PNHpKATCpo+gbeRmZYWegaUHhbvPWsKD0NXKt6wFIMfVlauK9hGqVMy35tj18nf8Cdl8DIZpILkt9kGST/2/SJ3n5vuhrllfKop+R0adyfspVimnqPQ17eX3fWzhFJ51Nnbk38y5CtcrPurXVW3k5+2N8kuzVeWeG8m+oGT8Vr+St/XMBuLjTBGakBY52N+PwjMHo2B480+fSoEmn3evilvVfsqX2ICEaAx8Mm3HEkc5tNUVcv/prGjxO+kQk8cGISwnTtV+Z0qLifdy+5gdcPi8DopL5cNQFx1zAk9tQw7VLviO3oQajWstrI09nSqfgszsnIkRJ4vPdm3luw1JcPh+RBiMvjpzCxM6BhX9/J/xDPE9iSJLEzOxtPLn2T5w+L2E6A8+NnMy0tJP7pnT7fDy2/g9m52wD4Pz03jw9dPIxNwSeX7iXu9bOwy36GBLTifdHnk+oLvh+3K3hu/zNPLblZyTg3M79eLL/GUfU190j+nhg8zf8Wb4HvUrDG4MvY1h08JHiFRW7eWjbV/gkkakJA3i41/RW55FnLeHere/Q4LXRPbQzz/W5EXMLbQS31+3lmT3v4RY99A/vwYPdrw9IPldUruSTvM8RERkQ3o+but6oOLbUUcT7B16m2l2JXmXgytRb6B0+wG+cJEmsqJzHH2WyJ2MXcy/OS7mNcJ1yirbSmc9vJS9Q2aRe7xU2ibFx12NoRaRT4djF5upPKLKvP+x/BaL0XUkwDSDR2J94Yz906sDlCe0BSRKpcedSat9MiX0zpY6teETboe1GdSR9Iy8lK+ysFgmnJEnsb1zJwrK3cPjqUQtaRsVczcDIswPUfoqsrf6N+aVf4JO8JBrTuSz1QcK0/lFJm7eRT/LeYl+j7MwwLWE6k+PPViR8m2u38E7Oe3gkL1khmdyZcVtAIdGaqi28su8TfJLI6OhB3J5xRYuk89eS1by+T+5UdUHKeK7tcnqrpHNLTS53b/4Up+hhRHQWz/e7LGjbJIfXza0bvmRTTT4WjYH3h15Fz/Ajq5PcVFXADWu+xuZ1MyAyhfdHXNquVkLf5m7l4Y2/IUoS4xO78ubwc4+5P+aKkjxuWTGPBreLRFMoH447j56Rccd0Du2NcruV+5b/zrJi2et4bHIXXho1hdj/IdX6P8Tzb4ADddXcuexXtleVAXB+t148MexULLqT189MkiQ+27uJpzYtRpQkBsUk896Yc4g2duxL+69YW3GQG1bOwepxkRUWy2djLibG2H4PiJ8KtvPQpnmISJye3JvnBh5Zb2GP6OW+zd+wtHwvBpWWNwdfxpBoZTWyEpaU7+DR7bPwSSJnJA3iwR7ntiqQOGAt5r6t79DotdMjNJXn+tzQYg/rHXX7eGbPu7hEN33Ds3go6wb0auWUUrBkw+pt5JPcN9hv3YOAwLTE85kUp9yLenf9OuYUvoFbdKJXGZmacBWDIicojvWKblZXfcn66jmAhE5lol/EGQyMPAezJqLFz6XcsZMDjQsptW+h1v3/jfQF1EQbMkk09ifBNJB4Y280qqNbzEiSRL2ngBL7Zkrsmyi1b8Ul1v+/MTqVhQRjP5LMQ8gIndriOSVJZH/jatZVz6bcuR+AGH0XTkt8gBhDquI+1a5Svi96m3zbbgB6hQ3nvJRAdZpFfHDgFarcFehUeq5IvZm+Ci1RAVZWruLjvM9aXYQArKraxKvZ8tgxMYO5rdsVLS6g5peu45Vs2QP0vOSx3JDeeg/zbbX53LX5Exw+N0OjMnih3+Xog1SNO3xubt/wFRuq87Bo9Lw79Cp6h7etJrgZaypyuWXtbBw+D0OjU3l7+MWYNe2Xnn1/z2pe3C7Xok9P68Mzg6YdU7cRSZL4InszT25chE+SGBiTxHtjziXmGD//2xsL8vfzwMr51Loc6NUa/jVkLFd273/Slwy0Ff8Qz78JPKKP1zav4p1ta5GATiHhvDZmGoNO8i4Hy0pyuXX5jzR6XCSZQ/lo3HS6RxxbMdXu2jJmLJ9NldNGZ0sEX4y9hGRzeLsdf37xLu7b8D1eSWRSYndeGnweuiDTdofD7fNyz+ZZrKjYh0Gl5T9DLm9TX/dFZdt4fPtsRCTOSR7Kfd2VI1CHI6exiPu2vYPV66BXWBrP9r4Boybwgmdn/X6e2f0OTtFN77AMHu5+U0DyeXh6NdXUmXsCpFd9kpe5hV+yoko2zB4QMYxLO1+vSHqqXCXMLXyLArtsV9LN0o9zkm8mTKcslCi072Bx2X+ocuUDoBF09A6fwqCo6YRpW4+82L3VlNq3UOrYQol9Mw2e/2+lpEJDrLEnCcYBba4L9YgOyh07KLVvxu6r/n/bNIKReGNfEk39STQNJFLfVdEq6nD4JC9765ewrvpbatwFTcfRMzDyHIZHX6rY316URNZV/86C0q/wSC50KgNTEq5kSKRy3fKOus18nv82LtFJlC6G69PvJtGo7AgQbNkFwIrKjby+7zNEJMbFDuWWrpe1SDr/KFvPy3tnIyFxTtJobura+m99V10Bt2/6GLvPxeDIrrzY/8qgrYqcPg93bPyadVUHMKl1vDv0SvpGBOeE8FcsL9vPbeu+wS36GBXXlTeHXtBulkmSJPHi9iV8sFfunnRD1nDu63NkAsgjhUf08fj6hczcvxWA87r04tlhU07qFpg2j5sn1/7J7H3bAegRGcsbY08nI+LYCrROFPxDPP9mWF9WyF3LfqXI2oBKELit33Bu7zfimHtjtidy6qu5dsl35DfWYtJoeX3kGUxKOba9avMba7hy2UyKbPXEGS18PuYSuoUpp2qPBH+WZnPn+jl4RB+nJmTy6pDzj5h83rVpJqsq92NU63h7yOUMiEwNev8FpVv4945vkZCYnjKcu7NajwJlNxTwwLZ3sfmc9AlL5+k+12FUByafuxtyeGrXOzhFF71Cu/Fwj5swBBifZ8vnlezXghKUrKxczJzCzxHxkWJM5br0uxXbLIqSj9VVv7CwbCZeyYNeZWJa4gwGRIxXvFZJEjlgXcfaqlmUOWXCqkJN97DxDIm6gCh98ATC6ilvIqGbKLFvPqQoP1qoBR1xhl4kmAaSaOpPjKG7ouG7Ejyii511f7ChZg4NTZ2c9Coz/SPOZEDk2Zg04Yr71bjK+L7obfJscrq8i7kX56TcQqTOn5BLksTC8p/4pWQOEhLdLN25ussdWBREYUpCswtTAlsULa1Yz1v7v0BEYnzsMG7uemmLpHNR2UZe3DsTCYkzE0dya7dzW/2N76kv4vZNH2H1OhkQ0YVXBlyFIcCC6a9w+TzctWkmqytzMKp1vDPkCvpHdg5q379iSek+7lj/LR7Rx/j4TF4bMr3d+q/7RJFHNv3Ot7lbAXiw76lcd4zbYNa6HNy07AfWlhcgAA8NGMd1PY59jX97YmtlKXcs/Zn8hjoE4PreQ7hn4MiTmkgfLf4hnn9DNLhdPLZ6IT8ckNNe/WMSeH3s6aSGtpwiPJFR53Jwy/J5rCo7iADc228MN/cadkwfSGX2Bq5aNov9DVWE64x8Ovoi+kS1n3p5RXkOt66djVv0MS4+g9eHnH9ELxWXz8OdG2eypioHk1rH22180f1avIlndn2HhMRFnUdye8a0Vj/nvQ0HeWDbe9h9TvqFd+Wp3te1+GLe25DLk7vfxuFz0iO0K4/0uClgC8NSRxkvZb9CtbuGSF0E92XeTaJR+XPPadzDx3lvYPU2EqIJ47oudwVst1jpLGJu0VsU2uV0ckbIAM5OvkmxJhFkQlRo38baqlmH1O8gkBEykqFRFxJnbJswQJIkGj0lcnrcsRVnG+2UVIKaaH0mCaaBxBp6tFivqQSXz8a2ul/ZWP09dl8tACZ1OAMjz6VfxOnoA9SiipLI+uoFLCj7ErfoRCvomZJwBUOiJiuSQ7fo4uuDH7K5Vo6ijYqewHkplyt2NxIlkS/yv2JJpeyV2pK1FsCSinW8tf9LJCQmxI3gpvSLWywR+bN8My/s+UoubUkcwe3dpgexsCrmto0f0eh10Dc8lVcHzMDUQlT//127z8vdm2axsnIfBrWWd4Zc0aaF4OFYUprNHeu+xSOJTE7swUuDz0V7BItTJbh8Xu5d9xO/Fe5BJQg8M+g0LujSr12OHSz211VxzZLvKLDWYdHqeGPkmZyaHLxQ8kSDTxR5e9taXt+yCp8kkWAO4dXRpzEi8cgWHX8n/EM8/8b46cAeHl79Bw1uFyaNlieGn8oF3XqftKtHj+jjqY2L+aKpvebZaT15fvhUDMdw5VjrsnNNU+cOs0bH+yPPZ3hcarsdf1X5AW5ZOxuX6GVsfAZvHCH5PDy1Z9boeXfIlfSJCL5f809FG3hut6z0vSx1DDd3m9Lq72Z3fT4Pbn8Ph8/FgIgMnux1TcA0OkB2Yx5P7voPdp+T7qHpPNr9ZowBakRl0/BXKXWWYtFYuCfjTrpYlMsIql2VfJD7KiWOAjSChgs7Xc2wqDGKY0XJx8rKn1hcPhuv5MGgMjEt8Rr6R4xt8XpLHXtZVzWbHOuaQ/+Xah7I0OiLSTH1DrjfiQC7t57NtfPYUvMTLtEKQIgmliFR59MrfDLaFghsrbuC74veJte6A4A0c0/OTb6FSL1yC8padzUf5r5GoT0PFWrOT7mCkTETFMd6RS/v537E+poNCAhcmXo542KVvzeAxeVreDvnayQkJsWN5Ib0C1skncsqtvDs7i8RkZiaMIw7M1o3es9pLOPWjR9Q77HTK6wTrw+8BnMbSOe9m2ezvCIbg0rLW0MuZ3AbSl8Ox+KSvdy1fg4eSWRKUk9eHHROu5FOu9fNzavmsqIsF61KxWvDzmZqO3duaw1Lig9w24ofsXrcpFjC+HjcdDLC2y+jdKxR0FjHXct+ZWN5MQBnpGXxzCmTCNO3nzj1ZMY/xPNvjmJrA3cv+5W1ZYUATEnN4PlTJhNhOHl72n6ZvZknNizEJ0n0jUrgw7HnHVNFoNXj4saV37GmIh+dSs2bI85hYlL7OQmsrsjl5jWzcIleRsd1482hFxxRWuZoxQzfF67lpT3zALgqbRzXd23db3RnfS4PbXsfp+hmUEQW/+51dYt9rvc15vPvXf/B7nOQFdKFR3vcjCmANVOjp5FX9r1Bni0Pg0rP7d1upWdYD8WxLp+TL/LfZXv9RgDGxU7lrKSLA9YIVjgLmVv4H4occvQzM2QQZyffSKg2sI8nyAr49dXfsLdhKRIiAEnGngyOmk4n8wB0Rykcai9IkkSNu5Dtdb+zrfZXvJILgEhdMkOiLqJ72DjFCOTh+2+o+YPfSz8/FOWcnHAZQ6OmBiRvedb9fJj7Go3eesxqC9d0uZNuIcqExulz8tb+d9jZsAu1oObG9OsYEjk44HwWla/mnRw5XT4lfhTXdbmgRRK5onIbT+/6AhGRyfFDuDuzZZIqz7+cWzZ8QK3HRvfQZN4ceC0WbXDf5+FiP71Kw1uDL2+T2O9wLCzZw93rv8MriZyW3IsXBp7TbqVT9W4H1y7/ls3VRRjVWt4bOZ2R8Uc2zyOBJEl8tGc9z25aggQMjUvh3dHnEGloPx/SYwlJkvghZzePrlmI1ePGotXx1PCJnNO1x0kb8OkI/EM8/wfgE0U+2LGBVzavwCOKxBjN3D9oNNO79TppTedXl+Zz0/J51LudxBotPD9sCuOPYVrG5fNyx5ofWFi8D7UgcG+fcVyb2X7tzdZU5HLz2lk4fV6GRKfy6pDpROnbruj8//Ytep7vf0GbTObnFKzi1b0/A3BBp1O4LeO0Vv0Kt9cd4OHtHzQJiNJ5pMcVROoD35M5jQd5Ytdb2HwOUs1J3JtxDUkmZeGOw+fgzf1vs7thDxpBwyWdLmJ8rHJ0UpRE5pf+wO9l3wPQ1ZLFJZ2uI8agHJ3zST5WVv7I4vLZ+CQvBpWJsbHTGRZ9GtoAxurNqHOXsKF6DjvrF+KT5L7pAipi9GnEGzNJMGYSb8gkSt+pVZFPe8DuraPUkU2ZM/vQv05f46HtsfquDIu+iK4hI1qdT4Etm/mlX3DQvgeAVHMPzk2+lagAUU6f5OPP8t/4rfQ7vJKXREMK16XfTbReWRRY5iznnZz3OWg/iE6l4/Zut9A7rFfAY39T8BtziuYDcFrCGK5NOz/gS12SJH4sXsm7OfMQEZkYN4h7si5u1S5sW20+D237ilq3lcyQJN4cdA2hLTRKOBwNHgcPbZnDqsr96FQa3hh0KcPb4K17+Nxn523k2e3z8Uoi05J78Xw7ks599ZXcvvp79jdUEao18PHoCxkQfWQq+yNBtdPOo+sW8FuBXDd9cde+/HvIpJO2L3lufQ1Pr1vC4kLZW3tQXBKvjZlGp5Dw4zuxExD/EM//IeyoKuOOpb8c6vfeOzqOJ4ZNOGmV7/kNtVy3dC776+Ue0xd27cMjA08l5BjZSHlFkYc3/sp3ebJScUxCOi8NOYMoQ/tYfqyrzOPmNbOw+zzEGUJ4fegF9Its+4vB7nVx24av2FSTj4DALZmnck168K1AZx1cwZvZstDj/9h77/DI7vpu+56Rpmp60aj3tk3aXt3ttQ3uGLBNL4GENyQkQAJJgEACSahP8vCQQKiBYGxT3I37um7vTb3X6b2X8/5xRiNpV9JKu9r1aj33dc11zpwZSaPR6JzP7/Nt6411fL3tfRjl8zvMx329fPnEj4mk45jkOr686sOs1s/tpPSGhvin0z8gkAyhlMr5ZP39XF+8ZdbnJjNJftT7Yw54xdF5q3Qr+Wjth7EqZq8QPeLdx/8O/ohEJo5MIuedZfdyrfWWWScoAdhjQ/x++PuMRsULiEFmYbvlDtYZr0M9SzHMdEJJNwc9v6cz8BrBlPOsx2USJTZVE6XK5pwg1RZaL8gNSWZi2GM9TEQ7GY92MB7rzBUJTadQIqdCvYYNpnuoKdo478/MCBn6QyfZ6/4jpwP7sq9dzs2lH2Cr+Z1zuoUD4V5+O/wLhiJiD9RW/UY+VPOpWceaZoQMz9tf5Pcjj5LIJNAUavjrpr+kQTN7D1pn3MP3On9OR1D83neW3cBHauYuDIqm4/x75yO87BBTc27OOp3ziU5BEHh0ZB//p+NJUkKaRm0p39/wJ+jlC/uf7gna+euDDzIc8aCQFvJ/Nr6P7dbFNwWPppJ87ejTPD4s9jK+o7KVf1l/15KITkEQeLjvKP985Hli6RTFSg0/v/Z+WgyXrj/mC8Pd/N1ecfpegUTClzbcyEdaNixLVzCQiPN/j+zm56cOkRIyFEqkfGbddv6/tq3Luqj3YpIXnm8zEuk0vzh9iP97ZDfBZAKAe+pX8nebrsW2DKcexVJJvnP0NX7afgABqCjS850dt7HVdn6tShaLIAj8pvcIXz/6AvHsSfx7W+9asrzPnoCTv9z3MP0hNzKJlL9rvZX7a+cXDbORSKf41uln+N2QOA/4etsK/rntXQsOHb5iP8k/n3yESDqBTann39Z+kBbd/CJ4OOLgayd/xmDEToFEyifr7uSeirkFryfu4/90/w8n/V0AXGvdxJ/W3T9r3qdYJf0ijwz/nqSQRCFV8J7Ke7mx+PpZRZEr7uA3Qz/JNSw3yS28s/TdbDLtmPX5GSHNUe9rvGh/EH9SbFVUIClklW4rG003UatZPW+oVhAEgiknE9Guaa5jF8lM9KznqguMlKqa0RSagYX/XVNCHEesF1d8IBfmn45JXpVzWktVzViVtXPOYJ/En3Rz2LOLQ96X8CZE8SpBwnrjDdxou2/OtlOO2DhPjj3CUZ/YPF9VoObeig+y2XT1rH/v0egYP+37Ob1hUUSu1LXwibqPY5LPntqw132UH/T8mlAqgrpAyafq38dV1g1z/h4jEQdfO/lzBiITSJHyyfo7eFfFtfP+38TTSb7b8ThPjorpGTfaWvmHVe9GtcD+mM+NneCrxx8jmk5QpjLwvQ0PnNcYzKGQh7/c9widATtSJHx21Y18rHFpxkMGk3H+4cAzPD0sFp5eXVLHd7bcgWUJ57rPRyAR458OvsTvesUc4WaDle9uv43V5tnd88uZjCDw++6TfPPgazij4oCG6yvq+PKW66k3zF6gmEckLzzfpjijYb598DUe6TqBABTJZPzl2u18dNWGZdnmYa99iM+/+TQjYT8S4OMrNvH5dddessKjTp+Dv9zzKD0BFxLg06uu4tMrr16SFW8oGecfDj/O82NiqPPOyla+uvb285oi8ujQIf7l1JMkM2mqi8x8d8MDNGgX5nQMhBx84egvGYq4kEsL+dsV93Bb+dwXf4BoKs73uh7mFccRAK61ruWzzffN2Wg+LWT4w8jzPDT0NBkylCqtfK75Y9RrZl9ITMTs/LT/53QFxdzMRk0DH6/9KKWqsy9kgiCwz/MaT4/9Fl9SrOIuU1ZyZ/n9rNS1zXphT2biHPbu4qDnRcaifbnjRrmNDcYbWW+6fs4q+DPJCGk8iZGcMzkR68QZ6ydDekFfPx+aQjOlqpacyLQpG+esSj+TtJCmK3CIA54X6QoezolYpVRNm/Eatphvxaac/f33J708O/4ou127yJBBgoTNpqu5o+y96OVnd9FIZVI8Pf5Hnhh7ipSQQlWg4v7K93CtdfYFSSKT5Of9f+DZidcAaNRU89nmj1GinLv/4evO43yn48Gs267lSys/zBrD/JO8HDE/f3f0fzkdGEaKhE813sr7axYWFUhm0vx7x3P8ul8sNNtiqeeb696LQb74PMVd41184dAfCCbjmBVFfHfTvWyxnl9B0pkc94zxmd2PMhT2USiR8tk11/GJlqVLDzoXuycG+ZvdTzMaDohthVZt4bNtVy/L681R5zj/uOdFjjrF8bO1OiNf2XoDN1QufGLc25m88Hybcyz7D3Rk2j/QP269kesrL12C+VIRSsb5+sGXc6M2G/RmvrfjdlrNpZfk50dSCf758PM80i/+/M3WKr639S5K1Rf+eRQEgV/07OG7p8RJHs06G/93y3up0sxf/DIbJ30jfP7QQ0zE/CgLZHyt9R5uKVtYJXYoGeNrJx/mDacogt9duY3PNN8+b96nIAg8Nvo6P+p9nLSQoUpt4yurPkp10dyCtz3Qy/c6f44r4aVQUsAHq+/mjrLZG1lnhAy7HK/yyPBviWXiyCSF3F1+F+8ovWXWYqJEJsFrjud43v4E0XQEEPM/7yy/n9qiucOio5FeDnpf5Jj3deIZ8eskSGnWrmej6SaadBvmHc84G8lMHGesl4lYF7F0aFFfK5FIscirKVE1o5UtvhG1Oz7OIc9LHPbuIpjy5o7XFK1ko+lGVum3z9qEHyCajvCS/Sl2OZ4lkRELlVbr1nFH+XvnbAjfHx7gp30/ZzgqNtFfa2jjwzUfmNPlHI5M8L3OnzEQESuD7ynfyQNVt885njKdSfPT/qf57bA4cWeNvo5/WPkhzAr9vO/DEU8f/3D8QbyJEDqZmn9ufYDN5oWFx12xIH975GEOewYB+Fj9Nfx5842LHn2bFjL8oP1V/qtTFNhrTRX8++b3YFMtzbnj5137+dbxl0lmMpSr9fzHtntYZ7k0KVbRVJJvHnmFX3SIqTFVGgPf3XEbm4oX3mXjcsEZDfOtrGEDU4bNx1ZtXLa5qW8FeeGZh4wg8IeeU/zbgVdzIYMbK+v5ytYblmXvz5dHevjC3j/ijIYpkEj49JrtfHrN9iVrP3Iunhg8xZcPPkMolcAgV/HNzbdzU/nSNLzf7xzgswd+hzseRitT8M0N93B96eIr6j3xMH935BH2uUUX7wO12/lMy80Leo8yQoaf973MT3rFSUFrDTV8o+39mBTzp2qc9Pfx9VO/xJ3woypQ8Lnm+7i2eN2czw8mw/yg59fs84hCfoNxFX/R+EH0s0wvAnDH3fx84Jec8J8EoFpdzZ/UfZQq9ewXuEgqzAv2J3jF8RypbEFQm2ETd5S9F5ty7hBpIhPnlH83Bz0v5UZEAmgLjaw3Xs8G042YFZdmsbNYkpkEp/17Oeh5kb7wydzxogId6003sMF4I1bl3IIkmUnyuvNFnp94jHBWKNcWNXJn+f00aFpm/ZpEJsFjo0/wx/HnyJBBU6jhA1UPsNW8ZY6G/QIvOfbwk77fEs8k0Mu0fKbxQ6wzzt7BAMATD/D107/khF/My313xXV8vO7cC6LfDe/hPzqfIi1kaNSW8m9tH6RMvbDF3BHPIH97+GGc8SCaQgX/1PYubiiZ+zXOhTce4W8O/oE3HeJr/0DdZv5mzc3nNUDiTNyxMH+7/0leGRe/960VLfzrptvQyS9Nt4WjrjE+++ZT9AXEuoL3N63j79dfT5Fs6cZ7XgqSmTT/c/ow/374zVyK2rsaVvHFTddiexvNWF8q8sIzT44zk6Tl0gL+ZPVGPr1227I7UXjjUb607zmeHuwAYI2phO/tuJ1Gw6UZUTYQ9PCZPY9y0jsBwAcbNvJ3a29ckrCSPRrgr/f/jiMesUXWnzZfzV+suO78XJbOl/hZr+iyrDdV861192FRLizX93XHab528mHCqThWhY5/XftBVunndzG8iSDfOP1Ljvl6AHhXxTV8ou7OOQWCIAg8N/E6P+v/PUkhhVGm56+bPsIaw+xCXhAE3nTv4cHB3xBORyiQFHBb6Tu4s+z2OYuJvAk3z4z/nn3u1xAQkCJlm+U63lHyrlnDxdNxxkY46HmJI95dhNOB3PFqdQtVRS2UqeooU9Vhkpecs33PxSCaCjEW62cs2stYtJ/u4BGiWcEoQUKDdi0bTTfRot1I4RzvD4iLjYOeN3l6/Hd4EmIxn01Rxh3l99Gqn7sopCvYzU/7f85ETMwX3WLaxAeq34dONvs5OpyK8sPe3/CGS3TH2vQtfKbpQxjlc7uWJ319/PPp/8GTCKAqUPD55vu5pnjtvO9LLJ3k2+2P8syYWHi0s6SNv19174KmEQmCwG8G9vK9drHavF5TzPc2PEC1ZvHnlpPeMT6z/xHGIn5UBTK+tu527qhsXfT3mY3d9gE+t/dxHLEQcmkBX1q3k/fVr78kBTyJdJrvn3iT/zy5h7QgYFNp+Oa2d3Jd+fKLpL0xOsA/7n2JHp+Y673abONr25ZvUe7lQF545jmLHp+br+19iddGBwCwqTX8/ebruKtuxbKrOnyi/zRf3v88/kQMubSAv113LR9bsemS5DUl0mm+c2IXP+0Uq4JXGIr5j233UK+7cPGbyKT59onn+d8+sZhjq7WW72y697xaLr08cZovH/tDVkBq+db6+xY86Wgw7OQLR3/JYNiJTFLA36y8mzvK5+6/CGJI9Of9z/Dw8MsArNbX8qWVH543JDoQHuW7nT9jJDqBBAnvrriF+6reOWdo25fw86vBX3MwW/lepirj47UfmbNiGmA8OsKTYw9zwi+KEZlEznXFt3KT7XbUhfO/r6lMko7gQQ56XqQneBSBmadKhVRFqao2J0TLVHVYFOWLDs3PRzjlZyzal7uNRvtyBULT0cssbDDdyAbjDRjk8zfpFgSB04FjPDH2MGPRoezXG3ln6b1sMV8z5+uPpWP8dvj3vOTYhYCAQabnwzUfZL1xboe7KzjA9zp/hj3upkAi5X1Vd3B3+U1zCnaxAv01/rvvCdJChmq1jX9c/TEq1bO3bZpkIurj7479io7AKFIkfLrpndxffdWCzm3RVIJ/OvE4fxwTO1ncWraGr6y5a8GTjKa/9ocHDvEvx58lmUlTVWTi+1veS5P+wivLk5k0/3HyNX7YvhsBaNBZ+L/b7qHZMP/7slR0ep18dvdTnPKIn707a1byT5t3YlAsr97RQ0EfX9+3i+cGxfxxk1LF3268hvc2rqEgX61+QeSFZ55ZEQSBF4d6+Kd9uxgK+gDYZKvgq1tvZLXl0rXdWArskSB/u+ePvDomhpW32Cr5zvbbqNQYLsnPf2W8h7/Z9ySeeARVgYyvrL+Z99TOXsyyWJ4ePsGXjzxJNNty6Xub38168+Ir+gdDLv760IP0hZwUSqR8bsU7uL9m9lDomYRTMf7p5CO85hDDzu+q2MpftcydizfJm84TfKvjQSLpGEaZli+t+jCt8xSBxNJxftr3O1507AagRVvHZ5s/ilUxd2j0gOcgvxz4NYFUAAkSbrbdxL0V96CYZ5Z8X6iTx0cfoi8sVterCzRst1zHJtOOOfMXp+NLOOkOHmUs1sd4tI/x6CApIXHW82QSOSWqmqwQrUdbuLi0lpSQwB4bygrN3lz1/ZkY5TbKsqK3Ut1MbdHKc/bujKYjHPMdYI/rldz7oCpQs9N2J9cW3zxn7ifACf9JftH/S1wJ8fVcY7mK+6veS9Ec4j0jZHh89CV+PSQKyGKFmc82f5Rm7dxFNZFUjO91PsyrzqMAXFe8js823YfqHALwsKeXfzj2IL5kGL1Mzddb38dG88J6bA6F3Xzu0G/oDtoplEj56xW38r6axY/tjaQSfPXo0zw5LIrXG0tb+NcNd6FdYIeJ+RgJ+/irPY9xxC3mxd5Xt5Yvr7v5vAoRF0s6k+En7Qf47tHXSGTSGORKvrH1Vm6rnj0F43IlmkryX8f38cPj+4mnUxRIJHxoxXr+ev2O/OShJSIvPPPMSyyV4scnD/CDY3uJppJIgPubW/mbDddgVi2f6RKCIPBg91G+cehlIqkkRYVyvrzxRu5raL0kLq4jGuTz+57kTXs/ALdVruQbG9+BdglyrXoCTj6z7xH6Qi5RNK6+iQ/Xn88FMc7Xjj/Gc+Ni7t87y1r58pq7FtROJiNk+J/+Xfy450UEBFoN1fxL2wcwnyPvcyTi4GunfsFAeBwpUj5S+w7eW3XDvGkDbzgP8V+9omAtKlDxkdp3cUPx1jmdsVAyxINDD/OmWxSsJrmRO8tu52rLVRTOIY4FQeCk/zBPjD3MRGw0d7xMVcUm0w42GrdjmKMo5kzSQhpXfGSaG9nPWLSPRCa2oK9fKBIkmBVlWSFbS5mqnjJlLarCheWgpYUU7YHjHPC8yQnfIZLZvNdCiYxrrTezs+ROiub5XoPhIR4bfZzDvqMAWORmPlr7YVbrV835NWNRBz/s/Q0nsi20tpvX8f81vI+iwrnPLT3BUf6l/ZcMRxwUSKT8af1d3F0+e9umSQRB4JGhN/l+1zOkhQxN2jL+be0HKVUtTOy/au/gH47+nlAqhkWh4Vvr7zuvmet9QRef2fcIPUEnBRIJn1t1Ex9p2LYk56Bnhtv5+wNPE0zG0coU/Mum23jnJRp9ORj08vndT3PAIRaO3VBez79tfcclnSh3oQiCwJN9HfzLgVcYD4vDFraVVvG1rTfSbFq+4zsvR/LCM8+CGAsF+NcDr/BEn5gzqZMr+My67XxoxfplVc135gnyurI6vrntHdjUF7+HaUYQ+O+OPXzvxCukBYHKIgP/vu1u1povPFconErwlSNP8syIKBpvLlvBN9bfhUa2+BDgrwf28H/an8sWXNj47oYHqCpaWMugN50dfPXEQ9kLtJavrbmf9ab5W4xE03H+o/O3vOQQw+Kr9bV8oeUDlKjmFnYTMRff6/wZ3SGxmniFrp4/q7+fKvXcRUHHfCf4xcAv8STEQgeL3Myd5Xeww7xtTgGaETIc9x3igOcNTgWOkBbE1kcSJDRqV7LJtIM2wyZUBYtbhGWEDJ7EBKORXsaifYzH+ommw4v6HlKkWJXludB9qbIWRcHiwpmCIDAQ6eGA502OePcSSk1NN7Ipythk2sFm89UY5XP//UciIzw6+kQurUGChBuLr+c9lfeinKVxPIhFSn8YeYHfjzxHUkghl8r4eO172Gmbu19lWsjwu+Fd/KL/j6SENGa5ni+t+tC8gwlAdOS/efpRXpgQi9RuLV3HF1a+C+U8o1yn/8wfdr3Mj3teBWCtsYpvr78f6wLzoKfzx5FTfOnIE0RSCaxKDd/b9G42WhaW0jIf0VSSfz7yPA/3HQVgnbmcf992NxVFhgv+3udCEAT+t+sI/3JoF9G0uKD/ysYbee8lWtAvFSdcE3x170u52eoVGh1/v/l63lnTtKx+j+VCXnjmWRT7J4b56t6XOOV2AFCvN/GVrTdwXcXySRpPZzL8rOMA3z4ihoT0ciX/tHknd9Zcmnm6R1yj/NXeRxkJ+7P99K7lEy3bLjjvVBAEHuw/wDePP0dSyFBdZOI/tryX5vPIGzvkHuBvjzyMOx5CU6jkG2vv5VrbwkJmw2EXXzz6K/rCYgPsj9XfyEfq5ncxxSKi/fxnz6NE03HUBQr+vPFd7LRtmkeIpHlqbBe/GXqaeCZBgUTK3eU38Z6Kd6CYo0gkkUnyiuNVnhp/Bn/SD4BVYeXusjvYZtk6b85lOBXiqG8/Bzxv0BvqzB2XSWSsMWxgk2kHK3St8848v1xwxiY44H2Tg543ccan8kC1hXo2mLaxyXQVlaqaef8fxqJjPDb6BPs9BxEQsj08N3F3+R2UqeZeAJz0d/HD3ocYjYo/d61hBZ+su49S1dyukj3m4ZvtD+aq1rebV/PXzfdhOMcErc7AKF86/iAjETFv9C+a3sl7q3Ys6P/cl4jwd0d+yx6XWAj3QM1WPrvi1kV3x0hk0nzn5PP8qlfMx95sqeE7m+7FugRN28/sH/xnK7bzmdXXXJIOHmPhAF/Y8wyvjw8AsNVWxbe3v/OSpTAtBa5omG8ffJ2Hu44jAMqCQv6/tq386ZpNKC9BesLblbzwzLNo0pkMj3Sf4NsHX8cdE/sZ3lBZxxc3XrusQhLdPhefffMpTnjEyvN3VDXztU07L0l4KJCI8Q8Hn+GZYbEf5g5bLd/cfPuS9Pw87hnlr/b/lvGoH4W0kC+1vYN7q9ctWlQ7YgH+5vDDHPOKRSUfq7+GTzVdf87cTRCLML7b8ThPj4ku2EZTA19e/R6KlfP3VByPuvhm+4OcCogpCVdb2/iLxnsxyud2mJxxDz/ue4QDHrG3nk1h5uN172GjcfWcv3M8HWeX4xWeGv8jwazLZ1PYuLPsNraYN89ZAT+JO+7koHc3B9xvYI+P5Y5rCrWsM26lVb+RclUlmkLdZeGYJDNJnPFxuoPtHPTuZiDck3tMLlXQZtjIJtNVNGlXnbPgaTgywtPjf2Sve1+uiGqTcQN3l99JhXruaVaeuI//HXqCXQ6x2M4g0/Kx2ndzlWXuqnhxQtUBftD9KJF0DKVUzv/XeA+3lsyff5wRMvx+eC/f73yapJCmRGngn1ofYI1hYQ7jce8wXzjyCONRH8oCGV9ZcxfvLG9b0NdOZyTs5fMH/sAxrxhh+WTTVfzFiusveLBEOpPh172H+NejL5HIpClWavjO1jvZYVuaZvPzkREEftd7nH8++DLBZBxFQSFfXHcdH27ZcMma0V8o0VSSX7Yf4fvTJvjdVb+CL268ljJNXj9cbPLCM895c2b7JYBbqhv5dNtWWq2XZx/DM0lm0vznyT18//huUkIGVYGMP121hU+s3HzRW0gJgsAjfUf5p+zMZFWBjD9fuYOPNW+54LZL3niELxx6lNftosDYaq3lK23vpFa7uIr6ZCbF99qf4zcDewGo01j50po7F5zf9szYIb59+jFimSTqAjkfr7+J91btmLe/YlrI8PDQS/xy4FnSQgZ1gYIHqnfyrvJrkM8THt3nPsaP+x7BnfAB0KSt5YGq22jTt8wrQF90vMwz488SSolthnSFOq4vvpbri6/FeI52SoIgMBwd4IDnDQ559hBM+Wc8XlSgoURVToly8lZBqbIcncxwUQRpIhPHHhtnIjbKRHRU3MZGccYnZlTaS5DQolvDJtNVtOo3zDpLfTppIc0R71FesL9ER3DK7V1vXMc95XfN2SsVwJcI8ofR53lu4nUSmSQSJNxSchUfqL5z3lzO0/4Bftj7GO0BMZ1ipa6GL6x4P2Wq+T/DnYFRvt3+GKf8Yruxq6wr+NLq96CXnTsdIpiM8f86X+SRwf0ICFSqTXxvwwM06hY30jGZSfOLnj38Z8erxNIpdDIl/7bhHq4vvfB+vkfdo3z10HOc8IpDP64rredbm+/ArFx8R4vFsntikH859DInsxXray1lfHf7bdTrl8eIyEAizi9PH+anpw7iiYnja1ebbXx1241sss0/AjjP0nFZCM9vfOMbPP300xw9ehS5XI7P51v098gLz7eOXp+b7xx6gz8OdOYubdeU1/Dptm1sKV0e0ylOuif4yoEXOOwUc3yKVRo+13Y1766/+K0zegIu/v7A0xxyia5IjcbEV9bfzLWlFzZ+LSMI/Kx7N/+v/RXimRQyaQGfaNrBJ5sWP6bu+fGT/OvJp/AmxDzEuyvW81crblnQWMCBkINvnPodJ/2ic1pXZONzK+5ivWn+9Iyu4DD/0fVbuoKigChRmvhk/Z1cZZk7fyyaivHbkWd5evwVEhmxOGalrp4Hqu5gtX7uaTTRdJSX7Lt40f4y3uw4zQJJARuN67nJdiONmoZzCsW0kKYreIqDnt30hTpxJ5xntVWaRFWgniZGxZtmjt6Wc5HKJHHGJ2aIzHP9zHJVNa2GDWwwbkMnM5zzZwSTQV51vs5Ljl253FgpUjYY13Fb2TupLaqZ82sDyRCPjb7IM+OvEs+IrlKLto6P1L5r3op1R8zLT/ue4mWH2NZKKZXz/uqdvKfyegrmWbAEkhF+1PM8jw6LTqy6QMGfNd7MuyvPPedcEASeHz/Jt08/gysuLkBuL2/jC6tuX3S1+UHXIF87+jQ9QScghta/sf5OKooubBiHOxbm28d38dvsZDSNTMHn1lzLBxs2XnRXvcfv4l8P7eKlUTHVQStT8Ok12/n4ik1LMhb4YuOJRfjpyUP8sv0wgYQ4aatSq+cv127n3Y2rl41Te6VwWQjPf/zHf8RgMDAyMsJPf/rTvPBcpnT73PzXsb081nuadPajsslWwZ+3beW6itrLIuQ4H4Ig8MehTv7t8CsMhXwANBus/P2G67m27OLmsAqCwOODJ/m3Yy/hjInibmd5E19at/OCiwSGw17++dgzOfezqsjEl9vewVW2hbWRmcSfiPAfHS/wh+GDABhkav56xS3cWXHuMH5GyPD02CH+s+tZfEnx97uldC1/0XTbvJXvGSHDS/ZD/LTvadwJ0U1co6/nUw130aide1HjTfj5/cjzPD/xBkkhBUCrvpkHqm6nRTf33zKVSXHYd4QX7C/l5r+DWIjUZmhjraGVFl0L8nOE4mHh7uNSU1SgoVRVMVPYqsrRFS7MZZ2ITnDUd4yjvuN0hbpzBVXaQg3XWa/l+uLrMM/TwiqcivD46Ms8Nb6LaFqs3G/QVPO+qttZa5i7F3A0HeeRoZf57fAu4lln9OaSTXy09p3z9njNCBn+OHaYH3T9EW/2s7WzpI2/aLoNq/Lc14LhsId/PfUku53i/0d1kZkvrb6TTZbF/c974mG+e+pF/jB4FACTXM3frrmZOysvrNAmlcnwYO8h/s+J1wgkxffz3ppW/rbteixLkCc6H85omH8//gYPdR8lLQgUSqS8v2ktf9l6FWbl5d/VxB4O8t8nD/DrjmNEU+JCtMFg5s9bt3Jn/YplIZqvRBweDzaz+fIItf/iF7/gr/7qrxYkPOPxOPF4PHc/EAhQWVnJzb/4Ee9Zu547G1ooLlo+rRyuJIaCPn50fD+PdJ0gkREvWqvMxfx52zZurW687JvvxtMpftV5mO+f2I0/IZ7ory6t4e/WX89K08XtYRpMxvn+ydf5Rfd+0oKAoqCQT63Yzidbtl1Q+F0QBJ4fa+dfjj+LIybmNN5U2sIXW2+hXG1Y1Pc66hni6ycfpycoFphtMNXwpTV3Uqs5d36vPxnhR93P8diIGMosKlTwifqd3Fu5bd7w+6QoeWR4Vy5cuxBR4op7+f3Ic7xo300qK6Da9C3cUnIVm0yt8/7MwfAQL9pfYq9nP4nMVB9OhVTBKv1K1hraaNO3Yphnqs5sTOZbjk8ToxOxUeLpxbVXkkgkmOXFuVB+qVIUm9pFO6cpukM9HPUd44j3GPb4zKbz1epqbrbdyGbz5nkF92jUzgsTb/KifTfhtBjGrC2q4IGq2+fNt519cVHHpxrunndxAdATHOfb7Y9x3CeG46uLrHy+5a4F9eZMZlL8ovcNftLzKvFMCrm0kI83XMNH665Gvoj/tbSQ4aH+g/zf07tywvA9Nev57KqbMMgvrGn6QecwXz38LO0+8X9tlbGEr66/hfWWixsWjqaS/LT9AP91ci/hlPjZv7mykS+su25ZhNUHA15+eHw/v+s+OeMa9Om2bdxa05R3ON8CUpkMrw8P8Fh3O8+1n6TzL7+4/ITnV7/6Vb72ta+ddbzyu/+MVKVEKpGwo7yKuxpXcGtdExr58hr5eCVgDwf58cmD/LrjKJHsarNeb+JTrVu4u2HlJZudfr744lH+34nd/E/nIZKZDBLgnrpV/HXb1Re9crPL7+Rrh59jr0O8oFYVGfjSup3ceIEz30PJON9v38Wv+0Rhqywo5JNNV/Oxxu2LErbJTJr/7d/Nj7p2EcskKZQU8NH6q/h4w7ULalNz2j/Md9ofpz0gphc0aEr4/Iq7aTPWzPt1s4Vhby/bzr2V12GZR4A6Ym5+O/wsLzv2kkHMRzbKdNxg28bNth0UK+e+mMbTcU4H2jnqO85R3zF8Sd+Mx2uLallraGWdoY0qddVl7+yD2Nv0mP8Ex3zHOOE/SSQrFEFMMWjRNrPW0EqboQ2bcu6JN8lMin3uYzxnf4OT2V6cAJXqUh6ovI0t5rY5+6umhQxvOI/x4OCL9IXFAq2FpFOIrz/Gj3tf4PfDe0gLGZRSGR+vv4n7qncsqPjtkHuAb5x8gr6QGA7fYq7j79fcSfUC24ZNcsQ9zD8fe4Z2v1iguEJfwpfb3sk684WlGDmiQb55bBePDYoFc3q5ks+vuY776tZd1IV7KpPhkZ7j/MfxN7BHxZSDVnMJ/7DhBrbYFj+Y4lLT6XHyn8f38URfO5llGHW70hAEgWOOCR7rPs2T3R24s3m1mWiM4c99efkJz7kczx/teZ3nxoc4NDFVaaosLOSmmnruaVzJNZU1yJZR38krAW8sys9PH+Lnpw7l8mvKi3R8snUz9zetuezbVgwGvXzryKu5ue9yaQHvb1rHp9dsv6jhJkEQeHq4nX89+iITUdGlvL60gS+t20mNdmHNy+eiK+Dg68ee4YBrUtga+bvWW7muZHHCdjTi5d9OPcXrDlF0VKpN/N3qO9huPbfjlBYyPDFygP/qfpZgSjwh7Sxp4xP1O6ksmr+A5MzCE5mkgJtLNnNf1Q2UzlN84oi5ed7+Bi/Z9+BLiu+pBAlrDSu4peQqNppWz1vVLQgCg5GhbCj6GP3hgRmPG2VG2gytrNGvolxVjlVhmbNH6KVCEASCqSATMTvdwW6O+o7THeqZEe7XFmpoM7Sy1tDGav0qVOfoBzoedfKC/U1ecuwhkJya/b7euIpbSq5ivXHVnO2zkpkUL9kP8dDQS4xGReG30AKyZCbF4yMH+HnfS3gS4s+93raGzzTfhk1pOOd74U2E+ff253l8RFy4mORFfH7lO3hH2eLC4a5YiO+cfJHHh8V8S51MyWdW3sB9tRvmbRt2LpKZNL/qPsh/nHyNUCqBBHhv3Vo+33o9JsXFPdc8O9TJt4++Rl9AzOUtL9Lxt+uu5Y6alZe9Q3jUOc4Pju7h+aGpbg3XVtTy6batbC5ZHnUGVxIDfi+PdbXzWPdpBvy+3HGzUsUdjS3cWFLJNY3NSy88v/jFL/LNb35z3ue0t7fT0jLVG3AxwvNMzszxHAr4eLy7nUe72unzeXLPMylV3FbfzN1NK1lvK82vgC4hoUSc/+04yk9OHsQZFXOxLEo1f7J6Ex9YsRatfHHNzi81x1zjfPPIK+yeEMWORibnkyu38PEVmy5qBXw4meAHp9/gZ137SGYyyKUFfKJlK59aseOCRuEJgsAzIyf51skXcuH360qa+PvWW6lcRCGEIAi8PHGab55+BkcsAIgzrD+/4h1YFtBo25cI85/df+TJUTF3tEAi5bayDXys/sZ5xYQgCBzwdPDg4Au59ktSpFxvW8f9VTdSUzR3Z4VkJsV+z3Gen3iD4/6pKm2z3MCNtm3stO3Aojj3e+BL+DjmP8FR71FOBk7PCMlPvh6rwkKJ0oZNWUKpqoQSpY0SpQ2DzDCnG3g+xNIx7DE747EJJmL23M0em5jhaE5SqapgbTZvtU5Td87Xksqk2e85xvMTb3LM35E7bpLrubF4OztLts87vjSWTvDs+F4eGd6FM+4DQFuo5p6Kq7mr/Gp0srmrslOZNM+NH+EnvS8yERO/tlJt4bMtd7LVcu7FkiAIPD5yhH9vfw5fUmwB9+6qTfxly050soWHw1OZDA/27ef77a8QSomL6Hur1/HZVTdiUlxYVflexyBfPfQc3QFRjLeZyvjq+ltoNc/dE3Up2DMxyL8dfoVjbrFK3qhQ8RdrtvP+pnUX3F3jYiIIAnvGh/jBsb28MSaekyXAO2qa+f/atrDGsrhOBHkuDHc0wlM9HTza1c5Rx3juuKqwkJtrG7m7cQVXVVQjKyi4eMVFTqcTt3v22cGT1NXVIZ8WAl9K4TmJIAicdNl5tKudJ7rbcUUjuceqdHrualzBHQ0tNJkW12Ymz/kTSyV5pOskPzqxj5GQKFR0cgUfXrmej63agOkyTloXBIHXxwf4t8O7OO0V864syiI+07qD+xvbLmr6QF/AzdcOP8cb2bGbZWodX1q3k5vLmy9oARVOxvnPztf4Zc9eUoIobP+kaQefaLpqQWHz3PdJxflB50s8NLCXTDZ/86P1V/P+2m2o5mjoPp3OwCj/3fMCu12iqJFJCrincisfrr0ek2L+fO3jvl5+M/giB71TgmiHZQ0PVN1Es27+EOF41MHz9jd52b6XQLalkjTr3t18DvduOolMko5AB0d9x+gO9WCPOYhn4nM+Xy6VZwWpKEQ1CxxtOUk6k8IRdzGRFZpnpgBMR4IEs9xMhbqcVv1q2gytWBQLO+fZYy7R3TxPlzicivLE6Jv8YeRVfFl31CTX8u7K67mtdBvqwrmrxjNChl32k/y49wUGw6Igsyi0fKT2Bu6s2LSgsPoRzyDfa3+WEz4xraNJa+Mf1txJm3FxoeP9rgG+fuyPdAfE//vVhjK+3PZOWk0XNnlsIhLgX4+9xFNDpwEwylX8Tev1vKdu7UV1Gk977HzryKu8MtYHgKpAxidWbuITK7dc1iaAIAi8NNzL/zu6lyNOMbJZIJFwT8MqPtW6hQbD5Z+DeqUQTMR5caCXJ7rbeW14IFdULJVIuKqimnuaVnJzbcNZxsxlUdU+ycUQntNJZTLsHh3isa7TPNvXncs7BGg2Wbi9oZnb61uoNVxY24s8CyOZSfNEbzs/OLaXXr/oSqsKZbyvuZU/Wb3psm7kmxEEnhxo57tHX8tVwNdojXxu7TXcVt1y0S4YgiDw/GgnXz/yAmMRUbRfZavlH9btpEl/Yc37+4Iuvn7sj+xxiheicrWBL665hRtLFyds2/1jfP3EE5zyZ1tTKXX8edON3F6xdkEC7rhvkB92P8sRryiwVQVy3lu1g/fVXI3uHL0Yu4LDPDT4Im+4TuRCyeuNTTxQdRNthvlbIiUzSfa6j/H8xBucDEyvaDeyxdxKm2EFq3QNqAsX5pAJgoA36cMes+fE4UTMznh0Amfcmcs1XUq0hdqco1qinHJXi5W2BVXigyj2BiNjHPN1cMR7mhP+rtx7aZBpudG2nZ227diU8wtXXyLEH0Ze5fHRN4hkC6dKlCbeW3kDt5RsnjekLggCe1yd/KjnebqCorjQydR8sOZa3l21DeUCFjKDIRf/3vE8u+zikAZVgZw/a7ye99VuW9QC0REN8q2Tz/N0dhytXqbis6tu5N6adRcUVo+nU/yscx//2f4mkVQSqUTC++rX89err8WguLCipPkYDvr43rHXeaz/FAJQKJHyQONa/qJ1O8Wqy7cgN5XJ8FR/B/91bB8dXnERoigo4L6mVj65ZjOV2sUV+eU5P6LJJC8P9fFkdwe7hvqJp1O5x9qKS7ircQW3N7RQrJ47AnBZCM+hoSE8Hg9PPPEE3/72t3n99dcBaGhoQKNZ2D/CYtspRZIJXhjo5cmeDl4d6ieZmboIrLHauKOhhdvqmynXXr7i50ohIwg8N9jN/zu6h5NusaK2UCLlrvoV/FnrFpqMl68bnUin+U33Ub5/4k1c2SlOa0wlfGH9dVxVWnPRfm40leS/2t/kxx17SWTSSCUS7q9bx1+tvuaCGklPVr9/88RzjEdFYXu1rYG/W3MrtdqFOwkZIcOzYyf4fueLjEd9ADTpSvjrllvYtoD8TzGM3sMPu5/LFSBpC5W8v+Za3lu1A1Xh/MJjMGzn4aEXecl+OCfwVupqeKDqJraYzz0adTRi53n7G7zs2EcoNTVDvUAipVFTw1pDC22GFTRoquetjJ+LVCaFK+7KilFRlMYyi6xqR4IlG8YvUZZgUxYv2jWdxBX3cszXwTFfB8f9nfiTwRmPtxlauMV27k4AIBaA/W74FZ4Z30M820u1Wm3j/qqbuL543by9OAGOePr4Yc9zuUp1Mf/zKu6vvhrNAnpqeuJhftS9i98PHSAlZJAi4V1VG/mzxusXlPoxSSKT5n979/GDjleJTOZb1m7gMytuwHgB+ZaTudvfOvYyoxGxin+duZyvbbiVVcaLFx52xyJ8/8Sb/LrrSO56d3v1Cj6/9hpqdJev2RJJJni46wQ/OXkgFyHTyOR8cMU6PrZqwyWZNPd2J55O8drQAE/1dvJCf88M067OYOL2+mbualxBvXFhtQeXhfD8yEc+wv/8z/+cdXzXrl1cd911C/oeF9LH0x+P8Xx/D0/2dPDmyGDOLgbYUFLGHQ0tvLO+eV4Fn+fCEQSB10YH+K/j+9gzPpQ7fmNlPX/WuplNtorLNic3lIzz0/YD/Pep/bn2I1eV1PDF9dex2nzxLiaDIS/fPPYSz42IeYoamYJPr7yKDzVuvKD8rEgqwX93vs7PevaQzKSRSaR8sGErf9Z89aIaasfTSX4zsI+f9LxKKCUKqx3WRv6q5eYFTYMRBIHXnKf57+7n6QuLixKjXMNHaq/n7sotyM8Rap2Ienhk+GWeHd+X6+dZqS7mlpLN3GjbOG8lPIjh88PeUxz1tXPM18FEzDXjcVWBktX6RlGI6ldQpiq+bD+j04mkopz0d3PML4rNybnpkyilclbpG2nVt7DJtGbeOeoA6UyaA54Onp/Yzx73qVzrqiZtJe+r2sk2y6pz5pC2+0f4Uc9z7HOLbrNcWsi7K7fzwdprMcjPfe6NpZP8un83P+t9nXA2//Ka4mY+03Iz9dq5K/PPRBAEXrP38M0Tz9EfEtPFWo3lfKXtnawyXli+5XH3GF8/+kJuWESJSsvftF7PndUXr4l5OJngJ+37+fHp/YSSU+emL6y/jjUX8dx0oXhiEX55+gi/OH0Yb1zMUTYr1Xxk5Xo+vHI9esXiGvvnWRzJdJrdo0M81dvJs33dBBNTaUMVWh13NLRwR0MLK8zWRZ/zLgvhuRQsVQN5dzTCH/u6eLKng/1jI7m6TwmwtaySOxpbeEddE0blxQuF5BGrFH94fB/PDnTl/gbri8v4VOsWbqpquGyrLF3RMP/v5O4ZrsIdNaKrUK29eK7CPscg3zjyAqd8ooCo0hj5YtsNF5z/ORBy8y/Hn801nzfJ1Xxm5Q2LDjP6EhH+u/sVHhncT0pII0XCXZXr+VTTDRQvoMl3WsjwwvgxftL7AqNRMS3DptTzsbobeUfZ+nPm+nniAX4/8ipPjr1JNC2eQKVI2GBq4ZaSTWwzr5439DuJPebimK+TY752jvu7ZrihIIblWw3NrNY3UaYqpkRpQVeoeUvFaCKTxB5zMxFz0hsa4pivg67gwIxQvxQJDZpq2gwttBpaaNbWLih/sj80zgv2A7w4cRDvNJe0zdDAA1U3sd7YdM7fvSc4wU97X+AVxylAdJXvKt/Mh+uup1h57vBpRsjw9OgxftD5EhMx0UFcoSvjsytuWXQT+J6Ak2+eeI43HOKEHpNczedW38TdVReWbzkeCfCd47t4bFAM16sKZPzpim38SfPWCyoQnI9kJs1D3cf4j+Nv4soOpbgU0ZgLZSTo58cnD/Bw14lc0/dKrZ5Prt7Me5tWX/ZdUJYzGUFg//gIT3Z38Me+rtxYUQBbkYbb6pu5o6GFtcUlF3ROywvPebCHQzzd28mTPR0csU9VacmkUraVV7Gzpp4ba+ov61zE5U6f38OPTxyY0Qi4Tm/kgeY27m1YjVl1eRYiDQd9fDebRwVi6sC76lbzJys30WS4sFzMuUhnMjw6cILvnNiVm360wVLBp1Zs57rSc498nIvZHKAGrZU/bb6aW8tXLWr6x1DYzf/teIEXJ8T3RVkg496qjXygdjulKsM5vz6VSfPU2EF+1vsSzrgYdrModNxevpHbyzZQrp4/HSCcivKq4xjPT+zPVcIDaApVbLes4SrLGjYYmxckQjNChv7wCEd9HRz3ddAe6M25qtNRFSixKcxYlSaKFWbxpjRhVYj3NYXqCzqJJzNJXHEvzrgHR9yDI+bGEXfn9j0J/6yTkkqVVtoMLbQZWlitb0Izz9z06QyF7bzuOs4bzmP0hEZzxw0yDTfaNrCzZBP1mvkLbuLpJK85TvPk6AEOeMRFjQQJt5au4+P1N57z7yj+3mmeHz/J//S+TldQXHCVqvR8uukm3lHeuqiOAX1BFz/tfpPHh46RFoTzdvjPZDwS4Oed+/l17yFi2Vy4e2rW8Pk111GivjjXjUgywe/7TvKT9v0MBn0AVGsNfK7tGm6vWXFZLtrTmQxvjA3yUOdxnhvsykUdV5mL+VTrFt5R05yfMnSRiKdT7Bkd5sWBHl4c6GUiHMo9ZlKqeGd9E3c0tLCptGLJPjt54blAhgN+nu7t5PHudtrdzhmPrbIUc1NNPTtrGlhlWR6htuWGIxLi56cO8b8dR3O9QGVSKTurGrm/uZWryqovy4lIJz0TfOvIq7w2NiVyrimr5RMrNnNVac1F+ayEknF+1LGHn3buyyV+N+uL+dOWbdxWtfK8T+DJTJqH+g/yg/ZX8GcntJSrDXyscTvvql67qAr4o54hvtf+LMd94hz2AomUm0tX84Ha7awynLtKOJZO8ujwXh4cfA1XfMpp22Cq547yTVxbvOqcr2ck4uSFiQO8YD+Qa+8DoCpQsMW0kqusrWw2rUBVuLAK33g6QXugl6O+dnpCQ0zEnLgTvnN+napASbHCtOCipUnSmTTuhG9OYTkdpVRBicpChaqEVn0zbYaWeZvmT0cQBHpDo7zhOs7rzuMMRabC8gUSKVvNq7i5ZDObTSvOmf/ZHRzjydGDPDd+lEC2rZEUCdfZVvPx+puo05x7MlggGeXx4SM8OLCb8ajocGoKFXy84RoeqNm2qM/hcc8oP+56g5fGO3Lv4E2lLXx+9U6qNeffK7cn4OLHHXt4fPBkLvKxwVLBl9buvGjtkeyRIL/sPMyvu47gy05csyjV/GXrDu5vWIv8MuxfPRYK8EjXCR7pOsFoOJA7flVZNX/WuoWryqrz19OLgCcaYddQPy8O9PDa8ADh5FTOplau4Na6Ru5oaGF7edVFEfx54Xke9HjdvDjQy4sDvRyaGJ1xyi8p0nBTTT03VtezrbwKZeHl2wdtORJKxHmyr4OHuo5z1DnlQldodLyncQ3vaVpD+WXoQB9yjPCT9gM8N9yVm6jRbLDy8RWbuKt25UXplzcRCfDzrgP8pvdwLu+0XK3nT1q28J7atecd4gskYvym/wC/7NmLJyGKB5NczYcatvJA7SZ08oU5RIIgsNvZw//0vcF+d1/u+GpDBfdVb+bm0tUoziEiEpkUrzlO89ToAfa7p5qiawuV3Fy6jjvKN9Ksm1/IpoUMJ/19vOE8zpuuEzNEqExSyEZTC2uNDazU1VCvKV9QCHqSeDqRcx+dMfdZjuSZRTzni1wqyzmoxdOcVavShE1hQS9bXLjfGfNxOjDAKX8/e9wnmYhN9UIulBSw3tjEVdZWtptXo5fPX9wRTEZ5YeIYT44eoCMw5ZAWK/TcVr6BO8o3Uqo6t8jrDIzz8MB+nhk7RiwtXihN8iIeqNnKe6s3o5cvzLEVP3d9/KTrTfY6pxaEN5a28CdNO1hrOv9xlEfdo/ywfTcvjk6lCG2xVvHJFdu4tqT+ooio0x47P2k/wJMDp3Mit0pj4OMrNvHu+jUXtcfw+ZBIp3lpuJeHOo/z6khf7n3Sy5Xc07CS+5tbWWFaeE5unoXR5/PkdMvBidHcdQigWF3ETTUN7Ky5NLolLzwvEHc0wq7BPl4c6OW14YEZ1V7qQhnXVNZwY00911XVYs0XJy0p7R4HD3ee4A89p3Iz1SXANRW13N/Uyk1VDZfdKn8o6ONnHQd4pOd47rNiURbx4eb1vL9p3UXpYepPRPnfnkP8ousAnrgoFA1yFe9rWM8HGzZQrFp4pe90Yukkfxg8ys+6dzMa8QFQVCjnvtqNfLh+66K+b7t/jF/1vckLE6dIZlMqjHI1d1du4N1VmyhXnzs/djzq5ZmxQzw1ejDXZBygSVvGHeWbuLm07ZztmARBoDM4xBvO47zuOs5YdGYxkVwqo0lbwUpdDSt0NazU1WBSnP/5Jp5OZEPkbuLpxLm/YBoSiQST3ECxwrxoYTmdRCZFT3CE9sAApwMDtAcGZ4hvAIVUxibTCq62trLFvJKic7izgiBwxNvHk6MHedl+gkRGdN4LJQVcXbySO8o3stnceM484WQmxUsTp3l4YD9HvIO54w3aYh6o2cbt5W3nXJxMkhYyvDDWzo+73uS0bzz7eqTcUdnKxxu3U687vxSYdCbDi2Nd/LRzX65oCGBneRN/2rKddZYL6/M5GxlB4NWxPn5yej9vTky9L5uKK/j4ik3srGi87CJAPT43D3cd5/fdp3DHpvppbyut4oHmVm6pbsobNUtIKpPh0MQoLw/28cJA74xBOgArzFZ21tRzU00Dq622S5qCkReeS0gslWLP6JC4qhjsxT4tV0ICtBaXcH1VHTdU113yP/SVTCyV4rnBLh7qPM7uadXwZqWaextXcV9T62XXVNifiPGb7qP8ouMQExHR9VIWFPKuutV8pGXDRckDjaWS/K7/GD/p3Mdw2AeI6Qp3VK3mY02bWWE8d5hzNlKZDH8cPcmPu97MNdeWSQu4o3IN76/bzErD3BOEzsQTD/GH4UP8bvBArlBEioRrbM28t3oLWy3nnrCTFjIcdPfw5OhBXnOcIpmtsJZLC7mueDW3lq1jvbHunIJFEAQGwuPscZ/itF8UZcFU5KznlShNWRFazYqsK3o+LZYuFa64L/v7DNIeGKA7OHJWbqoUKfWaMlboalhrbGSTqeWcvTMFQWAo4mKX/SRPjR5kNDo1QKSuyMbtFRu5tXQdxnM4pAD2WIDfDx7g98MHccfF82ihRMoNJSu5r3oL600LD8GGknEeGzrKr3r3MxTO9gsukPGemvV8uGEbZerz6/8YTib4Xf8xftG1n6Fp/093Vq3mkyu20aBb+jZw4WSCx/pP8fOOg/T4xfe3QCLhHVUt/MnKTay1XNwpR4slkkzwzIB4bj5gnxLlxeoi3tO4hvua1lB9GbdyWm64oxFeGern5cE+XhsemFGJLpNK2VpWKUZka+qpeAv7nuaF50VicmLSC/09vDzYz0nXzHYlVnUR11XVckNVHVdVVl/WkyKWE4MBLw93neC33SdwRKYqjjfZKri/uZV31jShvoxCT8lMmmcGO/jx6f2c9Ex9RrbaqvhQ83p2VjYu+TSkuRyaHbZaPta8mWtK6s9rUSQIAq/au/lJ15scck8tANaaKnhf3SZuKVuJfIEpBalMmtccnTw8uJ99rt7c8aoiM++t3sydFesWNOrQnwjz7PgRnhw9SG9oIndcKZWx0dzAdksLO6wtC6qeFgSB0aiT04GBnBAdCE+clV+pkMqo05RhU5iwKg1YFHqsCiNWhR6rwoBRrl3SUZlnEk3FccR9uOI+nHEfzrgXZ9yPM+ZjMDJxlpsJoJcV5RzclboamnSVqArOfU5KZFIc8fSx29XBm87OGWJTXSBnZ8la7ijfyEp95TmFoiAIHHT38/DgPnbZO0gLYtjYqtByb9VG3lW1cUEdECbpCjh4sG8/Tw4dJ5INzetlKj5Qv5n3120+716c45EAv+w+yG96DxNMihf2pYggzEdfwMOvOg/zu94TuZ+pkcm5v6GNj7RspEJz+TRPFwSBk247D3Ue5/He0wSzLZwKJBJuqKzn/uZWrquoyxcLLQGCIHDK5eDlwT5eHuzjmGN8xtnIqFRxbWUNO2sbuKay5rLRGXnheYmwh0O5lcgbIzOTeWVSKZtKK7ihuo7rq+qoMxjzCdUXSCqT4ZWRPh7qPM7Lw725KkmNTM5d9Su4v6mVNZYLawmxlAiCwF77EL/oOMQLI925/BubSsP7mtbyQMPai9Io+ah7lJ927uPZkY7cz2zQWfhY02buqj7/1iWH3UP8uu8Az4+eJpUVEGZFEe+uXsd9tRspXYTLNBBy8vDgfp4cOZKbj60skPHOsjbeW72ZFv25HVVBEGgPjPD06CFec57GFQ/MeLxBU8J2awvbLS2sNlQtuFVUOBWjMzDI6cAgpwMDdAQGZ3VFp1MgkWKR67Oi1ECxQtyqCxZXPZ0WMrgT/mkC048z5iWcnr8RvRQJtZqybLpANSt1NZSpLAv+X3DE/OxxdbLb2cEBTw/RaSkChZIC1hpruaV0LTfY1qBeQGFWKBnjqdFjPDK4j77QVOHmBlMN99Vs4XrbigUvvpKZNC+Nd/Dr3gMcdE+FoOu1Fh6o3cTd1WspOsfwgbk46RnnZ137eXpo6jNdozHxsebN3FOzBvV5ft+5SGcyvDTaw686D/P6+EDueLXWwAeb1nNfQ9tlIyRA7If9WO9pHuo8zmmPI3e8Smvg/uZW3t2wClvR0ovytxuhRII3RgbZNdjHrqG+GQYLwEpLMTdU1XF9dR1ri0suu5QLyAvPt4R4OsWBcTH3YtdgH/1+74zHq3UGrquq5dqqGraWVV5WDt1yxB4O8rueUzzceTzXXgSg0WDmnoZV3F2/8rIqSBoLB3iw6ygP9RzNTUMqlEi5taqZDzWvZ1Px0jfSHwn7+GXXQR7uO0IoW4hkUqj5QMMG3t+wAct5TkNyxkL8duAQD/cfwhETUwqkSLi+tJn3121iq7V2wb9LJBXnmdHjPDy4j+7glDtcp7Gys3Q1O0tX0aA9d7qAIAh0BcfY7epgt7OTU/7hGa6lTqZmq7mJ7dYWtlqa0J8jL3Q6GSHDcMTJQHgMZ1wUhY64D2dMdCA9iQCZc1SgLwXqAmVO0FqVhqzbaqRUaaZRWzHvfPQzSQsZTvuH2e3s4E1XB93B8RmPm+Vatlmb2WFpYZO5kaIFis1XHZ28MH6S3c6eXA6oqkDO7eVt3FezZUF/y0kcsSC/7T/EIwOHc5+zAomEG0pbeF/dJrZYzq+DREYQeHmsm5917mOfc8rF32Kt4uPNW7i+rHHJU6bcsQgPdR/jwe4juUpvCXBDeT0fbN7ANWW1l02aViKdZtdwH4/2nuKlod5cyztFQQG31jRxf1MrW0urLpvXuxwRBIEuj4vXhgd4dXiAfWPDMyYtqgtl7KiozhpXtZRoLn9xnxeelwH9Pq8oQof6zvpQyaUFbCot55rKGq6tqqXZtHBnIs9MMoLA3vEhHuo6zrMDXcTT4klSAmwpreRdDat4Z03zZeMixNMpnh3q5JedhznknKoGbjFa+WDTeu6uXbXkFavBRIxHsnlrk7Pg5dIC7qpezfsbNrDGtPB8zekkM2l2jXfyYN8B9rkGcsfrNBYeqNvIXVVtC+6XKBauDPLwwD5etrfnipHE72fl5tLV7CxdveBpNd5EiL2uLna7Otjn6iKYmnIMpUhYbaii1VBDs66MZm055WrTeYfKxRZIAZzZUHguJB7zEcskz/0NpiFFglGupTjrnFqzN4vCQNEihOWZBJNRuoJjdAZG6QiMcsDdgy855apIkLBCV8F2azM7rC00acsW9H6EU3FetXfwwvgp3nR258QmiH+391Rt5vaKtYv6HBx0D/Fg3wFeHGvPuZAWRRHvrlnPfbUbKVGd37UgkIjx2OAJ/qfrIAMhMS+0UCLltqqVfLRp83n/H8yFIAgcdY3zq65DPDXQkRNwRoWK9za08oHGdVRqDUv6M88XQRA47BjjDz2neKq/A1986v9lhcnKfU2t3NOw8qLOm7/S8cdjvDEyyKtD/bw2PDCjtyaI5tT11WK9yJayiovSFeVikheelxmTNrq4uulnNDgzJGgr0nB1RTXXVtVyVUV1foLSeRJIxHmmv5NHe0+xd3w4d1xRUMjNVQ3c07CSaypqlzy/8nw55bHzq87DPNZ/KteIWitT8O76NXygaR31+qUtnkplMjw70sFPO/dy3DPlcK0ylnB/3VruqF6NVnZ+Ar0n4OQ3/Qd4bOgYkay7qi6QcUdVK++pXs9KQ+mCF1fBZIzX7B08P36S3a6eGSK0XlPMztJV3Fy6mroFitBUJs0J3yB7XJ286eqgL2Q/6znqAgVNujKatWU068pp1pVTpbZc1gVFc+FNhOgMiCKzMzhGV2A0NxlqOkWFCraYm9huaWGbpRmTYmFpH+FUnNfsnTw/fvIssVlTZMkuEkSneqF/c38iyjMjJ3mo/yBdgamQ7npTJe+r28TO8pXIz+NvIQgCh1wjPNx3lGeGT8/4P3ugfj0fatxI6RI3fY+mkjw50M6vOg9zwjOVg9xmLuWDzeu5vbrlspnUMxjw8mjPaR7tPcVAwJc7Xqwu4q66lbyrYRUrzfk2SOdDOpPhmGMid90/5piY0e5IUVDI1rIKrqms5bqq2mWfjpcXnpcxgiDQ5/PmPox7x4aJpaZO3JOV8tdWimH5tuLSfML2eTAaCvBY72n+0HOKHt9UcYRZqeaOuhbe1bCK1sskH9Qfj/Hb3uP8b9cRBoJTKRpbbJU80LCWW6ubUS7h6nfyYvy/PYd4bmTKiVEVyLijaiX316+j1VR2Xu9NKBnnieHjPNh3gN7gVG5fvdbCHZWt3F65hnK1YcHfL5iM8eqkCHX25OaFgyhCby5bzc6SVQsWoSC2aDrg7hbFWXCU7uD4DPE0iUIqo1FbmhWk5dRrS7AqdJjkmrdckAqCQCgVwx0PMhxx5dzMzsAYjrh/1q8pU5lo1pbRpCunNev4LvT3iKTivObo5PkxUWzGp71f1UXmnCPduAixmUineNXezRNDx3nV3p1bYKgKZNxeuYb31W2iRX9+c8d98SiPDp7g4d4jdAem2mc16a08UL+Oe2valjyy0O518FD3Uf7QdypXLCSXFnB7zQo+1Lz+sqlO98aiPNXfwR96TnHYMZY7ri6UcWtNE+9qWMX20qrLMo/wcmciFOS1kQFeHRrgjZFB/PGZudmNRjPXVtVwTWUtm0vLL5sFyFKQF57LiFgqxcGJ0Zz93umZ2WNQJ1ewo6KKaypruaayhnLtlfk+XCwmqzEf7TnF473tufxKgHq9ibvrV3JPwyoq38I2FJNkBIHXx/v5Zcchdo315VbHBrmSe+pW80Bj25K3ZPLEIzw6IF6ge4NTAn2FoZj769ZxV/VqtAtsHj8dQRA44Brkof6DvDzeOUOobDRXc2dVK7eUrVxwY3oQp9tMitA9zt4ZIrSmyMJmSx1bzHVsstQtqDp+klQmzWDYSWdQFG5dgVG6gmNE5ujBKUGCQV6EWa7BrNBhUWgxK7TiVq6b2lfoFjVxB8R8Ul8ijCsexB0P4koExG08iDsewBUP4kmEcMUDs4rlyddXqbaIaQS6cpq1ZTTqyhaV15oWMnT4x9nv7mWfq48jnsEZf8OqnNhcRZN24Qs4QRA47B7myeHjPDt6KjctC6BFb+PuqrXcU7V2UZ+L6d/7oGuYh3qP8Mxwe25BpSwo5Paqldxft5615vNbUM1FOJngqYF2Huw+yjH3tOEXRXre37SO+xpaL0of38UST6d4aaiXR3tOsWukL5f6JZVIuLqshnsaVnJLdWO+9mCRxFJJ9o+P8tpwP68NDdDldc94XCdXcFVFNddU1nBNVc0VPYo7LzyXMeOhIK9nE45nWzHVGUxcU1nD1RXVbCwtR684/7yvtxupTIbXRwf4Q88pnh/szoXdALaUVHBPwypuqW68LC4U4+EAj/Qe55Ge4zPGzm2wlnN/Yxu3Vi5t3up8F+3bKlfy3rq1rLec31zfUDLO82OneWLoOPtdA7kyHLm0gOtLmrm9cg07iusXNXUpkIzyir2D58dOstfVk8sFBDFPskVfxhZLHZvMdbQaKtAscjZ3RsgwEnHnXNHOwBiDYQeeRCjXEmghSFnc+7XYIiVtoZJipYEmbVlOaDZqyxZUDDTj5woZ+kJODrkH2Ofq5aBngEAyOuM5lWpTztls1i1ObPYEnTwzcpKnhk8wkh1MAFCs1HJ75RruqmylSX9+PWed0RBPDJ1a8sXTXEzmbv629ziP95/OTRArlEi5ubKRBxrXsqO05i0vvklnMhy0j/J432me7OvIjSUGWGkq5l0Nq7izfgW2i9BZ40olncnQ4XGxZ3SIV4cH2D82khthDG/viGVeeF4hpDMZjjvtvDbcz+vDgxyxj+VaCIH4IV9hKWZzaTmbSyvYVFqRn6S0QEKJOM8OdvOHnlPsHhvMXe6lEgkbi8vZWd3AzdWN1LzFjZDTmQyvjffzUPcxXhzpzv395dICrimr5bbqFm6qaFxSEeqLR3ls8CQP9R6hOzAVLi9Rabm1ooWbK5rZaKk8r1DceMTPUyMneGLoOD3TQvHKgkK2W+u5vrSJ60qasCgXfjEMJKMcdPezz9XHflcv/eGZUQMJEhq1xbQZq1lrqmKtsYoyleE8K6Iz+BIR3InAlCuZdSNFh3Ly2NyO5LmYdFRFB3XSTZ3ppFrkWkwK7aId1UmiqQQnfSMc9Q5x1DvEMe8wodTMRa6mUMEGUw1bLPVsttRRryle8HuWymQ47B7i5fFOXp7oZDg8lUKiLpRzS9lK7qhcw2ZrzYLbXE1nPBLghdFOnh3pZL9j6v9XXSjj9qpVPFC3jjWmhecVn4tJsfnMUAfPDHbMWAzWaI080LiWe+tWY1G9teffaCrJayMDvDDUzUvDvXhiU4uHErWGu+vFvM1m09IPs7gSSaTTnHDa2T8+zIHxUQ6Mj85o4A7iSO1rKmu4prKGHW/jGo288LxC8cdjuZXW3tHhs1o2AdTqjVkRWs7msgoqtfrLIo/xcmY8HOSxntM80dc+o1cdiO2Zbq5u5ObqRlotJW+pi+GIhPht7wn+0HeS3sCUszMpQt9R1cxNFY1L5oKLYdFRHu49wnOjnYSSUydck0LNTeVN3FLRzLbimkVXYAqCQIffzhPDx3hutJ3x6My8xBX6ErZZa9lWXMcGc/Wi3FB7LMABVx/73H0cdg8wGj37/8Sq0NKos1GrsVKrsVKX3RrlSyMcJnMw4+dR1a6TqZcshzSZSTEYdtMfctIfctKXvfUGHWc5t6oCOWsMFWwy17LFUs9KfdmCX4cgCPQFXexx9rHH0c9+10CuPyuIn9HtxfXcXrmGG0qaF/X3nKQ/6Ob5kU6eG+nkmGdsxmNrTWXcW9vGHdWrzrtA7kwygsAR5yjPDnedJTbVhTJ2VjRyf2MbW21Vb+k51hUN8+JQLy8MdfP66OAMB04vV7KzuoF7GlaxreT8FotvJyLJBIft4+wfG+HA+AhHHOMzajBA7Bu9oaSMqytrFI5hIQABAABJREFUuLayhgajOX+NJS883zY4ImEOjI+wf2yE/eMjdLidZwXqSoo0bCqtYHNpOZtKK2gyWd7yENDlzHDQz4tDPbww1MO+8eEZIdxidRE7qxq4sbKB7WVV53XxXAoEQaDL5+LpoQ6eHuiYIUILJVK2lVRxc2UTt1Q2LVmD+ng6xesTfTw30slLY134E1PumKZQzvVljdxS0cw1JfWLLtoQBIHOgF10x8Y7OeWb2VNSJi1gnamSbcW1bLPWsdpYtiiXzBkLcizr7B31DtHhH5vxd52OQabOidHpgrREpbuok4kulFAyRn/YlROYk7eRiHfO1ACbUsdaYxVrjdW0mSpp0pYsSvA6osGc0Nzj7Mv12pzEIFdxXUkT15c0s8NWv+gm74Ig0O6z81xWbE533yXAeksFN5c3847KFZQXLU2OdjKTZu/EEM8Od/H8cBfO6FTLKXWhjBsrGrituoXryuressIQQRDo9rl5ebiXF4Z6OGQfnXHer9Doubm6gZ1VDWwqqbhsunhcjnhj0ayTKV5DTzrtM6KKACalKnsNrWBzWTkrzMVvm/D5YsgLz7cp/niMg+Oj7B8f4cD4KMedE6QyMy86eoWSTSXlbMoK0TVWG7KC/IlpNvzxGLtG+nhhsJtXRvoJJaeKTRQFhWwvreL6yjpuqKx/y4qTpovQ54e66PCdcXG2lnNLZRO3VjVTtUQ9A5OZNPudQzw/0snzI504YlP96BQFhVxtq+XmihZuLGs8r75/rliIfc5+djv72OPoYzw6s/2YVqZgi0V0Q7dZ66jRmBblOETTCTr84/RNE2h9ISfjUd+cX6MskFGlNmGUF2FUFGGQqXNbg1yNSV6EQa7GkN1e6MVeEASCqRi+RARfIoI3EcY7bX/6diLmxxELzPm9igoV04S0hZoiKy36UkpVhkW9pnAyzn7XYFZs9s1IlQDR1dxgrmZbcS3brXW0GEoWHUbPCAKHXSOisznawUh4ygkvlEjZZqvh5vJmdpY3YVUtzaIqmkry2lg/zw138uJIz4xcSK1MwY0VDdxa1fSWis1YKsnu8SF2Dffx8nAfI6GZEYI1Fhs7qxq5ubqBFqM178DNwVgowIGxyWvkyFnFQABlGm0udW1zaXne0VwgeeGZB4BoMskRx3jOFT1sHyN6RthAVVjI2uJSNpSUs76klPW2Mgxv0xyV+YinU+wdH+b5wW52DffNCLuBGJK/obKeGyrr2GArf8tchoGAl+eGu3h2qJMjrpnhyBXGYm6tEp3QZsPSXJwygsAx9xjPjXbw3EgnQ6GpsHaBRMKW4mquK21gh62GJn3xot12QRAYDHvY4+hjt6OPfa7+XKuaSUpVOjZaqllpKGWVoYyV+hKKziPcGk0lGDjDNewLORkKe2ZU0C8ETaESo1y96BzMtJAhkIziS0TmdGXnwqLQzHBqa7L7xQrtov/WGUFgMOTmlG+ck94xjntHOeEdnfGaJMAqQ1nOiV5nrjyvnFNvPMJexyCvT/Tx0lg3rtiUy6gsKOSaknpuqWjm+rIG9PKlOTf5EzF2jfTy3HAXr4z2EU1PpURYlGp2ZiMG20uqkb9FC/PRUICXh3t5ebiX3WNDM4ohFQUFbC2t4qbKBnZWN1CaH1t5FqlMhg63k8P2MQ5NjHFwYvSsHtoA9QYTm8sqcilqFZdBh5PlSF545pmVZDrNKZeD/dmwwsHx0RkTKiapN5jYUFLGhpJyNpSUUWcw5cPz0xAEgS6vi5eH+3h5uJdDjtEZ4RmdXME15TXcUFnPdRV1mFVvTZX8RCTI88NdPDfUxV770IzXWKM1cktlE9eW17HRWrEkF1dBEOjyO3luRBShHf6Z+bJmRRHbbTXssNWww1ZL2XmER9NChlPe8ZzrdtgzPKPBPIiCqEZjZpWhjFWGUlYaS1mpL0Vznrl/yUya0YiH0YgPXyKMNxnBGw/jm7adciCjM0Z1XiiqAjnGrJNqlKtn7E9uLQotNRrLolpITScjCAyE3JzyjXHKO84p3zjt/vFctfZ0qoqMbLPWsa24ji3WWgznIQRjqSSHXCO8ae/nTXs/p7wTM94xrUzBjWWN3JxN3ViqlJahoI9Xx/p4caSH3RMDM6bJlRfpcpGBDdbytyQXMpXJcNgxmjuvdHpnFsmVFmm5IRth2V5alW99dAb+eIzDE2M5oXnUPk4kNTPHWiqRsMpSnAudbyotf8vOz1caeeGZZ0FkBIEer5tDE2Mcmhjl8MQYfbMULOkVStbbJl3RMtqKS5a8+fJyxh+P8epIPy8P9/LKSD/e+FQlqQRos5ZydXkNV5VVs6647C0ZheaNR3lxpJvnhrp4baw/1y4JoKhQzraSKq4tq+OaslqqtUtTyT8Y8vLSaBdv2PvZ7xia4SoB1GhMoggtqWVrcfV5uVnRVJLD7iGOeUc47RNF00T0bFdjNjFaq7FgURQtaRhtumPpTYRnvM8LQQLoZapcyP58q9bnIppKMhzx0uGbEIWmb5x2/0Ru2tR0lAWFtOhLWJV1kjdZqqkoWvxnI53JcMo3wW77AG/a+znoHD7rfWnUWdlhq+Ha0nq2FtcsyUIokkyw1z7Ea2P9vDrWR39w5rmtQW/m1spmbq1qYpVp4Y3vl5KhoI83Rwd5Y2yA10cHZ+ROSyUSNhSX58RmszE/WnkSQRDo9Xk4PCGKzEP2UXq8Z0/n0srlrLWVscFWxvqSMtbZSi+b8clXGnnhmee88UQjHLaPi0LUPsYxx8RZVX1SiYQmk4V1xaW0FZew1lZKo9Gcr5gkOybNNZELkZ1yz3T9VIUyNpdUcFVZNVeV1dBisl5yNzmcTPDKWB8vjXTz2tjAjNAmiG7oNWW1XFtWx1Zb1ZIsMhLpNEfdozmX67hnZmswqUTCGmMp2201bLPV0GYqO2+H0h0Pc8oriqpTvnFO+8bOyhOdRFlQSIXaSGWRkYqi7DZ7v1xteMsKyM6XjCDgjAUZDnsZCXsZjngZDvvE/bAXVzw069dNF5mrDWWsNJZRp7GcVxFFRhDoC7rZ7xhit72fPY5BfImZPUFtKg07bHXssNWw3VZDserCQ8WCINDpc+aE5gHHyAyBWyiRst5azrVlddxS1UTDEo+kXQjeWJTd44O8MTrIG2ODDAV9Mx43KJRcV1HHDZV1XFtRm5+NnsUdjXDUPs4xxzhHHRMcc0yc1eMaxK4u60vKxIidrYyG/HXpkpEXnnmWjGQ6TbvbmVtVHp4YYywUPOt56kIZa4ptrC0uZW1xKW22EkqLFp9bdqVhDwd5ZbSfN8cGeXN0cMbkJBDzybaXVXNVuShEyy/xZIuMIHDaa89drA85ZubxyaRSNhVX5oRoyxLlhgYTMfY5h3jT3s9u+wA9gTN7b0KDzkqbuZQ2UzlrzWU06c+/mtQdD2cdUTGc3OGfYCziP2fDdqtSQ6VaFKWlaj0GmQqdXIkuu9XLVOhkSnRyFeoC2UX5vCczaYLJGP5EjEAySiAZw5+I4k9GCSRiuOOhnLgciXjP6bLqZUrqtNask1l6QSITxAbuRz2jHHOPccwzxnHP+IzWWwAamYKtxdW5NIs67dIUbPjiUd4YH+DVsT5eG+vHHp0prCuK9LnP7vaS6kvudsVSSQ7YR3ljdIA3xgY55bbP+MQVSqSsKy4TF6LlNayzlr7thVI0meSky84xxwRHHeMctY8zMktupqKgkLbiElFoZh3NfNj8rSMvPPNcVCZCQY5mTwrH7OMcd04QTp7dr9CqLsoK0RLabKWsthS/rQuXBEGg0+vijbEB3hgdZN/E8Fk5SLU6IzvKqtleVs2WkopL3pA6lIyze2IwJ0SHz6ietSjVbCquZFNxBZuLK1lhLF6SC+VEJJALxe53DjEWOftCoywoZLWxlDZTGW1m8VauPv8+tclMmvGIP+sKeqdcwuxteh/KhSCTSNHKlOjlqpwwVUgXl1aRyVazB7Ii05+MzRoGn48CiYQytSHr3BpyTm5lkYkKteGCCnTCyQSnvBMc9Yxy3D3GUc8Y47P8rVQFMlpNpWzLCs1WU9mStKBxRsMccAyz3zHMfvswHT5nbrQsiJ+RrbaptJE63eI6HlwoyUyaEy47e8eHeGNskIP2EeLpmQuBZqOFq8qq2VFWw5aSCjRv49BvKpOh2+vmhGMid03pdDvPamkE0GA00ZY1NtbaSmg2Wd+ywq88Z5MXnnkuKelMhl6fJ7s6neCYY5yOOU4elVo9a6w2VlttrMne3q5iNJFOc9Q5xutZN+SYc/ys96xeb2JraSWbSyrZUlJ5SatXBUFgIOjNuUl7Js7O09TKFKy3lrO5uILNtkpazaVLksPqjIY45hEdtGPuMY57xs6qZgexYKnNXMpKQwn1OguNOgu1WtMFt70RBAF/MpYToiNhLxOxAIFELOc0TncfF1uBfj5oChVnuaw6mRKTQp1LD6gsMlKi0l+wyBMEgfFokN6Ai56Aiy6/k+PuMboCM4UeiO50o97KWlMZreYy1prKadRbl+Q1DIf87HcMi2LTPnxWniZAk97CteV1XFNayyZbJcpLmEMdS6U46hxj38QI+yaGOewYI3rGYrJEreGq8hp2lFWzo6z6bTuiMicynROccNg54bTT7nbOaHg/Sc60sInGxRprCTrF21egLwfywjPPW040meSUyyGKUcc4Jxx2BgO+WZ9bodVlxWhJToy+HceOBRJx9mWdkr3jw3R4nWc9p1KrZ2vJpBCtoEp7fqMfz4d4OsUJ90TObTrkHD1LDMqlBay1lLKpuJLNtko2WMvPO1dzOhlBoD/ozonQo+4xOvz2GZXJk0iAyiID9ToL9ToLDTozDTorDTrzks7snkQQBCLpJIFEVohOE6ZnVtyfCwkSNDKFKC4nQ/oyJVqZ8qI0rU5lMgyHvfQEXPT4XfQE3PQGXPQF3bNWtYM4OrXNXEabqZw2cxmrjSVL9jfu9rnEz5djmAOOESYiM9N6JECz0cqW4krxM1ZcuWRDEhZCOJngkH2UfRMj7J8Y5qhz/KzUBoNCyZaSSjGFpqyaev2ldV0vB5LpNN1eNyedosCcT2RqZHJWWYtzaVprbaWUFGnedu/ZcicvPPNclvjjMU45HRx3TnDCaeekc34xuspiY4XZSovZygqzlUqd/m3V1skXj3JgYoS9E8PsnxjhpNt+lttUotawwVZOm7WUNksJq822Sxa6S2cydPic7LdnHSnHyFmFSlKJhGaDlTWmElaZbKw22WgxFi9JwVI8neK0184xzxidfkfOnZteGXwmxUoNdTozNRoTpWodNpUGm0qbu+nlyivqghdPp3BEQ9ijQezRII5oiImoWHzUF/AwGPLMmRNaKJFSrTHSoBcF/BpjKW3mMmxLUAiUEQQGg15OeezizWvnmGv8rL+dTCpljbmUzcWVbC6uYIO1YslGwi7kNfb5PRx3TnDMNc5R5zgnXBNnRSWsqqLcYnBraSUNBvPb6jzlj8fodLvo8Dhpdzs57XLQ7naSSJ/9udLK5ayy2GZEvWr0xrfV+3WlkheeeZYNk2L0hHOCEy5RjA74fbM+t0gmo9k0KUQttGRF6dulPUYwEeewY4x9E8PsmxjmmHP8LMdPAtQbzLRZSmjNitEVpmKUhRc//DgZmt9vn3KshkK+s54nAWp1JlaZbOLNKG5NygsvDBAEAXc8nHPuerK33oDrrMKT2VAUFGJTaijOiVFRmBartFiVRWhkCjQyOUWFcooKFagLL05B0Vwk0mnCqTjhVIJQMk4omcCfiGLPiktHNMhEVmDao0G8Z1STz4ayoJB6rVl0h/UWGrJOcbXGuCSDEJKZNN0+F6e8osg87bFz2uuYMQlsEnWhjPWWcjbbxDzitZayS9JZQBAERkIBjrvGs0JzgpOuCYKzvMYKjY4t06IONTrjFbVYmYt0JsOA3ycKTJeTDreTdo9z1qbscLbIbLWWUK035EXmFUpeeOZZ1kyK0dNuBx1uJx1uF11e16wraBDd0UlXtNFooclkps5guuITz2OpJEecYirDUdc4x53jjIXP7jhQKJHSYrLSZi1hjaWENkspTcbzr2JeDPZIkCOusSlny2OfUwCWqXWsNBWz0mSjQWemTm+iTmtaskbZwUSM3qCb3oCboZAX+xki7cyWPwtBgtgHVSNTiGJUJgrSIpkcTaEC+SLf4wwCkVSSUFIUl+FkglAqTjiZIJxKLLovKIjpD5OObnFWSJcX6anRmGjUWyhTL00kISMIjIUD9Abc9PrddPlcnPLY6fQ5Z33dioJCWgxWVmYXIG3mUlqM1ksy9csZDXPcOc4x1wTHnBOccE3gPqPjBIiifJXZxlqruJDbWHzlT7YRBAFHJEyXx0WXx02Xx0W720GX131Wa71JyjTaXHSqxWxltcWWF5lvM/LCM88VRyqTod/nod3tEsVodtU9PovQArGyt0ZvpMlkzonRJpOFGr3xihakZ15Qj7vG8cTOFlTKgkJWmotZaSpmhclKi6mYFqPlkoTpndFwToSezrpgA7MUjUxSptZRrzdRnxWj9Toz9XozNtXS5oGdGZYWb1NOoisWFp3GVIJwMr6E84kWj6KgEE1W4OpkCkqyrmyxSpPbL8neN8hVS/o+hZMJ+gMeegMeegNu+vzitj/gmTHWcTpamWLK4c663PV680Vf/KQzGYaCPk57nHR4HHR4nJxw22c9b0xfoLVaSmmzltBouDQLtLcCQRBwRiN0TwpMr4tuj5tur3vWHpkgjlhuMllYkV3oTwrNS5X+kOfyJS8887xt8MWidHiyYtTtpMvjptvrIpiYvTCiUCqldpogbTSZqTeYqNEbL0k4+lIjCAKjoQDHXBM5QXrCNTFrmBOgSmtghcmaE6MrTFaqtBffuQgm4rR7HZzy2OnwOegLeOj1u/HE53Yhiwrl1OlM1OlMVOuMlKt1lBbpKCvSUabWXtSRgoIgEE0nc45kKOtIhpOT+3FCqQSpWYqf5kMCqArlaLKOaVEurC9HKxPvqwvlF9UVTGcyOGNhxsKB3G0k7KfP76Ev4Jm11dUkMqmUGq2Jer2JBp2FVaZiVplKqNScf9urheKPx8QFqVcUme0eJ51e11lV5pDtE2sw02YtzUYBLl1KyqVGEAQmwiH6fB56vO7sOVJ0MmcbmQxibnaNzkCjyUKzycwKczEtZitVOv3bvs9ontnJC888b2smT7RdHhfdXjfd01bzcwkuCVCh1VNvNFFnMFFvMOX2rSr1FZXDNVk0ccI1QafXxemsE2SPzB4CVxfKaDaKYrTJaKHBYKZeb6KkSHvRBak3HqXP786Gb7MOW8DDYNA7a7uu6RgVKkrVWsqKdJQXZUWpemrfrFS/JeNL30oygkAgEWMiEhRF5eQ2HGAs7GcsHGQiEjxneyiTQkW93px1n03U6cT9Cs2Ft3I6F6FEXHRZ/R66fW46PGIEZDQ8uyBWFBTSbLTQYrLmHP5LWYR3qYilkvT7ffR63fT5vPT6PPRlb7P1WQZRYFbrDDSazDQazTRlF+N1BtMVKcLzXDzywjNPnlkQBIHxcFBc8XtcdHnd9Hjd9Ho9BBJzNwvXyhXUG0zUGYzUG83U6g3U6I1U6w1X1Mx6TyxCe/Yi3u5x0uF10OV1ndUAexJVoYw6vVEUIPqsWNebqdUbL3pBSCKdZijkozcrSodDfsYnBVQkMOcC40yKCuUYFSpMShVGhRqTQpW9rxa3ChVGpRqjXIVBoURZKENVIEMmlb6li5GMIBBLJYmmU4SScTyxKN54BG88ijcexROP4o1FxG08u41F8SaiZ3VGmI0CiYSSrGgvVYsucp3OmE13MGO8yKMcM9n/1T6/hx5fdtHhE8P6E3MskADKi3S0mKysyKWQWKnVGa8Yly6dyTAeDjLo99Hv94oC0+um1+dhNBiYM/2jQCKhWm+kzmCkKZt61GiyUG8wXnDP2zx5IC888+RZFIIg4I5G6PV5ci5Br1fcHwkG5r1QF6uLqNEbqdEbqM5ua/RGqnUGNPLlL0pTmQz9AW8udNnjc9PjczMY8M3rilVodNTrxZzMap2BSq2eSo2eSq3+oobAJwkkYjknbzQcYDzn7C3c1ZuPAokEVaEMZYEMVWEhqgKZeD+7P7ldrPuXEQTi6TSxdJJoVlhGU8mcyIymksTSqVn7IS4Gg1wppiTM4QYXqzSXJP/SHgkxHPQzHPIzHPTlnMw+v3fWEPkkFqU6t9BpNllZYRRF5pWQa5jOZBgLBRnwexnw+xjwexn0+xgIeBny++ctMNPJFdQbpyI29QYzdQYjVTrDFZ3bnuetJy888+RZImKpFIN+b06U9no9DPh9DAZ8eGcp2pmORaWmNuuMVupE0TW5LS7SLOuKz2QmzXDQT++kG+XPvj9+95x5Y5OYleoZQlS8ieK0rEh3SS6QGUEgmIjjmeESRvDGojmXMHc86yb6E7FzhvffCtSFsqw7q8aoFLcGhTjR6Ez31qRQo1coL0mKgSAIeGLRnKgcDvqnbiEfo6HArAMAJimUSKnWGXKpHXV6UUjV603LXmCGkwmGA37xFhS3QwEf/X4fwwHfvO+LTCqlUmegRm8QozBZcVlvMGG+wtKC8iwf8sIzT55LgD8eE10Jn+hGTIa/Bv2+WSvJpyMvKKBCo6MiK0SrdFlRmr2vVyzfRuaemOge9/jF0OhQcEp4zJfSAGLOmU2toUStwVakndo/475GJr/k748gCCQzGaLpJPFUiuikK5ndn+5KTt4WK1QlErHjgOiaysT9wilXVVk402FVFBS+JQuYZCaNMxJmIhLCHgkxEQ7iiIRy9+2REGOhAJF5XEsQxWW5RpdbgNTojNm0DTOVWv0laa10MUik04wGAzlRObkdyW7PeX6QFlCp0+cWrpORlBq9gTKN7opJHchz5ZAXnnnyvMX44zExPOb3MRjwznA3xkPBcwoSjUxOmUZLqUZLmVZHuUaXu1+u1VFSpF2WoTN/PDYjtDrlgIkX5bna8ZyJulCWFaOiKLWqijApxRxNg3IyN1PM3TQoVFdsS5ylQhAEwslELhfUE8tu4xG8sRjuWBhHJMxEJIgjEsYVDS+4nZRNrck53FU6wzSn20CJWrPsRNSkkzseCjIaCjAWDDIWCjAWmto6wqFzvj96hTIbBckKb52BWoOYplOm0S679yXP25u88MyT5zImmU4zEQ4yHDjbERkO+nFGwuf8HhLAqi4ShahGR5lWS0mRNifEbEUabEVFy6pwQOwrGGY0FMi5ZvZpjtrk/rlc09nQy5ViAZFSlSsm0sgUaORi66Ki7E0z7Za7L7/0E4oWSzydEls7JbNN5xPT9nPbeG7fF4/hiUXxxadE5mKb08ukUrEh/eRnTq2hRK3Nff5K1BrKNfplVR2dyeZ728Mh8RYJMREKMR4OMh4KMhYMMBoKLijHVllYOCO9plKnp2LafZ3iyqqqz/P2Ji888+RZxkSTyZx7knNVshc98f7CLnwguiolRRqKizSU5ERpkRi2LirCqi7CrFpebYUiyQSOaDgnRiciIdzRbE5mbConUxRW8+ebLhSxv6YMeUGBeJNObguRFUhzW8W0+/KCAgoWKVYFxIVJIpMmkd2eeT+RTpOcto2lUuc10Wg2FAWFmLMCPZczmhXrNrWGYvXU58ikVC+bPOWMIOCPx3BFwtgj4Slhmb1NhEM4IiEckfCCe69a1UWUTVv4lRbpKNdqKdPoKNVoseTzLfO8jcgLzzx5rmAmQ3258F421DdxxoV0MZXPWrkCq0qNRV2ERaXGrFJjUauxqIqmttnjRbLL2/2bTiqTwR+P4YlH8WVF6WQRUWiaGxhMZMdSZo9NdwsX0n7ockFZUIhWrqBIJpvm4Cpy+5NbvUJ0gCcLjyYF5qWYi75UJNNpvLEormhEvEXCM7fTjnti0QULSglgUReJDm520ZZLe8mmutiKNMtqsZYnz8UmLzzz5HmbIwgCgUR8pqOT3dojUwLVHY3MW0E7G3JpAQalKFwMk47YjJtSFDIq8b5eoUQrVywbd2w6giAQS6cIJuJEU0kSmQyJdIpkdjt5f7oLmXMn02ky5zFYc9JNlUnPdFcLkM1yX1lQmEsVWI65rIIgEE+n8MfjYq/R3C2Wc7B90/JOJ29zTSebD71CiVWtpqRIS7G6KJueMhkBEIWmRaVGtgzzp/PkeStZjF7LL9ny5LkCkUgk6BVK9AolTSbLnM8TJkOQ0QjOSBh3ziWK4IqGs9vsfjSSC+s6ImKxyYJfD6CRK9ApFOgVSnTZfZ08e1+hQCdXolcoRMduWu6lRi5u1TLZJRevkmy/zuXkBF5q4ukU4cSkQ5wknM0vDSXiBBJx/PEYgXicQDyOPzG1H4jHCCTE/fNNFZAA5kmHfppjP92ln9w3q9TLsiAvT54rjbzwzJPnbYxEIsGQdS4bjOZ5nysIAtFUMudEeWdxoaY/Nvl4LJVCQJzHHkzEGQ3OPet73tcKqGUyNPLJ0LEYTi6Sy1FnxaFKVjjVcijb0F1ZWJgTj6rsfWWhDEXBTGcx5zAWFCxLd3Y6giCQymSmckTTaeLTXNl4OiW2gUomiaWmGtNHkymxLdTksdRUe6hJUZkrWMruL9YxnwupRIJBoZzhnouuunKGq25SiZ0KTCoVOrkiX/2dJ88yIy888+TJsyAkEglqmRy1TE65duGpL/F0imA8jj8ezzpcsex+dptzvrJuWCJOKCtqxFuSjCAgAOFkcs6500tJoVQ6FdouKEAulSIrKEAqkVIgkVAgzW4lEqQSKYVSKdLs/dxjUilSFltcJJAWxFsmkyElZMhkJo9lSGfEbWba/VQmMxXmn1aQdKlzqJSFhVMdArKO9ZkO99S+6G7rsvf1CuWyyh3OkyfP+ZMXnnny5LmoKAoKUagLsaiLzuvrJ53W2Ry3ySKgSDLb1D3n0InOXWza/Whq5nMmhVoykz7LtUtlMmIxyjkaoC8XpBLJlKsrleZc35wLLJtsVl+Yc44V05rXKwsLZ6Q9TN+f3C7H/NI8efJcevLCM0+ePJc1051WzlO8nouMIEy1KJomSMXwdLagKJ0RXUZBIJMNZWdyLuSUA5nJPiedOZ/SIs52TiVSpFIJhZKsqyo903WVTrV5OiNlYNK1zYvCPHnyXC7khWeePHne9kglEtGZzbfIyZMnT56LyrI4y0aSSQovQV5Xnjx58uTJkydPnsURWYRGWxbCc+tPf4hUqXyrX0aePHny5MmTJ0+eM8jEFj4lLp/4kydPnjx58uTJk+eSsCwmF427XPnJRXny5MmTJ0+ePJchgUCAUovlyplcpJbJUMvyk0Py5MmTJ0+ePHkuN1KL0Gj5UHuePHny5MmTJ0+eS8KycDzz5MmTZ6FkBEFsFJ9MEcs2ko8mU8Ry4yHF/dykn8m+nempfp0z+nlO205OFUpnpnp2iv08hdxUoanj55fFJJWQ7dEppUAqyW2lEsnUhKTstlAqpUAq9vGUFUiRFxQiL5i8P62vZ2HBjGOKggJUMhlKWXa0qGxqtKhSVohKJsv3/syTJ89FIS888+TJ85YiCAKxVIpAPI4/FiMUTxBMxAknspOKpt1C2WOhM47nJhdlxWaeC0cmlaIoFEWosrCQIrkMjVxBkVxGkVyeu2nOuC8ek6NRZEdmKpVo5PL8TPU8efIAeeGZJ0+eJSKZTuOPxfBEo/iiMbzRKN5olEAsjj8eIxiL58RlIB4nkL0fiMdJptMX5TXJCgrEMZCy7IhIWSGK7AhIRWHhTGfwDMdw5mPi9J9Jh1Gc0Z51IM+YJJSbMiSVIDmPWe2CgOio5txTYZqrOjWvfdJ5TWUyOZd2Psd2+n48lSaaShJLZkeKJlNZ4Z7MTVtKZjIksyJ/KdAqxHntOoUCnVKRE6Xa7FanFLdGlQqTSoVBJe6r8zPc8+S5osgLzzx58sxKLJnEFYngCkey2/BZonLqFiMYj1/Qz5NKJOgUCrQKhTj/O+uaiS6aTJwRnnXTJo9pFKIDp5bJcsJyeug477ItDkEQSKTTRJPifPtJFzmaTJ7lMocTU8dmeyyYXVRMOtDBeJxgPM7oIl+TvKAAY1aEGlUqDNmtKXvMrFZjLlJjURdhKVKjVyqR5oVqnjyXLXnhmSfP24hkOo0zHMEeCuIMhWcIS3d45v3weThdEsi5Vsasa6VXKtErFVnHa2pfr1ROuV9KJUV5Z+stRyKRoMi6wYYl+p7xVIpQPIE/HptyubOutz8WJ5g97o/H8WUXNp7sgmbSqbWHwthD4QX9vEKpFLNahTkrRC1qNZYide6+taiIYk0RNo0GjVye/8zlyXOJyQvPPHmuADKCgCcSYSIUwhEK4wiFsGf37dP2PZEIiyl5kRUU5C7cFrUak1o9Iwx65k2vVCwrlzGVyRCOz3TqwvEpBy8UT+RyTaOJ5LTw9cxCpERqtmIkMTy+WAqlUuSFZxYFiWkAsoLsY9NSACbzL4sU0/MsZdPcYnnucWVh4SUXWpNC1lykXtTXCYJANJnKueqi2z7TZfdEo7jCYdzZBVMgHieVySxYqKplMoo1RRRrNNg0RRQXaSjWaCjRasTjReJxZb6dX548S0ZeeObJc5kjCAKBeJzxQJCxYDC7DTAeCDIeFG8TwRCpTGZB308mlWLVFGEtEm/THSHzNIfIolajVSiWhSMkCALBeBxvJJsGEMmKk+x9X0QUKt7IpHiJEYrHiacuTm7p5UqBREKRQo5OqciFrI1qJSb1ZAhbiVGtwjjtvkGtQl5QcMlfq0QiQS2XoZbLKNcvbIBIPJXCE43iDkdwRcLT0kQiuLP3HeEwzlCYQDxOJJlkwOtjwOub9/salEpKdVpKtVrKdFrKtDpKdVrKtFpKdVqKNZp8F4A8eRbIsphctJBO+HnyLFcygoAzHGbE52ckEGDE72c0EMgJzYlAkHAyec7vIwEsRWIIcTKUON3NsWm0FGuKMKpUyyYHLiMI+CJR0cEKhnAEs47utK09GMITiZK+gFOZrKAgW7UtzzqHsjNyTOWoZLKz2hKJ7qP0rEKkSceyQLrI4qJsIVHOVU2lSGYyJFJnt3aaPBZLpbKu7bQuANPuh+Ji1f+FoFHIsU5+prRF2CYdwTO2isLl42VEEkkc4ekRgnA2MjAzWrCQLglSiYRiTdEMQVqu11Gp11Oh11Ou0+Zd0zxXNIvRa3nhmSfPRUYQBLzRGKMBP8P+gCgw/X6GswJzxB8gsYCqbqNKRdk010Xc6ijVainRiqJyubkukURSfD98fkb8AUZ8AcYDAexBUVQ6Q2GSC3RyAYrkshnOnSHr6OWKUtTicb1SiVapyLUDki8jwXQ+ZASBSCJBKJ4klBA7Ckx3gWe4w9n9yXzLxaQLGFTKnCi1abVUGHTZm54KvQ6LpmjZLHpgykmfjCyMBWZuxwNBJoLBBX1GrUVFVEwXo7l98X9Y9ha4ynnyLBV54ZknzyUmIwg4QiEGfT4Gvb4Z2yGf/5yFOlKJhFKtNndhKtPpxJBeTlhqUC1DxySRSjEWCDLiEwX3sM/PaFZgjvj8eCLRBX0fc5F6yr3VTjlskyLHXFSESa284gXkpSYjCARicTyRCM5QGEcw6zyHQuLiIBTKHVvI4klRWECZXkeFXk/lpCA16CjXi/sGlXJZpHZMJyMIuMLhM4SpuKAUF5iBBf3/l+m0VBsMVBsMVBnFbbXRQJVen3dL81z25IVnnjwXgXQmw0QwxJBvprAc8HoZ8vnPGZIrLiqiIutwTG4n3Y8SrWbZOh7JdJphn59Bj48Bj49Bj1d8bzw+xgLBczpmOqVCfD+yQqRMr8Wm1eRu5iL1Jc0xTKRSouOXdQR9kSi+iJgTOjkBSdxmpyFl96OT+6mp+5nzmF6kKCxAKZuaIKSUFeZuufuFhShlYvsotVyGQa3CoFZiUE1uRUf3Uok4QRDwx+IzUiDEBcfUQmN8AZ8FrUJBtclAjUkUXTUmo7hvMmJQKS/J77LUCIKALxbLidDR7HbE78/ezh3xKNFqqDEac8K0OitMqwwG1PK8KM3z1pMXnnnyXACxZJJ+r48+j4cet5s+j5det5t+r3feYpQCiYRyvW7ahcEoOhYGPRU63bJ3LZLpNINeHz1ON91ONz0ucTvg8c1b2KSSFeZCreVZh2tKaOrQKS++oEhnMjiDYcb8Acb8QSb8QdzhbBFSduuJRPCEo0QSF5YPeblQKJWKqQVFaoxqFaZs0ZCxSEWxtogyvY5SvZZSvZYihfyiv55kOs141v0enUw58QVyqRbOcGTer7cUqWmwmmm0mGm0mnP7+mUqSCeZzPEe9vnPipgMeL3nbOBfotVQbzJTbzZSbzJTZzbSYDJjKVIvO/c4z/IlLzzz5FkA3miUXvdMcdnn8TLi98/ZckgmlVJp0E+Fwaa5D+U63bJ1Lc/EG4nS4XDS6XDRYXfSYXfR7XLPOWFIJSuk2jjpTolOVbXJQI3RgPkSXABD8QTj/gDj/iBjviBj2f1xf5AxfxB7IEh6Ee5jgVSCXnW2g6iWy3JOo+hCiuMkVfLCaS7l1PHFtpYSEEik0jn3NJpMEk+mcg5rzlWd5rSG4gkxHzMSw5d1aKPJxY0N1auUlGVFaKleJ+4btDlxatUUIV1kodRiiSVTDHmzrrnXm3XPfQx4vDjmaY1UotXQYrPSUmylxWahxWal2mhYVm295mIyP3zQ5z0rhWfQ68MXi835tVqFgjqTkQazmTqTkXqzmXqTiSqD/op4b/JcXuSFZ5480wjG43S73HS5XPS43XQ53XS73TjDc1/M9ErFDPegzmyi3mSiXK9bdgU885HOZBjw+OhwOHMCs8PhxB4Mzfr8IrmMeovoNDVYs86TxUSpTnvRxWU6k2HUF6DP5aHP6aXf5aHX5aHP5cEXmfsCPEmhVIpNp6HMoKNUp8GsKcq2ElJjKlJhUKswFYmuoFahuOhC62ISS6bwRaJ4Ilk3d5q764lEsAdCOWEeiJ174pSsQEq1yUCtxUSdxUSd1UidxUSt2YhGqbjov08onqDX5aHX5aY767T3ON2MBYKzPl9ZWEij1TxTkBZb0V6C13op8UWj4qLZ46HX7abX46XP7WHY758zrUFRWECdyUSTxSK6x2YzTRYL5Xrdsir8ynN5kReeed6WRBJJej1uulxuul2u7NbNeHD2ixNAuU5HvdlEnclEQ3ZbbzJhUquuuDBVOpOh1+Xh+NgEJ8btnBy30+V0zZk+UGnQZy/coovUXGyhwqC/6BenSCJJv8tDn8ub3XrodXoY9PhIzJPqoFcppty6aa5dqV507iwadd7pmYVQLM5YVoSOZ1MRFuMW23Qa6izGKVFqMVJvNVOsLboETnecjpwrLzr0XU7XnI5vhV5Hi81Ka1kJa8psrCm1XZJUj0tNPJUS02I8HvrcnmzakLidKxddLZPRYBYFaYPFnBOmJRrNFXcuzLP05IVnnisaQRAYDQRodzjpdLpodzrpcDgZ8vnmDJHbNBqaLGYaLZbs1kyD2UyR/OLntr0VCILAiD/AibEJjo/ZOTE+walxx6z9HNUyGU3FFlqKLTRn3aHmYgsaxcV3h7yRKKfHHZwec4jbcQeDHt+cz1cUFlBjNlJvNc0QOpUmA5qLkKcoZKu6Hf4Q7nCEcCzbIzOeJBSPE4knxelGZ9xC8QSReGLBTf0nkSBBNTmFSDG5FW8ahRx1djv1uAK9SoFVp8GiVV+UVI9UOoM9GKLf5c26zeJioN/lwRmaOy9To5CzorSYVaXFrCorZmVpMTVm40UX/+lMhiGvf5qL76TD4WJ8Dne01mRkTZmN1rISWstKWGGzLqt+pIshIwgM+/x0u7MRoGwkqNfjnTONRqdQ0Gy1sKLYSovVyopiK41m87LPWc+ztOSFZ54rhmgySZfLlROZHU4nHU4Xwfjs4UGzWp0Tlk0WC41mcf9KdDWm4wlHOD5uF93MMTvHxyfwztKqqEguY3WpeJFdXWpjhc1KldFwSUJszmA4Jy5Pjzs4NWZnzD+7GDAVqcRQrsVIvcWcC+2W6rVLJlwSqTTOYAi7P4QjEMIRCOMIhLAHxPt2fwhnMExskfmSbxUSCZiK1Nh0Gor1GnGrE7dWXVHuuG4Jq90D0Rj9bi+9Tk/Ope5zeRjy+GZ1SVWyQlpKrKwqs7EyK0rrrKZLkhvti8bodDg5NeEQ/0fGJhj2+c96XqFUSnOxhTWlU2K03mK6ot3yVCbDoNc3FSnKCtMBj3fWwQxSiYRao1FMY7BaWVFsodlqzbujb2PywjPPssQbjXLa7uCU3cEph4N2h4MBr2/WXCWZVEqD2UxLsVVcjVvFE+Bi50EvRwRBYMjr59DwKAeHRzk0PEa/x3vW82RSKc3ZsGJr9iJaewkcJxBz8o4Oj3NkeIxTY3ZOjTtwBmfPqa02GXKO2MrSYlaUFGMsUi3J60hnMoz5AvQ7vPQ5PfQ7PfQ7vQy4vLjncevORK9SYtGq0SgUotOolE/NRp+2r8nuqxXi5CNZ4eIElSAIRBJJIlnXdNJdDcfiU3PkY6KbGk6I+/5oDGcgvGB3VSUrpNykp9ZqpNZqos5qoq7YRI3FuGTV7YlUmn6Xh1PTFhrt445ZQ+DywgKabRZWldpYU1HCxqpyqkz6SyJgPJEoJ3MLtgmOj9txz1Jdr1HIWVdexsbKMjZUltNaVoJSdmW6otOJp1L0ebx0OJy0O510OsXtXP13DUolLcVWVhYXs8pWzGqbjZorpNArz/zkhWeeyx5XOCwKTLuDk3Y7p+wORgOBWZ9rVqtpsVppsYoFAiusVurMprdkfvRbQSqTocPu5FBWZB4aHp219Uy9xZRzadaUlrDCZrlkDdUdwRCHh8Y4NDjKoaExOiacZy0YpBIJdRZjVmDaWFlWzIqSpSn4iCSSDLq89DmmxGWf08OAyztvXqisoACbrgirbppDqJ90CsXjxTrNZS8yMhkBTzgyp3M7edwfnb8Iq0SvodZqyt6MOVFqXYJ8zXQmw4Dbm3W7pwRpKH52uyCLRs36qjI2VpWzvrqcFpuVwoKLL14EQWA8EMzlQR8fm+DkuJ3wGS22ZFIpq0ttbKgsY2NlOesqyjCql2axdLkjZNs/TaY4dTidtDtc9Hs8s7qjapmMFcXWnBBdZSum3my+ooo08+SFZ57LDHsoxIkJO6fsdk7aHZy227HP0R6l2mBgla2YlbZiVhYXs8JqxaopusSv+K0lkkhybGw8JzKPjo6ffeErKKC1zMaGynI2VJSxvqLskvUzFASBfrc3JzIPD40y5Dk7ZFlh1LG+sozWilJWlhbTUmJdkmbXkUSS9lE7J0bsnByZ4OSIneFZfv4k8sICaizGnMM3KarKDDoM6uU3KedCiCVT2P0hhty+rED3ZJ3g+R1gjULOirJiVleKruSaihJKDRfeySCTERj2+jk9bufUmIMjw2McH7WflW+olstYW1nKhqpyNlSJn6lL1Tg9ncnQ6XBxaHgsG2EYnbW9U4PFxIbKcjZWlrOhsowKg/6SvL7LhXgqRbfbTbvDmYtctTtnd7kVhQW0WK2sKi5mVYkoRpstliumHd3bkbzwzPOWEYjFODFh59jEBMfHJzgxMTGryJQAdSYTq2zFrMquglcWW6/4XMzZSKbTHBubYE//EHsHhzk6Mn7W7GedUsH6ijI2ZEN9a0ptl6wAQhAEepxudvcOcWBghENDY2flj0ok0GKzsr6qnA3VZWyoKsem01zwz06m0/TY3ZwYFgXmiZEJeuzuWdMvjEWqGeHjWquRumITZQbdkoT6IvEkgWiMYDROIBojEIlPuz91LPd4NE4qvbjiIoAipRydSoFOpUSnVqBVKtCpleIxtdhPVK+efFyJRilfkt/PF4kxMM0t7nd66HN4GPH6Z83XNGvUrC63sbqyhDUVNlZXlCxJikQ8meLkmJ1DQ6McGhzj8PAYwTNaPhVKpawsLWZ9VRnb6qrYVFNxyYToZOHewSFRhB4aGaPX5TnreRV6HVtrqtheW8nW6kosb7MFNIiivc/j5VQ2qnXSbue0wznrCFF5QQEri4vFtKCSElpLS6gxGvMtnpYJeeGZ55IQSyY57XByYmKCY+MTnJiw0+89O9dQKpHQYDax2mZjdYmNVcXFtBRbr9iK8nOREQQ67E72DAyzZ2CIg0OjZ1Wbl+q0OedkQ2U5jVbzJT0BO4Nh9vQN8WbvIHv6hnCckZ+pKCxgTXkJG6vL2VBVztrK0iUJmY96/RwZGMu5me1jjlnbPRXrilhTUcLqihLWVNpoKb3wvNB4MsWYN8CoJ8CI28+ox5/dBhjx+AlGz93v8q2gUCqlxKilwqSn3KyjwqTP7otbQ9GFubqJVIp+p5dTo3ZR/A9P0DXhmjWntMKoY02l+HdprSxhdYXtgtM9MhmBbqc767CPcmhwlInAzD6zsgIpayvL2FFfzY76KlaWFl/SvEJPJMqRkbFczvXJcftZ70+T1cy2miq21VSyqariiuspulAygsCQz5dNtbJzckIUpIFZCka1CgWtJTbWlJTQVlrCmhIbJVrtW/Cq85yLvPDMs+SkMxl63Z6ck3lsfIIu1+wXn0q9ntbSkuyq1caqYtvbep6wIAgMen3szQrNvYMjZzmGRrWKrdWVbKupZHttFZWGS1NcMUk0keTg0Ci7e0Wx2WV3zXhcUVjAxuoKttVVsqG6nFWlxRcsKARBYNQb4EDfCAf6RzjQN8KY7+w8X61SweoKW1Zois6aTX9+bmoylabX7qZ73MXwNHE54gngDIQ419mwQCoRnUbVlAupneZO6lTK7H1xX77I4qKMIBCOJwhERMc0EIkRiMVz7urksUmXNbqA8Z5qhUwUotOEaW2xiZZyKybN+RXjxZIpOsedWSd6ghMjdgZcZy86lbJC1laVsrG2gs11FaypLFmSvOMxX4BDQ6PsHxhhd+8Qo2d8bvQqBVtrq9heX8WO+moqjJc27B1OJDg4NJpbXLbbnTMeL5BIWF1qE4VobSXrK8qu2BZOC0EQBAZ9vqyBIV5fTjtmX3TaNEWsyTqibSWiGH07RsouN/LCM88F445EODQyyuGxMY6NT3DKPnsPSJNaRVv2JNBaUsKakhJMb5Mk+/nwR2O83jfAG32D7B0YPmvCilomY1NVBdtqK9lWU0VzseWSh5T6XV5e6ujhjZ5BDg2NnZVXt7K0mB31VWyvr2Z9ZRmKJSiwcQXDvNE1wL7eYQ70jzDum/m+FEqlrCwvprWyJOdoVpkN5zVFKJpI0jHq4OSwnY5RB51jLnrt7nnD3yq5jAqznnKTjoqsYzh5v9SgQ62QXVY5oclUGncwwohHFNFnurWOwNzTuUB0jpvKrLSUW1lVIYbNbYbza4kTiMb+f/bOOjyKs4viv7gRd3cPxEjQQAIEd6eUOqVO3b2l7k69tEWKuwcIkEA8RIi7u+vuzvfHhkBKNkawfjnPw5N2d2Z1dua85557bhdVNDa3iKrGrgssFUUFPK3M8LOzYLyTDR7mxtc8IUoQBPKqajmbmUtYZi7nsvOvaliy1tNhrL0VE51sGWtndcOa7i6hqqmZ87n5hGfnEZ6TT251TZf7VRQV8LEwY7SNFUEOtjgbGdxSx9nNQLtYTFpFZScRvVBcQnpl9zYbW11dKRE1NcHPwgJnwxt/Pv1/xxDxHEK/UVRXR0R+AVEFhUQWFpJZebVnSUNJCXcTY0aYGDPC1BRPExPMbsCoxNsFuVU1hKRnEZKeSVReYZcOTyV5ebwsTDtLbSPMTG64kV4QBNLKKjmSnM6R5HTSyyq73G+qrclYOynRHGNnid4gRFOJJRKSCkoJTc0mNDWHpMLSLvcrysvjYWmMn60FfraWeFmbDijSRySWkFlaSWJeCYn5pSTmlZBeUtGtN1FTTQUnUwNsDHU7SOVloqmr8d+aWNXaLqKwqu4q20BGcQW5FTXd7mOgqY6HlQnDrUzwsDTB3dIYbfX+K0qCIJBZVkVUh5odmV1wVQOTnoYa451smOBiy1hH60FpkBOJJSQUlXA2I5ewrDziC4q7HAcaKsoEOdkx1c2BAAcb1G5CNaaotq5TDQ3PzrsqpcJMS5NJTvZMcrTD38r8hhPlWxVNbe0klZV2WrsuFJeQX3t1Y6GWigq+5mb4WVgw0sIcDxPj/5sUlJuFIeI5hB4hCAI51TVEFBR0ks3uooycDPQZaW6Op5kpw02Msdf7b4co9xdiiYS4wuIOspl1VYOBg4EegQ62jLGxwtfS/KbYDQRBILm4jMPJ6RxJziCn8nI5VFFenlG2lgQ62TLO3hpbA91BIV1tIjHnM/M5mpTOieTMq1Qvd3Mjxjna4GdngZe12YA+F7FEQkphGWGpeYSn5ZKYX0Jz29Xds5dIlJu5Ec7mRriYGWKqe+2LJUEQaGxpo7qhueNfE9WNLdK/Dc1U1zdT09jcY5RTd5CTAy01VXSHqaE7TA2djr96w9Q7b9PWUBuUaKHGljbSiytILS7nYmEZSXmlMsm6tYEO/o6WjHGyxt/BcsBENLu8msjsfMIz8ghPz+uiTMrLyeFrY06whyNT3B0GbKf4NxpaWonIKeB0Rg7HUzK7+JXVlBSZ4GjLNHdHJjjaXpfpV71BEAQyK6oIz8nnTFYOYTl5XUrMGsrKBNhZM8nJjon2tv83sU19RVVTMwmlUkU0prCImMIiGv9VnVNVVMTLzBQ/C3P8LS3wMjVFbWjy0qBiiHgOoQskgkBGRWUn0YwsKKS8sWsJTkFODg8TY0aam+NnaYGvuRm6akMnuH+jobWNs9m5HE/L5FRmThevpqK8PCOtzJnsaEeQox1Wujo35TVKJAIXCks4kpzO4eT0Lv43ZUUFxttbM9XNkSBnu0GLYGptFxGWkcuRhHROXsyi7oou5GEqyoxzsibA2ZbxTjYYag6su7e4uo7wtDzCUnM5l55HbVPXTEoNFWXcLY2lkT9WJnhYSb2gAyGZgiBQVtNAVmkVWSWVZJVUUVBR20kyaxpbaO8nqRxMaKmroNtBRg21NbA11sPORB87Ez2sjXQHbItobmsntVDq3UzIKyExr4T8yq6KkrycHB6WxoxxtmaskzXDrQem3reLxcTmFhGams3p1BwySrsq8F7WpgS7OzLVwxEz3cE5/0skAvGFxRxOSufIxYwunmJlRQUCHGyY6uZAkJMdWjconuzfaG5vJzw7j+PpWZxMz+qihsrLyeFtYcpkR3uCHO2w0x+cxeJ/CSKJhJSyciIKOip4BYVUN3dd/CrJyzOioyzvb2mBj7kZw/5Pm10HC0PE8/8cYomElPKKDpIp/Vf9r+BoZQUFPE1N8Le0wM/CAm8z0//bLvPeUNvcwvH0TA5fTOdMdl4XL6SWqgoT7W0JcrRjgr31TTO5iyUSYvOLOJyUztGLGV26flWVFJngaMM0NycmOg2eqtPc1s7ptByOJqZzKiWbxivUK/1h6kxxdyDYw4GRthYDIiaNLW1EZuZ3ks2c8q7NK8NUlfF3kKpwfvYW2Brp9dsvKJEIFFfVkVlSSXZpFVkll4lmY8vVkS//hqqyYicB1B2mhq6G2uX/1lTvNwGUSCTUNrVQ03CFenqFqlrb1NJrA5S8nBwWBtrYmVwmo3Ym+tga66Gm0n+Vp6axmfjcYsLTcglPyyOrtKuyr6GijJ+DBWOcrBnrbI21gc6AyFBhdS1HEzM4mpROXG5xl/vczY2ZOtyRYHdHrA10+v3Y3UEQBBKLSjmSnMGR5HRyq2o671NSkGeMnRXT3ByZ5GJ/01RGiSCQUFTKiY6qSkpZ1yYlW31dprs4Ms3FEVdjwyES2g0EQSCzqorI/EIiCwo4n19AaUPXVAQFOTncjY06iehIC3O0hxqW+oUh4vl/BrFEwsXycs7l5nM+P5+owqKrZpmrKiriY2aGv6X0h+VpavJ/3UXZG6oamzialsnhlHTO5eR36d631tVhkpMdkxzt8LEwu2mhx4IgkFJSzq74ixxISKH8Cv+curISQc52THVzJMDBZtDK/I2tbZxKyeZIYhpnUnO6hEMbaw0j2MOBYA9HvK3N+m3LEASBtOIKTiZlEZaWy4Wc4i6fu4K8HB6WJoztUNo8rEz6VXIWBIHs0iqiMwqIzyqWks2SKpmz2BXk5bAy1JWqiaZ6WBnqoK+p0aUEfqP9gWKJhNrGli5ktKS6vgth7in2yVRPCzsTPdwsjfFxMMfL1qzfZLSkur6ThIan5VLzL+XZTFeLMU5WjHOxYbzLwDyUpbUNHEvK4GhiOtE5hV0aSpxNDQl2d2DqcEfsjfT7/djdQRAE0korOiwp6WSUXybXCvJyjLWzZp6XK5Od7W+KJ/QSCmvrOJGexYn0LM7l5HfJ+7XU0WaaiwPTXJwYYWY8REJlQBAE8mpqieyoAEYUFFBQ29VqJge4GBrib2nBaCtLRllaDHXO94Ih4vkfhyAIZFRWEZ6Xx7k8Kdms/VfAsoayMiPNzfCztMDfwmLIXN0HlNY3cDQ1gyMpGUTkFXS92BkZMNXZgemujjgY6N/Uk3ppXQP7ElLYHX+xS+yRlqoKk1zsmermwDg760HpQgdpSfR0ag67Y5IJTc3u4ls019Ui2ENaDh1uYTKgDuXMkkoOxaVyOD6d7LKuapqlvjZjna2lqqaDRb/KnxKJQHpRBTGZBURnFBCdUUh1w9UzppUUFbAx0r2sDJroSUvWhrr9nrV+syEIApX1TWQVS0nolUpuVf3Vk4kU5eVxtTLC18ECXwcLvOzM0FTre76kRCKQUlQmtT+k5RGT3TUdQU1ZiUA3O6Z5OTHexWZAx2RFfSMhyZkcTUznfFZ+Fw+qvZEec7xdmevtNmieUIDM8spOJfRiyWWVUUNFmelujszzcmOklfk1d+RfCxpaWzmRns3hlHRCM3NoEV1eQJlqaTLNxYGpLo74WJgNdXj3gkvNtREFBUTmF16VRy3foYiOsbJktJUVI81vjmf/VsYQ8fwPIr+mtpNohuflX+XR1FBWxt/CnFEdqzNXI6OhWbh9QFFtHUdSMzh0MZ3YgiKu/DG4mxgxraOMZauve9NeI0jL2sdSMtkdn0xYZl4nKVZSUGCyix1zPV0Zb2/T79zInpBRWsmu6CT2xF7s0o1sra8jLXt6OOJmZjQgEp5bXs2huDQOxaWSUXLZ26esqMA4ZxvGu9owxskKS32dPj+mSCwhtbCM6PRCYjILiMkspK6p64JMRUmBETZm+DiY42JhhJ2JHub62tfcrCMIArWNLVTWNVJR20hFbROVdY20tffTByoHOsPUMNDSwEBbA31tdfQ11VEehEVEdUMz2SWVZJZUEp9VTHRGAcXVXeOs5OXkcLYwxNfBAh97c3wczNHpRzB/U2s70VkFhKXlciIxk8Kqy0rSMFVlgtztme7lxBgn6wGR+prGZk5czOJIYjphGbmd0VjycnKMc7RmwUh3glztBrULPKeymj3xF9lz4SIF1Zffj5mOFvNGuDLX0xVbg5t7fmhqayc0M5tDKemcysjuMmLXaJgGU5wdmO7iyEgr86HrQh9Q1tBAZEEh5/PyOZefT1ZVVyKqJC+Pp6kpo60sGWttNVRBZIh4/idQ3tBIWF5eJ9n8dylARVEBXzNzaei4lSXDTUyGTih9REVDIwcuprEvKZW4wq5eMi9z006lwPImz1qWSAQicwvYHX+RQ0lpNF1xMfGxMmOepyvT3Z0GdUZ7fUsrB+NT2RGVREJBSeft+sPUO5QlV5xMBpYxWFBZy+H4NA7HpXGxsKzzdkUFecY5WzPNy5kgdzuG9WOiS0FFDcfjM4hIzScuu+gqX6a6ihJedmb42Fvg62iOu6Vxv0mcWCIht7Sa3NLqDlLZSGVdU8ffRiprm6ioaxzQeMy+QltDFX0tdfQ7COmlf/pa6pjoaeJoYdgvpfISiqrqiE7vUIQzC8kvr7lqGwdTfXwdLJg43A4/J8s+W0sEQSAxv1SqZselUVp72VenqabCZA8Hpns54e/Y98e8EvUtrRxLzGBndBLROYWdt2urqTLby4UFI91xNTPq9+PKgiAIxOQVsSs+mYOJaV068j0tTJjn6cYMD6eb3nXe0i7iTFYuh1PTCUnL6mK70lNXY7qrE3PcnfGxMBsqx/cRJfX1naJPeF7eVbnMqoqK+JqbM8bakrFWVrgb39jJWbcChojnbYiW9nYiCwo5m5vLmZxcUsq7To5RlJfH09SkU+r3NjP9v19h9QcNra0cTc1kX1IKYdl5nRmbcsBIK3OmuTgy1dkBE62bP44tu6KaXXHJ7L1wkaLayyc4S11t5nq6Ms/TFSs9nUF7PolEICIrn53RSRxNTO+MclGUl2eCiy0LfN0JcLYZEDkoqa7ncLxU2UzMv5zhqSAvx2hHK6Z5OTPJw75f8Tw5pVUcjUvneFwGKQVlXe4bpqaCj71ZZ+nYxcKoX2pmS5uIjMIKUvLLSMsvJyW/jIyCCpk+0H/j3wRRVbmfzUWCQHV9c6dyWlnb1O10sO5gYaCNk6UhLpZGOFsZ4mxhhKGORr/IRWlNA7GZhR3WhAKySrpaH7TUVZjoYU+wtyOjna36TOIlEoH43CIOxaVxJD6NiivK/jrqqkwZ4ch0LydG2lsM6IKdW1HNruhkdsckU3pFY52LqSELfN2Z7eXSL+W2N7S0iziRmsmu+IucycjpLP8rKcgT6GTHPE9XJjjaDmoFYiBoE4sJz87jcEo6x9IyqbmiydRCW4tZ7s7M9XDB0dDgJr7K2wuXPKJXViArm7raWHRUVRljbcV4G2vGW1tjrv3f5i8wRDxvC0gEgdTyCs7k5HAmJ5fIgkLa/j05xsiIcdZWjLa2ZKS5+VDXeT/RJhYTmpHD3qQUQtIzu2TjjTAzYa6HC9NdHDHSHDxv2EDR3NbO4eR0tsUkEpV7Wb3RVFVhhrsT871c8bYcXIWisLqWXdHJ7IpO7hIr42Cs33mxNhhA9FGbSERIQibbzydyPiOvswtbXk4OP3sLpnk5MWW4I7rD+kYEBEEgo7iSY3HpHItLJ7P4cmleQV6OkQ4WTBhuh6+DBY5mBn0mLrWNLaTml5GaX05KXhmp+WXklFR3OxlFTUUJO1M9DLWHdSqN+toa16UkfiUkEoG6pqtL+FeqrvnlNZRU1Xe7v66mGs6WRrhYGuJsaYSzpSFWRrp99iZW1TcRk1nIuZRcTlzIpPIKwqihqswEDzumeDow1q3vTURiiYSYrEIOxaVx9EI61VfkvBppD2O+nzsL/N2x0O9/xUEskRCWnseu6CSOJ2d2+k0VFeSZ5GrPAl93xjpaD0oO6iVUNDSyPyGV3fEXSS6+vBDS11Bnvpcri3w8sDPQG7TnGyhEEgnh2XnsTUrhaGpGl3K8s5EBcz1cmOXmjNn/AUkaTAiCQHplJefy8gnLlZLRhraulRdbXV3G2Vgx3saGUZYWaKr0vzpxq2OIeN6iKG1o4GyOVNE8m5t31SrJRHMY462tGW9jzRhrK/TVr31yzP8bJIJAVF4hexIvcjglvUvTla2+LnPdXZjt7oL1ICqG14KkolK2xSSy90JKZ+lOXk6OAEcbFni5EeRkN2hNQiBtFApJzmRrRALhGXmdt2uqqjDT05kFvu54WAysIza9uIId5xPZG32xS8amj5050z2dCB7hiIFW34isIAhczC/jWFw6x+MzyC27IvheQZ5RzlZM8XIkcLh9nwlsTUMz5y/mEZ6cS2RqPsWVVw9NgO7JmqWRzi1dOruSRF/6m11cJZNEe9iYMMbdmjFu1jiaG/aJiIolEuKyijq/k7KaKyK7lBUZ72ZLsJcj491t0VDt2yJZJJYQkZHP4bg0jiWkU3dFR/4oR0sWjRrOJA/7Af0Gappa2B+Xwq7oJJKLLhNCIy0NFo70YJGfB2Y6g3tdSSutYE/8RXbHJ3dJmRhpbc5iHw+muTne1K74S2hpFxGSnsnepFRCM7K7dMf7WZkzx92FaS6ON902cDtCJJFwobiEMx3X+vji4i5T7BTl5fEyNWW8jTXjbKz+Mza5W4J45uTk8M477xASEkJJSQlmZmbceeedvPLKKyj3Ubm73YlnY1sbEfkFnM3N42xuLukVXQOS1ZWUGGVp0XEAWmOvpzfkuRkABEHgYmk5+5JS2JeUSkn95Qui0TANaTnJ3QU3k4E1wgw26ppb2JeQyraYxC4KiYWuFot9hrPAyw1jrcFVYQuqatkWmcCOqKTORiE5ORhtb8V8X3emuDugOoCLe2NLG4fiUtlxPpELeZc9oZeUq/n+bn1uEBIEgeT8Ug7HpHEsNp2iqq7h3mNdrZni5cgEDzu0+lCabxeJuZBVzLnkXMKTc7mYV3pVBqaFgXZnWdrZSlqmNtDuX3n63xBLJFTXNVFe00hrN9OUeoKcHOhqqmOkOwzVAWRuXolLtoFOVVeGbUBfS51RrlaMdrNmtKs1Btq9Lw4kEoGE3GKOx2VwNC6d4m6+q2BvJ4JG2KPex9zYS0r5johEwtMuL4q01VWZ4+vKglEeOJkOrCScUlzOzqgk9sVd7Ix/kpeTI8DJhiWjhjPB2XZQFxYisYTQ9Gy2xiRyKi27cwEwTEWZOSNcWOzjgbuZ8aA937WgtrmFQynp7EtKISK3oLPBUklengB7G2a7OzPJ0X6oi3uAqG9t5VxeficRza2p6XK/looKY6wsGWstJaLWOgPLwb3ZuCWI56FDh9iyZQsrVqzAwcGBxMREVq9ezapVq/jkk0/69Bi3I/EsrK3jWEYGxzIyiSoo7LKSlAOGmxgz3saGcTZWeJuZDUUcXQNqmlvYeSGZLbEJZF0xW15TRYVpLo7M9XDBz8r8llGq8qpq+PVsNLvikzsv/koKCgS7OrDE14NRNpaDHs9yIa+Y9ScjOJWS1Um6DDTVWegrVXws9AbWQFVUVccfp6LZGZFEc0fJTlFenonudiwc5cE4Z+s+f+7Nbe3sj7zI5lNxZFxRRldVUmS8uw1TvJwI6KOKVt/cypmEbE7EZhCWlENTa9fReQ7mBoxxs2a0mzUetib9bsgRBIG6xhZKK+spqarv+FtHaWU9pVX1lFTWU1HT0O3Yyf5CU10FYz1NjPU1MdHXwkRPs8v/G+pooNhPD6FYIiGnpIrI1ALCk3KISiug+V+fkYulEYFe9kzydsDerPfosCvV6aNx6V0alNSUlZjt78qKiV7YmfQ9c7OgspZdkUnsikjq0pTkaW3KvUEjCXK3H9BvpU0k4nhyJv+cTyAiK7/zdlMdTe4N8GWR3/ABLcB6QmldAztjk9gWm9ilK97DzJgHxo8k2NXhljlHldTVsz85lb2JqSSXXl4UqykpMsPViRU+nkMZodeI/Jrazl6OsNw86v6VuW2prc0kezumONgz0sL8puVE9xe3BPHsDh9//DHff/89WVlZ3d7f2tpK6xVfQl1dHZaWlrc08RQEgeSycinZTM/kYnnXyRKW2tqMs7ZirI01Y6wsh8ZQXiMEQeBCUQkbYy5wIDm107epoqhAkIMds91dCHSwGdQ4lWvFxeIyfjoTxaGktE7lw9FInyU+HszxdB30cpYgCERlF7L+xPku5fSxDlYsGTWCIFe7AZ/M0oor+O1EJAdjUzvJlY2hLgtHeTBnpGu/PKGl1fVsOR3P9rMJnaV5FSUFJnrYM9XHiXGuNn0KN6+qa+JEXAYn4jKJSMnr0l2uq6nGaFdpSXmUqxWGOn1XkkViCen55VxILyQ+vYiM/ApKKuto6YOKKS8nh762Omp9LDlfglgsobK2sc/PYaCjgbmRNsPtzfB0NGO4oxk6fbQewGVVOCwph3PJuaTkl3VRhS2NdAjysifIy4ERdqZ9IqHpRRUci0vnYHRqFxI6ztWGOwK9GeNi3WfSKJZICEvNZWdEIicSszobrWyN9LgvaCSzfFwGnLWaU17N1sgEdkYlUdvRdKM/TJ17AnxZNmoEGoM8t10iETifk8/W6ASOXrzsP7XW1+GBcSOZ5+l6S523Mioq2ZuYwv7kVPKqL49NdTM2YoXvCGa7Ow/1HVwjxBIJiaWlnM3J40xuLrGFRV3EKm1VFSba2hLs6ECArc0tPdbzliWer776KocOHSIqKqrb+998803eeuutq26/1Yhnu1hMRH4BxzIyOZ6Z2SVaQV5ODl9zMyY72DPFwR4b3Zub7/ZfQWNbG3sTU9gUc4GLpZfJvYuRIXd0nASH3WKG7ajcAtaHRnI6I6fztgmONjww3g8/a/NBVw0EQeBsei7rT5wnJqcIkKqQc7xduX/iSGwNB97gkJRfwvdHznEqObvzttGOVtw3aSSjHa369V4Scor5+0QsR+PSOsmrub4WKyZ6M2+0e59USIlEICIlj+2nL3AqLqtL57etqR5BXg4EednjamXcZ5LT0NRKQmZxJ9FMyiq5Sg28BF1NNYz1tTDRl6qQJnqaGHX8t7GeJvraGgNuYBEEgYamVsqqGyirkiqrJR2KamllnfRvVYPMWfE2pnqMcJQS0RGO5lgZ9710V13fROiFbE7EZXAuObfLsABrY10WBgxn9hi3PvlqBUEgKqOAjSdjOZmQ2UlobY31uCPQi9l+bv2amlRR18jfZ2LZfDaeho7YLGPtYdw3yY9FozwG7IVubRexOyaZn09FUtihSGqrqbJqnDcrx3pdl5ntVY1N/HU+jr8j4qjt8LUaampw7xhflo0cPuik91ogCAKxhcVsirnAweS0ziZYDWVl5g135Q6fETgZDXXFDwYa29oIy83jWEYmIZlZXWbMKykoMNbKkikODkx2sMNo2M1vir0StyTxzMjIwNfXl08++YTVq1d3u82trHg2tbVzOieHI+kZnMjM6iKPqykpMt7amimODgTZ2aE3ZMgeNKSWVbApOp7diSk0dnQKqigqMNPNmRU+I/A0M7mlyj6CIHAqPZsfT0cSkyclf/JycszwcGL1eD9cTAyvy3OeuJjFDyHnSSqURhYpKSiwcKQ7908cibnuwPNIk/JL+eHIOU4mS6sUcnIQPMKR+4L8cLfsu0etXSzmeFwGG0/GciHncnaqr4MFKwO9mTjcrk/lxqq6JvaEJbHjdAIFFVeoMNbGTPZxIMjLARuT3gm2IAgUVdR1kswL6UVkFlZc5QEdpqbCCEdTRjiY425ngqmBFkZ6w1C9yX43iUSgqq6Jsqp6MgsrOt9DTnHVVdvqaqox3OESETXD1aZvWaZNLW2cTcrhRGwGoReyOm0LSooKTPFxZGHAcHwc+7aAKqioYXNoPDvDEzuzVrXUVVg4djjLAjwx1ev7+b2hpZWt4Qn8GRpDeZ10kIaR9jBWT/Zn4Sj3AauG7WIx++NS+OlkJDkV0mY2DRVl7hjjyV3jfNAbNvjNno2tbWyNTuT38GhKOmKgtNVUuHOUN3eO8rrlmnuqm5rZcSGZzTEXyK2u6bzdx8KMO3xHMM3FcSjmb5AglkiIKSriWHomxzIyr/KGepmaMtXRgalODreEwHVdieeLL77Ihx9+2OM2Fy9exMXFpfP/CwsLmThxIoGBgfz88899fq6b7fGsaW4mJDOLI+kZnMnJ7TKSTF9dvdOHMc7aClWlIeP1YKFNJOLgxXQ2xcQTU3CZpNjq6bLcZwQLRrihcx1UiGuBWCLhUFI6P52JJKVjxJ6SggILvd24f9zIQc3dvARBEDiTlsNXR8I6u3bVlBRZOmoE9wT4YnQNDUoXC8r47kg4J5OkhFNeTo5ZPi48GDwKG8O+n+RqGpvZcTaBzafjO7uglRQVmOHrzB2B3rhY9B7wLZEIRKXlsz00gRNxGZ2l9GFqKswa5crCCcNxNO9dcZFIBBIyizhyLpVTMRmUVTdctY25oXaHWmiOp6MZtmb6N3UsYn9R09BMQnoR8elFxGcUcTGrpItqCdLP38fZguBRzgT6OqCl0ftvqbGljUMRKWw/nUBK3mXvn62pHgvHS1VQ7T48TkNzK3vOJ7PpVCz5HQsHBXk5Jns6sjLImxE2vZfzL6FNJGJnRBI/HYvo9IEaaw9j9RR/FvgPnICKJRKOJKbz44kI0kqkecrqykqsGufDPQE+10UBbROJ2XvhIj+diSKnsrrzOZf6Dufesb6D3mx4rZAIAudy8tkUE8+x1MzOrm0dNVUWebqzwmcEVro6N/dF/odwaUz2sYxMjmVkEF9c0uV+RwN9pjo6EOzogLvRzWmiva7Es7y8nMrKyh63sbOz6+xcLyoqIjAwkNGjR/P7778j3w8T9c0gnsX19RxNz+BoegYR+QVdYhAstbUJdnRgqqMD3mamt4wh/L+CsvoGNsVcYHNsApWN0s5rRXl5pjjZs8J3BKOtLW8pdROkF79dcRf55WwUuVU1gPSCscJvBHeP8bluGaERWfl8dSSM2NyizudcOdbrmpWZ1KJyvjscTkhiJiAlnDN9nHlwyihsjfpeqq9uaOb3Y5FsCY3vbKTS01Rn6fgRLBk/Av0+xCpVNzSzNyyJ7acTyC+r6bzdw9aERQEjmDrSqddSrSAIpOaWceR8KsciUimpvGyLUVSQx8XaiBGO5oxwMGWEozkGOv3PLQWpV7KkvI6i8lqKymspLquluLxOZqleFuTl5TDQGYaZkRZmRjqYGWpjaqSFlobqgI79tnYRKbllUjKaIVVFq+oux/woKsgzdoQtwaOcCfCyQ70PntTknBK2n07gUGRq5/tTUVJgiq8TiwKG42nfe96sWCLhTFI2f5+MJSLtcpOPl50Zj84ai5+TZd/fo0jE9nOJ/BwSSVkHATXR0ZQSUD/3AXtAJRKBkylZ/BByjqSOSVtaqircO2EkK8d6XZdyuFgi4ejFDH48HdmZeKGkoMB8L1ceGOeHdT9GyN4olNY3sC0ukS2xCZ2JInLAJEc7Vvl5M8bm1jtv3+4obWjgWHomR9IzOJ+f38VqZK6lRbCjPcGODow0v3HNtbdMqb2wsJCgoCB8fX3566+/UOhnQ8ONIp6ZlVUcSc/gaHo6F0pKu9znYmjQQTYdcTEc2KjAIfSM+MIS/oyK5WByWqex2lhzGCt8RrDY0/2WCHj/Nxpb2/gnOoHfwqIpq5eW+7TVVLlrtDcr/b3Q6ccknv4gPq+Yr46c5Vym9GKtqqTIijGe3D/BD91rmMySWlTOD0fOcSwhA5CW1Gd4ubAmeBR2xn0nnPXNrfwZEs3fJ2M7S6rOFobcGejDNB+nXku8giAQnVbA9tMJhMRmdPoYNVSVmTnKhUUBI3Cy7N2ukF1UydHzqRw5n0peyeUMUA1VZSb6OhDs74Svq2WfS+aCIFBe3UBxWS1F5XUUldV0/K2luLyWsqr6q8r0gwkNNWXMjLSl/wy1MTW84r+NtPr1PvJKqgmJSufI+RQyC65IEVBWJMDLnuBRzowZboNKL1OXGppbORiRwvbQBNIKLvuuHcz0WRgwnFmjXdHsw+8grbCcjSdjORCV0qnOjnKy5NHZ4xhha9qn9wVSr+b284n8cjyCso4SvJmuFqun+DNvpNuACaggCIQkZ/L10TDSS6Wfl56GGg8E+rNs1IhB74K/9JxnMnJZfzqic6CEvJwc092deDDg+lh2rhUiiYTQjGz+jo7ndFZu5+2OhvqsGunFvOGuqA1VBgcdtS0tnMjM4mh6BqE5OTRfEZemq6bGZAc7pjo6Ms7a6rraIG4J4llYWEhgYCDW1tb88ccfXUiniYlJnx7jehLPtIoKDqamcSgtvUu+phzgY25GsKMDwQ4OWA+VC64L2sViDqeksyEyrsu8dB8LU1b5eTPV2eGWjJGoaWrhr/Ox/Hk+rrMT1lhrGPeO8WWJr8d1awq4WFTG10fDOJUibe5RVJBnid9w1gT5Y3gNZbi04gp+OBLO0QuXCed0T2cemjoKO+O+x980t7az8VQsfxyPoq5J6n92sTDi0dljGe9m06du6JPxmfy47xyp+ZdJjJu1MYsmDGfaSOde1bjC8lqOnU/l8PkUMvIvj5xVUVJgvJc9U0c5M2aETZ9JWlFZLVFJuUQl5ROdlEdVbVOP26soK2J2iRAaScnhMPX+HQ8isYSyynqKyjqU0/I6Kmsae9xHUUEedwdTfN0tGelujbuDSZ8nKGUWVHQQ9BQKyi57ZjXUlAnydSR4lDN+blY9NkoJgkBiTgnbQxM4EpnaqXCrqSixdKInd031RVezdxW+rLaBXw5HsD0sodNOMcHDlkdmje2TJeMSWttFbDuXwM/HIzpHc5rrabF6sj9z/dwGfF4RSyQcupDGt8fCya2sAaRh9A9NGsUCX4/rNh4zJq+Q9acjOZV2ubFvopMtawL88LEyvy7Pea3IrKjir6g4dl5Ipqldqoprq6qw1Hs4d/h6/l+MkLwZaG5v50xOLkfTMwjJzKKm5fIgDw1lZSbb2zHD2YkAG+tBtwfeEsTz999/59577+32vr4+5WAST0EQOsnmwbR0Mq/IfVSSl2eMtRVTHR2YbG+P4bCBlduG0DsqG5vYEpvAxuh4yhqkF1QleXlmujtz10gvhpv1bVFyo1Hd2MyvYdH8HRFHU0dupbWeDqvH+zHX0+W6xaBklVXx7bFwDiWkAVI/3DwfNx6eNBoz3YH/JrJKK/n2cDhH4tMBKeGc5unEmuBROJj0vUO1tV3EtrMX+OVIJFUdF3k7Ez0emTWWSSMcevVHSiSXCecl1UxNRYkZ/i4smjAcV6ueG5iaWtrYfyaZg+EXScy8vIBRUJBntIc100a5EOBtj4Za7wSworqB6KR8opLziE7Ko7i862QjBXk5jPW1MDXSxtzokuqo1Uk2dbXUr0tFpKW1nZKKOgrLajsV1qLyS/9dR0NT1xxAFWVFPJ3NO4mos61Rr+U2QRC4mFPKkXNSS8KV/lcdTTUm+zmxIHAETlY9K231TS3sP5/CjtALZBRJF/SqyoosDfTkruCR6Gn1TkALK2v56dB59pxP7owfC/Z25JGZY7HtQ+PYJbS0i9gafoFfQiI7R32a62mxJng0c3xdB5w6IBJL2BObzHfHz1FcI7VuWOhq8fDk0cz2Gvjj9oaUknJ+PB3ZJZbN38aCx4LG4G9jcV2e81pR19LC9vhk/oyKpaBjLK+8nBxTnOy529+bkZaDn+4xBCnaxWKiCgo5kp7BkfQMShsu/6Y1lJQI6iChE2xtBkWJviWI52DgWomnIAiklF9SNtPIqrpcclNSUCDAxpoZTk5MdrBDS/XWalb5ryG5pIwNkbHsS0rtjOMw0FBnhc8IlvuMuGXJfnVjM7+FR/PX+cuE08XEkAcD/Jjm5njd/DP5VTV8f/w8e2MvIhEEael7hDOPTh7Tr+aef6OstoHvjoSz83xS58VrqqcjDwWPxrEfU2HaxWJ2hyfx4+HznU1DFgbaPDxzDNN9nXv9XCQSgRNxGfy4/xzpBR0NHCpKLJ/kzZ1TfHrNoiytquefY7HsPJFAQ0ccjZwc+LpYMnW0C0G+Dmj38hi1Dc3EJhcQlZRHdHIeOYVdO8IVFOTx6FASfd2t8HAwHfRZ7IOBwtIaopPzZCqzw9RV8Ha1wNfNCl93S+wte7YMSSQC8emFHDmfyvHINGrqL0e6+Ltbccc0X8YM71nFFgSB0wnZrN8bzsWOZiRVZUWWTPTk7ql9I6C5ZdV8fyCcwzGpCEKH39jPhYdmjMbCQKfX/S+hua2dreEJ/HriMgG1N9bjqdkBTHC1HTDxaROJ2BqRyI8nz3cqq7aGujw2ZQxTPZyuW1NabmUNP5+NZFdcMu0dyrC/jQWPB43B7xYloGKJhJMZ2WyIjCU857Kn19XYkLv8vJnt7jzUDX8dIREE4oqKOZSWzqG0tC4RkOpKSgTa2TLd2YlAW9sBT6j6vyaelwLdL5HNnCsiH5QVFAiwtWGGkyOTHezRvMVyH/9rEEkkHE/L5I+IWKLyCztvH25qzN3+3kx3dbplJzdVNzXzW1hXwulmasRjgaMJcra7bqv0sroGfgg5z/bIxE7D+GQ3ex4LHotTP5TIf6OhpZXfTkTzZ2g0zR3h5JM87Hl0+th+jSEUSyQciExh/aFznXFGxjrDeHD6aOaO7r2MeYlwrt93joxCKeHUUFVmeZAXK/tAOFNyStl4OJqjEWmIOy66lsY6LJ7kRfAoJwx6CYivqG7g8NmLHAtPJTWn6whNOTlwsjZipIcVvu5WeDqb96nZpjsIgkBlTSPNzQNoLtIb1qu/sqfnzS6sJDpJSkRjk/Op/5ciqqulxlhvO2ZOcMfL2aJHgiQSS4i6mMee0ERCItM7Fyu2ZvqsmObDjDGuPb5WQRA4k5jN+r3nSM6V+ucvEdC7pvr2qcksvaiCb/eFcTJB2vCmKC/P/DHurJ42CmNdzV73v4Tmtnb+CbvAT8cjOgcW+NqZ88ycCQy3GnilpbmtnU3n4vn5ZGSn/cbZ1JC1U8cywXngxLY3FNfW8+PpCLbFJHYS0FG2ljwWOPqWJaAAaWUV/BkVx+6Ei51JMbrqaizvKMMb34Ke/v8SBEEgvriEQ2lpHExNp7DucmVHTUmRiba2zHB2ItDOtl8DAv7viKcgCCSWlnIwVcrm82oue5WUFRSYaGvDDGcnguzthsjmDUBdSwtb4xL5KyqewlrpQa0oL880F0fu9ve+5bI3r0R1UzO/h8Xw5/nYTsLpamLIY0FjmHQdCWdtcwu/nIrk77C4To/cOEdrHg8ey3DLgV8U20Vitp5LYP3Rc1Q1SJUrT2tTnpkTgLdt3/1hgiBwLC6d7w+Ek1UiVQb1NNW5f6ofi8eN6DW8WyIRCIlN58d95zpLsBqqyqyY5M3KKT49RvFIJAJnL2Sz8VAU0SkFnbf7OFtwx3Rfxnva9UieWttEnI7J5GBoEuficzrJE4CNuR4j3aVE09vVoleV9EoIgkBVTRMFxdXkF1dTcOlfSQ2FJTU0t/SPdF6CnBwYG2hhbqKDpakuFp3/dDAz1u6X6iqWSEjLKeskovGpBbS0Xm4+MDPUZkaAGzMC3DA31unxsYrKa9lyLJY9py5ncepqqrF4sheLJ3mi24OKKQgCZxNzWL8vnKScDgKqpMjiiSO4a+rIPs2HT8wt4dt9YYSnSBtXlBUVWBrgyX3Bfuj1wUN6CXXNLfxyPJK/T8d2Tj6b5unE2pnjsOyHkvpvNLS0suFsLH+cjqahVfr5eFub8dT08fjaXD8vZlFNHT+diexCQEfbWvJY0GhGWt+6BLSmuYWtcQn8FRVPcYcCpygvzwxXJ+7y88bT/Na0Xf2XIAgCCSWlnSQ0v/Yyd1JRVOgkoUH2dr1OTfq/IJ6CIJBUWsa+lBQOpaVTUHuZtasoKhB4ibX34QMbwuAgt6qG3yNiuhjKddRUWe49gjt8PTG5xbLorkRdcwu/nI2+4YSzpV3En2dj+PVUFHUtUmXK29qMJ6eNY6TtwC8agiBw9EI6Xx44S15FDSAdbfnkrPFM8rDv1/uJySjgs12nScyVZsdpqatwz+SRrJjo3ac4o+MxXQnnMFVlVkz25o7JPRPOtnYR+84ks+lwNLkdnekK8nJM8XdmxTQf3Gx7vjAlZ5aw72Qix8JTuqh+w53MmBHgRoCPPQa6fTsm84uqSUgpvEwwS2ooKK7ukVzKy8uhptq/spVIJKG1h3GZ8vJyGBtoXiajJjrYWRng6WbRJ0LaLhJzIbWQI2EpHDuXSlNzW+d9Xi4WzJzgRvAYF1R7+F4bmlrZfSqBzUdjKa2SEgZlRQVmjnNj5YyRWJvItoJcIqA/7jtHYo70eFJVUmTRhBHcO92vTyX46IwCvt0XRkymtIqioarMPZNHsmqSL6r9UIpLquv55nAYe6KSEQQp6Vk6dgQPBY/u02QmWahpauHX0Ej+OnuZ2E50tuXJ6eOvqWrRG4pq6vjxdCTbY7sS0Ccmjbllm5DgcmXs94gYovOLOm/3Njflbn9vprlcP0vTEC7jEqc6mJbGwdSrBbwJHQJesINDt+X4/zTxrGpqZs/Fi2xLSCSl/HLnqpqSIoF2dkx3cuy3RDyEa0NxXT3fnD7HjvikztxTJ0N97vLzZq6H63WJGxksCILAngsX+ejw6c7sUBcTQx4LHM1kl/4RtP4iKruA17cf7eyQdTIxYO3UcUx0ubbyXHldA29vPd45bUhfU51Hpo5mwSiPfnX0Nra08cXu02w9cwEANWUlVk3yYdUk3z6NtcwuqWLdX8eISZcShEuEc+Vknx5Dy8USCYfDU1i/I4ziSumCcpiaCvMDh7NsijfG+rLLqyKxhJOR6Ww5GE1i+uVmI2N9zU5lz8q09wYViUQgJbOE0xEZnI5IJ6fg6olAcIkIamFhqoOFqW6HQqmDhYkupkbaKCn1z0oiCALVtU1SYlt0WUktLKkhvweiq66mzGhvW8b7OzDGxxbNPoS5t7S2cyoqg4OhSUQk5nbaDrSGqTJv0ggWB3th1NNnLRITEpXO34ejuZgtVTEV5OWYE+DBA/NHY9RDGVwQBMKScli/7xyJ2VICOkxNhScWjGNhwIhe/ZGCIBCeksvXe89yMV/qIbU20uXNlcF42/WPZKUWlfPF/jOcSckBQG+YGq8umkzwCMd+Pc6/UVrbwA8h59gelYhYIqAgL8f9E/x4aNKoAY/37Au6I6DzPF15bmoABreol/4SEotL+SMylgNJqZ3Reo6G+qydMJZg5+t7Ph7CZVyyLB7qIKFXWhY1lJWZ5eLEkuEeeJleHvjwnyOeVdXVXKiqZmtCIsczMjsPSGUFBYIdHZg5iJ1ZQ+g7KhoaWR8WycaYC7R3NAwF2Nlw/2jf2yI0OKOskjf3He/MybMz0OPpKeOY5Gx/XafVNLS08tmhM2w5LyV0RloaPD09gJmevTfl9ARBENgfk8L7O09Q19yKooI890/y476gkaj3M+bpfGoeb248SnGVlPgtHOvBI7PGYtAHT167WMyfR6L5cd852kRiVJUUWTXVt1fCCRCRlMsXm091xiEZ6miwaqYfcwI8euxMr2toYc+JBLYdiaW0IyReSVGBSaOcmDXRHV83q16/07Z2ETGJ+ZyJyOB0ZAaV1ZdjjBQU5BnuYoaNhT6WprqYm+hiaaqDaT9L39eCK0v7BSXVFBRLyWhCSuFVr9Xb3ZIAfwfG+9ljbNC7P76ssp5DZ5PZE5JAYUeskoKCPJNGObFsug/uDrLzNAVBID69iD/2R3A2Xhr5o6KsyIqpPtw7Z1SPqriUgOby7a6zpHQQSB9Hc15fFYyVce9NdBKJwOGYVD7bFUp5bSNycnBnoA+PzB6LWj+bJM6n5/HBrpNklEiV+Rlezry8MAida8jHBcitqOazQ2c4liSNLLM11OXthcH4XMfyO0BhTR0/nDrPtthEBAE0VVV4avI4lo0cfssriJeGifwZFddZCRpuasxTgeMYZ2t1y19b/ku4nAiUzp6LF7soofb6eizx8GC+uyvKYvF/i3j6f/oFleLLY988jI1ZPNydOa4uaA91o99w1Da38Mu5aP6IjOkMq/W3suCpwLH4Wt66JZ1LaGxt47tT5/gjPBaRRIKqkiKPTBzFPWN8r1sW3yWEpmTz1q5jlHRMWFniP5xnZgSgqXpt3uOKukbe3nacE0nSBgw3CyPeXT6tX53qIFU5P98VyrazCQCY6Wnx1sqpfZ4kk5pfxlt/HO0kEWPdbXh55WTM9Hs+EVXUNPLF5pMcOZcKSNWvu2f7sWyKd49l36raJv7YdY49JxM6vYu6WmosnOLFgime6PcyiaihsZXwmCxOR2RwLja7S+n5kooY4O/A6D6qiDcDXdXZDHIKuk6Wc7Y3JsDfgQA/B+yseu5oF0sknI3JYsuhGGKSL3cfezia8sCisYwaYdPja4lLK+TbraeJT5eWTE30NXnuzkkEeNv3uJ9YIuGfk/F8vfMMLW0iVJQUeHjuWFZO8ekTSaprauGTHafYcz4ZACtDHd66c2q/1c82kYgfjpznl5BIJIKAvqY6ry2azOThDv16nO5wLCmDd3Yfp6K+CTk5WDHaiyenjbtu2b+XcKGghDf3He+chORuZsSbsycz/DbwUF661myIjO20b91O15r/GgRBILKgkG0JiRxMS+u8/ivKyzPWxJjf7rzjv0M8bd9eh76uDvPcXFnk4YGr0a03teH/AU1t7fwRGcsv56K6rEKfDhzH2NtgFSoIAoeT0/ng0ClK6qTEb5KzHa/MDMJc5/oGGlc3NvPBvpPsi0sBwFJPm7cWBjPKvu+jAWXhSHwab287Tm1TC4oK8jwUPJr7Jo3sd1D2uZRc3tp4lOJqqWK4LMCTtfPG90ktbWsX8dP+8/xxOAqRRIKWugrPLg1k1mjXXqN7dp1K4Jt/TtPQ3Iq8nByLJ3uyev6YHht9WttEbDkUzR+7IzrJooOVIcum+xA81qXHTuvGplaOhF4kNCKd2KR8RKLL4+b0dTUI8HNgvL8DPh6WA1Yz6+tbyMmvoLGprfeNr4CCvBymJlIlVWGAeZB5RVUdqm0miamFXTr3zYy1CfBzYOpEN5ztes5ITcspY8uhGI6GpXROkBrtacPjKydiZyF7QSMIAqdiMvl848lOq0SQrwNPrwzCWK/nLvTCilre+fMoESlS0utuY8wbd03FwbxvC6jTSdm8s/kYZTUNyMnBykAfHh2A+pmYV8Krmw+TWSq1WMz2deHlBZP6ZDHpCbXNLXx64DTboxIBMNXR5M0FUxjvZHNNj9sbxBIJmyIv8GVIGPUtrcjJwVLf4Tw1efx1m7I2mKhsbGJ9WAQboy90xvFNtLflqcCxuJn0fbjAEAYP9a2t7E9JZVtCEnHFxUhaWsh+/ZX/DvHcGhXNHM8RQzlfNwltYjH/xCbw3ZnzVHT4IJ0M9Xly4jgmO12/xpvBRE5lNe/sP8HZTGlHrIWuFq/MCCLI2e66Pq8gCBxKSOO9PSeoamxGXk6Ou8b78NiUMf2+GP4b9c2tfLDrBHuiLgLgYmbIuyum4WzWv4VZQ3Mrn+8+zfYOldNcX4s37+i7ynkhq5i3Nxwhq1h6kZ7s48ALyyf12qmcnl/OB38cIyFD6sV0sTbixXum9Ng0JAgCR8NT+H7zGUoqpKTG2caIR1YE4Odh3eOxWFndyNb90ew6HN8ldN3GQo8Af0fG+zng6mDSL5tFc0sbOXmVZOdWkJ1b3vG3gorKht537gEqKorYWOpja22IrbUBNtYG2FkbYGig2a/fW1VNI2FRWYRGpBN1IZe29suVI9/hVty5YBQjR/S8aKysaeTPvRFsPxKHSCxBXk6OuZOGs3rxWPR6+I6bW9v5eXc4Gw9FI5YIqKsq8dDCcSyZ4tWjiikIArvPJvHZtlAaOiwjD8wcxb3T/fo09rKuqYVPd4ay+1wS0KF+rpyKt33/FLLWdhHfHQ7n95PRSAQBU11N1q2Yjp/9tXeKh2fk8saOYxRWS4/hud6uvDBr4jWX9XtDRUMjHx85ze546TlDV12NZ4PHs8DL/braiwYLxXX1fHv6HNuv6CeY7e7M2gljsdbTubkv7v8Y6RWVbIuO5uXp0/47xPN6z2ofQvcQSyTsS0rly9CwzqkTljrarJ04hllu1+ZHvFFoaRfx4+kIfjoTRbtYjJKCAqvHj+TBAP/r3vRUXt/I27uOE5IsLX87Guvz9qKpjLiGeKRLiM4q4OWNhymqrkNeTo77J/vxcPDofs+jDk/J5e0rVM7lE7x4Yu64PqmczW3tfLcrjI0hMQiCNF7pxRVBTPF16nm/1nZ+2hXOpsNdCcniyV49Tn2JTy3kq79OkpwpbUYx0hvGQ8sCmDbOtceLZkFxNZv2RHHwRGIn8bKx0GNmkAfj/R2wMuu92UgklpCTW0F2npRYZudISWZxaa3MfYwMNdHpQ5f2lWgXiSksqu5CEK+EhroyttaG2FjpY2tjiJ21Afa2Rmhr9U5YmprbiIzPISQslZPhaYglHY2AdsasnO9H4GinHlXW/JJqvtt0mpOR0mlX6qpKrJo7iuUzfXocQ5qRX877/VxgAJRVN/DexuOEXpA2yTmYG/DmXcG42fTt93MmKZu3r1A/75jozWNzxvV7wReXXcRLmw5RUFmLnBzcM9GXx2aMveZpZU1t7Xx15Cx/hcUiCKA/TJ035k9msvu1l/V7Q2ROAW/vDyG9TGrL8LY05Y3Zk2/JGfDdIbeqhi9Dw9iXJLXmKMrLs9jTnUcDRg/lgN4k/Oeai4aI542FIAiEpGfx+cmzpJVLT0yGGuo8GjCaxV4et2zo+78RkprJugMnKewgzeMdrHl1ZhA2+gOf/NMXCILAwQtpvLs7hNpmafl7TdAoHpjod80e0naRmG8OhfHbySgEQToC8P07pvcrkxNkqJwrp+Ln2DeVMzI1n3c2HO0MkZ812pVnlkzsNQD+dFwWn/wZ0q8SbEFpDd9tOs2JCOnYUDUVJe6a58/yGb49+j9TMkv4e2cEp86nI+kgWR7OZqyc78+4kb03kInFEhKSCzgemkLo2TRqZMxq19PR6FQlbTv+2VgZMExjYGVZsVhCUUkN2TmXiK6U5OYXVneG5l8JeXk5vIZbEhTgwoSxTuho9052i8tq2bI3ir3HEjrjmyxMdFg+z48Zge49WhXiUgr46q9TXMySLgCM9TV5aNl4po6VvQC4ZKn4dutp6psuWyoeWjSOYT2UrwVB4HBkKh9tOUlNg7RisCrYlzVzxvQpOqmuqYXPdoayq0P9tDTU4e0BqJ+NLW18tPsUOyKkJXJnM0Pev2N6vz3U3SE+r5jXth8hs6yjrO/lwktzgq57CbxdLGbDuVi+PXmOprZ25OXkuHOUF48Hjblmz/mNQnJJGZ+fDONUZkdTm6ICd/l5s3qMHzpqt76F4L+EIeI5hAEjIreAT0+cIbZQqk5oqaqwesxIVo30HvAorRuNgupa3jt4kpBUqVJiojWMl2YEMtXV4brbAiobmnhn93GOJko7WF3NjFi3eCrOpteuJGSUVPDS34dIKZLONF/g784L8wLR6Od0ndisQl754xBFHR3r/VE565tb+XL7aXaclhJWY91hvLJyCuOH2/a4X1l1PZ/+dYIT0dLPxURfk+dWTSbAS7bVoa6hhd93nWPr4djOEu+cQA9WLxkns2lIEASiE/L4a2cEURdyO28f42PLnQtGMcK159nQgiCQnFpMSOhFTpxOobLqcre4hroydrZG2HaUvW2spCSzL0RvMNDeLia/sKqznJ+dJ1VeC4trOrdRUJDH18uayRNcGT/GsVfyW1PXxPYDsWw/GEtdg3Tqjp6OOktm+bJgmpfM/SUSgaNhKXy/5XRnioCrnTFP3BmIl4vsUnRlbSNfbDrF4XNSr7OhjgZPrwxi0kjHHr+X6vomPtpyksORUoXLykiH1+8Kxsexb2Xvf6uf907x4+GZY/pdITiekMFbW49R3diMsqICa2eO584A72suU7e2i/ju+Dl+DY1CIggYaKrz1oJgAl2vrxUIoKS2ng8On+JQklTJNhymzvPTJjJ7uPNtYaMCiMwr4NMTZ4kpkDa1aaqo8MBoX+7y9x6KVrxBGCKeQ+g3kopL+ezkWU5nSS/WqoqK3OXvzerRI9G+TVaObSIRv56N5ofTEbS0i1CUl+eeMT48PHHUde8cBTickMY7u0OobmxGUV6eNZNGsTrQr99NPv+GRCKw8Uwsn+8/Q5tIjI66Km8uDe53p227WMxPh87z8+EIJIIg7Vi/s+8q55mEbN7961jnbPbFE0bwxMLxPSpWYomEbcfj+GF7GI0tbSjIy7Fimi+r54+RGbPTLhKz42g8v+4M7yRDo0bY8NgdE3Cw6p7Ai8USQiPS+WtnBKmZlzMlp4x35Y75fthbyyb+giCQnllKSGgKJ06nUFJ2eRjFMA0VAsY6MXmCC96e1j1aAW4WiktqOHE6leOhF8nIKuu8XUlRgVEjbZk0wZWxo+xR62GB0tTcxr7jCWzeG0VZhZRIaqgrM2+qJ0tn+8oM2m9pa2fLwRg27Lnc5DXRz4FHl0/A0lR2ZeF8Yi4f/Xmc/NIaAMZ52vLcnZMwM9Tu8b2eis9k3d/HqaiVLgiWBnryxILxfRptWt/cyic7TnV6P90sjXjv7hnYGPdutbgSFXWNvP7PUU5flKpsoxwteXf5NEx0+j6+UxYu5BXz8rbDZJdLBybM83HjxdkT0boB5+AzGbm8cyCkM1d4lK0lr80MwsFI/7o/92BAEAROZmTz2cmzpJZJ49j0NdR5ZNwolvkMv20qdbcrhojnEPqM7MpqvjgVxsGL0jKmorw8S72H88g4f4xuI69MWGYub+8/QU6l9ITtb2PB67Mm3ZCTZnVjM+v2hHDwgvQzdDIx4L0l03A1u/Zuy9LaBl7bfJjwtDwAxrvY8PayYAz7OQUqr7yGVzYcJKFjWsxsP1deWBLUpy7d5tZ2Ptt2iu2hUpXTwlCb11cFM9K5Z8Kanl/OO78cIaVjRKKHvSkv3j0FJxnkESAsNosv/jxJfsekIjsLfR5bOZExnt0rqmKxhIMnk/hrZwQFxdJ9VJQVmTNlOMvmjMTUSDaRyc6tICT0IiGhKRQUVXferqaqxLjRDkya4Iqfj82AOtubm9vIyi6nqbG1942vgLyCPObmuhgbaQ1IbcorqCQkNIWQ0BRy8y/HKqmoKDLW355JAa6MGmmLigzSLxKJOXomhY27Isju2F9JUYHpge7cvXg0Jobdn4erahv5aVsYe0ISkAgCigryLJ7qzQOLx8rMX21pa+f3vRFsOBCJSCxBVVmRNQvGsnxazxFK9U0tfL7tNLvOSsvelkY6vHvfdIbbys4avRLH4tJ5e9NR6ppaUVVW5NmFE1k0dni/Pm9BENgafoGP94TS0i5CU02F1xZNZoa3c58fQxZa2kV8czSM389EIwhgrDWMtxZOIcC556rCYODS4v370PO0isSdi/dHAkffNhUviSBwIDmVL06FkVcttQJZaGvx+IQxzPVwuS16E25HDBHPIfSKf08bkgPmeLjwxIQxWOnq3OyX12eU1jXw4eFTHEiUkj6DYeq8cAPLRMeTMnhr13EqG5pQkJfjgYn+PDRp1KDkgR6KS+Wdbcepa25FVUmRZ+ZMYNnYEf2+QO4+n8yHW0/Q3NbOMDUVXl02mem+fbtAJueU8Mqvh8gtlRKzFZO8eWxBzw0aYomEvw5G8ePOcNpFYoapqfDokvEsCJQ9kaa6rokvNpzgSJi0BKurpc6DS8YxO9BDpsoYl5TPl7+dID1bqvJpDVNl0QxvFs7wRldG+bu9XcyRkCS27o4iO/fy5DNlZUXG+NkzaYILo0faodrHUZeCIFBRUU9mVhkZGWVkZJaSmVlGUXE113JmHTZMBXs7I+ztjXGwN8Le3ghrKwOU+zgSUhAEsnIrCDkltQxcWY5XV1Nm8kRX7lg8CjNTnW73l0gEwqKz+HtXBAkp0gELKsqKrFzgz8p5fjKJa1ZBBd/8HUp4R5C8sb4mL9wfzBgv2aQpu6iSD/44Rmyq9Hm8nMx544FpmBt1/9ou4VxyLm9tOEJpdQMK8nKsnjWa+2b490mVLq1p4PU/D3E+TRrbFDjcntdXTOnXzHeAnPJqXvr7IIn50sXVTG8XXlkUNCgKZWxuEa9sPdypQC4a6cFzsybcEP9lQXUt6w6e5ESHXclUW5OXZwQy5TpPcxtMtIvFbItL5Nsz5ylrkCrkjob6PDlxLFOcbp/3cbtgiHgOQSaqGpv4ISySjdHxnXlokxzteCpwHM5G12+O8GBDJJbw1/k4vjoR1mmMv8Pfk7WTxt6QE3NNUwvv7z3Rmctpb6THe0um4WFx7R3r9c2tvLczhH3R0sd2tzTm/TumY2vUv5JgbWML72w+yrE4qa/S18GCd1dNw1Sv99+SWCLh90NRrN8bjkgiwVBHg7fvmcYoV+se9ysoq+Gtnw51BogHeNnx0j3BGPTgyTwansLnf5ygpl7aPLJshg/3L5KtlJWU1/HdhlOEhHWEzWuocPfi0cwL9kRdxj6tbSIOHLnAxm3nKSuXlpIVFeXx97Fl0kRXxvnbo67e83EjEonJy68kI6OMzKwyMjNLycgso66uudvt9fWGoavXvxGF7W0iCouqu2SLXoKCgjzW1vo42Bt3kFIpMe2to10QBFIzSjgRmkLI6ZTO968gL8fkQDfuXDoaa0vZlYH4iwX8tPEMcckFABgbaPLo3YEEjXGSefE+F5/Dx78eo6hcqjhNH+/Gk6sC0dbs/rUKgsDuUwl8sfkUTS3tqKko8dSKicyb2LMSWdfYwvubQjq9nyPsTHnnvulYGur0+JmAlFz/fTKGr/aepV0kxkBLnbfvnMZYV5te970S7WIxPx49z0/HIxBLBEx0NFm3Yhr+Dtee0dvc1s6XV3S+m2hr8s6iYMY69vw7HCz8u0FzoqMtr84MxPI2ii5qbm/nr6g4fgyLpLYjf3qEmUln/vQQBgdDxHMIV6FdLOaXc9GsD4ugse3yBIing8bhY2F2k19d/5BWWsHzOw6RUiJtsvG0MOGN2ZNxM70xQcLnM/N5fssBKuqbkJeT474JI3l0yuhrjlcBaXD10xv2UVxdj7ycHA9M9uehqaP67RO9kF3Ms7/uo6ymAUV5eR6ZNYa7p4zsU5mpqq6J59bvIzZDqkBN8XHklTunoN3L5J6DYcl88Mdxmlvb0VBV5umVgcwe7y6TONQ2NLNu/WFOR0vjpuwtDXj5wWm42XdP3sViCZv3RvHrljBa20TIy8sxd8oIHlgxTmZskVgsYd/heH7fGEZVx1hJPV0Nli3wY9bUEWhq9vyexGIJ4ecy2Lc/jti4XNq7iTmSl5fDyqqDENobdRJDHZ2BNR21t4vJy6sgI/OygpqZVUZ9fUu325uZ6RA8xYNZMzwxMOjZZyiRCMQl5LFpWwQRMVJVUk4OggJcWHPvRExkWBMEQSAkLJVvN5zq9ID6eFjy4iPTMDPW6Xaf5pZ2ftp2li0HY5AIArpaaryyZjrjvGU3zBSW1fDWz4eJS5Mee+M97Xh7zQyG9bAoEASBgxEpfLAxhIaWNtRVlHjj7qkE9xLrdQmpBeW89McBskqkXeV3TfJl7bzx/S7JxucW89LfB8nviF26L8iPJ2aMG5R8zKjsAl7ddoT8KimRXz7akxdmTbzuk9ZASn7Xn47gl7NRtIslqCgq8ETQWO4d63tbZH9eQl2LdArS7xGXJ+6NtbXi9WlB2On3b1E/hKsxRDyH0AUXikp4Zf/RTsO1u4kRTweOY7xdz4HbtyK2xybxzv4QWtpFaKup8MyUABb7eNywE+A/ERdYt/sEIokEO0M91i2eygirvnnLesPxhAxe/PsgLe0iLPS1eX/FdLxs+78oOJmQyYu/HaClXYSNkS7v3T0DN6uep9RcQl5ZDY99tYOC8lrUVZR4YUUQs0e79doJ/tOucH7efQ4AH2cLXn9gWo+NIpn5Fbzw2W4KS2tQUlTgnvmjWDXXX2aXcWlFHe98dYC4JKnq5uVmwdr7JuFoK3uxEZ+Yz1c/HCejoxRvZKjJHYtHMTN4uMxS8SVUVTWw/2A8+/bHUd6hEAKoqyt3qo0O9sY42BtjY9P3EvhAIQgCZeV1UrW1Q2nNzCqj+IoSury8HOPHOTFvrg9enr1PEruYVsxfW8I5c06qiCsrK7JyySiWL/SXaTVoaW3n710R/L0rkrY2ERrqyjyzegpTJ7jJfJ6kjGLe+/EwWQWVyMnBg0vGc/c8f5mvTyyRsOlwDN9vlyqRdub6fP7UAkx7mTlfVFnHa78e6lwwPb14AncG+/a4T+f7ahPx5Z7TbDoVB8AULwfW3TUDlX76e5ta2/hozym2n5P6T6cMd+C9O6Zf87AIkOZ+fn7oDBvDpa/Rz86CL+6Yfd1D5y8hq6KKd/aHEJ4ltSdMdLTlg4XT0FW/Mc8/WChvaOSHsxFsjrlAu0SCsoICjwaM5oHRvtfcCPr/jCHiOQRAeqL64lQYGyJjkQgCOmqqvDRlIvOGuyJ/mxHOprZ23t4fwq446Szm8Q7WfLhgOvrDbkyUjUgs4eMDofwVFgvATE9n3lk0dVBC6AVB4K/TsXy85xSCIG0g+njVTIYNwDKw/WwC67YcRyIIjHOz4eP7ZvUpJgkgMbuEtd/uorq+GTN9Lb55YgE2Jj0rAW3tIt799QiHwqW2gHtm+/PQwp5VnpMR6bz9/UGaW9sxNdTig6fm4WQjm0CeCE/lox+OUt/QgpqqEmvvm8SsSR4yiUtZeR3f/3qSkFDpaxqmocJ9d45n7gwvlJRkX1gEQeBCQj579sYSejq1MzNTW1uNmTM8mTrFA0tL/VtK5WlobOH8+Sz27IshIaGg83ZrK33mzPFmarAHw3pRqtMzS/nmpxDiEqSEwthQi0ceCGLiONml9KLSGt756mCn/3PqBFeefmCKzPildpGYLzacYMexeACC/J149aFpPXajp+SU8vQXu6ioaURPS53PnpyPm13PVhaxRMJnW0+xKSQOgDsme/P04ol9/s4OR6fy6l+HaReJ8bE354sH56I1gDzNAzEpvLr5CO1iMcOtTPjqvrkYaPbPdiELp1KyeG7zQRpb27DU0+bbu+dhf4M6zwVBYGt0IusOnqBVJMZEaxifLZmFj9XtVTUDyKuu4c1DIZzpSHJxNjJg3axgRpjd+jPsb0UMEc8hcCYrl9cPHKOgtmMkm4cLL0+ZiJ7GjSFqg4n0sgqe/Gc/meVVyMvJ8cSksTw43u+GEYDa5hae3bifsAxpZ/ljU8bw0KRRg6IWiyUSPtx1ik1n4wBYOmYELy0I6ndsjyAI/HDwHOsPSlXH+aPdeWX55D6v4EMvZPHiT/tpaRPhYmXEV4/N73XkZU1DM89/tYe4tEIUFOR58a7JzJs4XOb2EonAT9vC+H2X9DX6ulux7onZMn1/Tc1tfPXbCfYdl3bTuzqY8PraWViadR/T09razuYdkfy99RytrSLk5GDODC/uv3N8j1mbjY2tHDuexO69MeTkXG44cnczZ95cHyYEOF93RXMwkJVdxp69sRw9lkRzR7SRqqoSUya7M2+ON/b2slVvQRA4eSaV73450ekB9RpuyRMPTcHepvsUApFYwobt5/h9azgSiYCpkRavr53FcBfZ4ey7Qy7wyW/HEYkl2Fsa8OHT8zCXUaoHKK2s56nPd5JRUIGKsiJvr5lBkK9jj5+DIAj8eTSaL7afBiDY15G3753eZ/UyMi2fp37aQ0NLG3Ymenz3yEJMdPsflRSdVcDa3/ZS29SCuZ4W3z0wHzvjwSGI6SUVPLphN4XVdQxTUeaTFTNvSNf7JaSWlLP2n/3kVFajIC/H05PH33ald5AeK3sSU1h39CQ1zS3SkcZ+3jw5cext08V/q2CIeP4fo7qpmfePnWJXgnQWr5mWJm/NmMxEhxt3UhpM7IxL4u19ITS3izDU1OCzxTPxs7n2Wcl9RU55NY9u2E1ORTVqSoq8t3Q6Uz16vvD1FU2tbTz/1wFOJUu9ds/MDuDuQN9+E1qRWMK6LcfZGS4t7z04fRQPzxzT58fZcTqB9/6WqqRj3Kz5aM3sXkPp80urefKzneSX1qChpsyHj83B3112w0NDUytvfnuAs7HSLtnlM3x59I4JMgl2SkYJb36xn4LiauTk4M4Fo7h/2VgUuynFC4LA6bB0vv3lBCUdIyxHuFvwxJrJOPZAtrKzy9m9N6ZbojZ3tjcODn2zJ1yCRCKQn1tBUmIBFxMLSUkuor6++8YjWZCXl8fGzhA3D3Pc3C1wdjNDo58TkC4R6V17Ysi9onPfw92CeXO9CRgvm0i3tLSzaft5Nm6LoK3DRztvphf33TkeLRkLhISUQt7+cj/FZXUoyMtx95Ix3LVotMzv9kJaIS9/sZfKmkY0NVR594lZ+A+3kfl+GppbeeW7/YQn5CAnB08sm8gd03x6Pb4PRqTwxu+HEYkl+Dia89nDc9HqRf29hPSiCh75bgfltY0Y6Qzj24cX4GjW/+bLnPJqHvlpJ/mVtWiqqfDlPXPwG4SmI4Cqhiae/Hsf0TmFyMvJ8cyMAO4e3/vnMlhoaG3jjb3H2J8gbewKdLLlgwXTr/vEpeuBqsYm3jt2ij2J0iqJhbYWb82YTIC9zc19YbcRhojn/yEEQWB/cirvHjlJVVMzciBduQWOvS0nNzS3tfPOgRPsiJWGPY+1t+LjhTNuWGkd4GxaLs9u2k9dSysm2pp8c9fcQcnmBCirbeCxX3ZzsbAMFUUF3rtjOlM9+9YMcSWa29p54bf9hCZmIy8nx8tLJ7F4/Ig+7SsIAj/sDeen/ecBmDPGjVdXTelVJY1LK+S5r3ZT29CCqb4Wnz09H3tz2Rfl3KIqnv90F3nF1SgrKfDiA1OZEdC9J1AiEdi4O4KfNp1FLJZgpK/Jq0/MwMej++7Tisp6PvjiIJExOQAYGmjy8H2BTJrgIvMCnJhUwM+/nOJCR2kZwMpSn7lz+1aavoTGxlZSkgtJTiwkOaGAlOQiGhq6bwAaKOTl5bCxNcTVwxx3DwtcPSwwt9DtE7m4ZB3YvSeG02fSOq0DOjrqLJw/kqVL/GUS0OLSWr7/5SSnzkpJhZamKk8+HMzkia7dbt/Q2MrnPx/ncKjUCjPcxZzX186UmaNaVlXPS5/vITmzBHk5OR69YwIrZspedInEEj7+M4SdJy8AsHiSJ0+v7L0yEJGSx7Pf76WhpQ1bUz2+eWJBn1IdAIqr6nj0+51klVQxTE2FL1bPZWQfJyVdiaqGJtb+toe4nGIUFeR5e2kwc0bK9sT2B20iMe/uCWF7pHTROd/XjTfmTx6URse+QBAE/olOYN3Bk7SJxJhpa/LZkll4WQ6O7/1G41RGNm8cPE5RnVT1nzfclZenTLztfKw3A0PE8/8MJXX1vHHwOCcypMqZk6E+62YF42l+e/74M8srefKf/aSXVSIvJ8fjQWNYE+B/w8o4giDwd1gcH+4/hUQQ8LI25cuVcwbNo5VWXMGjP++ipKYevWFqfHXfPDyt+/9dVTc0s3b9bi7kFKOipMAH98wiaIR9n/ZtF4tZ99dx9oRJif3qWaN4aE7vKunhcym8/bPUA+dma8wna+fLjEoCOBOTyZvfHqCxuQ0jvWF88PQ8XGX49OobW3jjs31ExOUAEDjGiefXBMtU2iKis1n36X5qaptQVlJg+SJ/7lgySuaEnrq6Zn765ST7D0h9hv1pxhEEgYL8KpI71MykxAJys8uvyupUVVXCycUUt+EWuLmbY2jcv/NWa4uI9NRiKZlNLKC0pPaqbbR11HF1M+t4DgucXE1RkxEjdQmVlQ0cOBjP3v1xVHR0pVtY6PHkE1Px8baRuV9MfC5frT/emXk6Z7onjz84SWZz1pHTF/n0x6M0NrUxTF2FN56axRif7rvYW9tEfPzbMfafkh6D08a58uLqYFRllDgFQWDjoWi++icUQYCxI2xY9/BsmbFbl5BWUM4TX++irKYBQx0Nvn5sAU6WfRthW9vYwpM/7iY2qwglRQXeu2s6wd79XyC2tot4ZdNhDsdL84YfmTqah6aOHhR18nqfr/qCi8VlPPnPfnKralCUl+eZ4PHcM+bGqa+Dica2Nr44Ke2NEAA9dTVenxbEDFfZnuchDBHP/xtIBIF/YhP48PhpGtvaUFJQ4JFx/qwe63fbjgfbFZfMW/uOS0vrw9T5ZPFMRtkOTmmqL2gTiVm3J4Rt10lBCEvN5ek/9tHY2oaNoS7frZ6Ppb5Ovx+nsLKWR77bSW5ZNdrqqny5Zh5edn0z+De3tvP8j/s4m5iDvJwcL90xiUUTelZJBUHgt70R/LDjLACBvg68/eAMVGUQEEEQ2Hwgmq83ShumPJ3Nee/JOejJ8I3mFVXx4vu7yCuqQlVFkSfvnyyzgUgkEvPLn2fYuE2q1DrYGvHGi3OwsujePycIAkePJfHDjyHU1DQBMGP6CO65OwDDXuKH2lpFHD+ayPbN58m9wv95CSamOrh5mHcqkrb2Rt3aAQaKyop6kpOkimpyUiHpqcW0t3WNdFJRUWTqjBEsXDYKC8uem8HEYgknTl7khx9DqOqYQz9lsjsPrZmEnm73341ILOGPjWf5c0s4ggC21ga8+eI8bKy6/7yLSmt4+8sDJKYWIS8vxyOrJrJsTvdqpiAIbDsSx5d/nkAsEXC1M+aT5xbIPE4ATkSl8/qPB2ltE+FoachnT83HWK/n77Gkqp7Hv95JZlElGqrKfPbIXPx6mbx1CS1tIl7+4yAhFzKQk4MXFgWxfKJXn/a9EhKJwJcHzvDriSgA5o505c0lwf2eFy8LZ9NyeWbTfuqvQ4WmL2hoaeX1vcc6h3lMcrbjvfnTbsvSO0B8YTEv7z9Kerl0gtdkJ3venD4J49toot+NxBDx/D9ATlU1r+4/RkSetJPV29yUdbOCcTC8Pebq/htXldbtrPho0XQMht24VXtVQxNPbdxHVHYhcnLw7IwJg+qZ2n4ugXe2H0csERhpb8EX98xBewAn5ZSCMh77ficVdU2Y6mry7SMLsDPp2/de3dDM2q93kZhTgqqSIu+vnslEz55VUrFEWubccUJa5lw53ZfHlgbIzDkUSyR8seEE247EAbBgsidP3R0k8wIbGZ/Da5/upaGxFSMDTT58cYHMmKTSsjre+nAPSSnSgPr5s7x55IEgVGSUjPPyKvniq8PExUsbw6ytDXhq7TRGDO+ZdNRUN7J3Vwx7dkRT05H/qaSsgJOzKe7DLXB1N8fNwwI9/Rt7EWprE5GRVsrFpIJOVbSioxlITg5Gj3Nk8fLRDPe07PG4bWhs4dffQtm9JwZBkE5KWn1/ILNmesmsLETF5rDuk/1U1TSiqqLEU48EM32KR7fbtreL+eznY+w9Jm0MmxnkzrNrgmWOH41JzuflL/ZI7RuGWnz2/EJszGUf00lZxTzzxW6q6pow1tPkq2cXYmvW82+grrGFp7/fQ0x6IUqKCrx73/Q+Z32KJRI+2HqCrWekv4H7gv14fM64AZ0btp1L4N2O84C/gyWf3T17QOeB7pBdXsVjG/Z0etLfXzqd4EHypPcFgiCwOfIC7x8+9Z8ovbeJxaw/G8EPZyNol0jQVFHhhckBLPGSnarx/4oh4vkfhkgi4ffzMXwZGkarSIyakiLPBI1npa/nbTuDNqOskqe2Xi6tPxY4mjUT/G/o+0krqeCxK7pEP14+kwkug9OQJZEIfH3oLD8fjwRgtq8Lby0NHpCKGpGax1M/76WxpQ1HMwO+eXgBxjp9Iz9FlXU89uUOckqr0dZQ5YtH5+Fp37NK2tLWzuvrD3IyWqr2PLMyiKVTvGVv39rOG9/sJ7QjFP6JlRNZLsO7JwgC2w7E8s3vUrXLw9mM956fh56M0v2Z8HQ++OIg9Q0taKgr8/zaGQSO7370Z2trO39vCmfzlnOIRBJUVBRZdec4lizy7zFSKS+3gh1bIjh6KIG2NmnItKGRJgsW+zNzrhcaw24t9UYQBOJjc9m2+TznwzI6b3dyMWXRslFMCHLpUYFNSS3msy8OkZEhHfno5mrGU2unyeyAr6xqYN0n+4mOl0bQTJ3kzlOPBHc7MerS9/v17yeQSASGO5uxrofvN7+kmqc/3EFBaQ2aGqp89Mw8vFxkeyqLymt54tMd5JVUo6WhwmdPLmCEY8/Hc2u7iFd+OUhIrPR4fn5ZEMuCvHrc58r38/ORCL7dFwbAHH83Xr+jd090dzibksMzG/bT2NqGrZEe3z0wHwt92bm3/cH1TOHoK5KLy3jqP1J6B0grq+Dl/Ue5UFQCwGhrS96dNeW2Gi99vTFEPP+juFhaziv7jpBYIg3EHmdrxTszp2ChMzgnrJuBf5fWP148k9E3sLQOEJKcyQtbDtLU1o6lnjbf3DUPh0GKPWltF/Hq5sMcipOWnx4KHsUj0/recX4lDkWn8uqfhxCJJfg6WPD56jl9zhhML6zgsa92UF7TiImuJt+sXYCdae8K0bNf7iYuTaoQvb1mBpP9ZCtEVbVNPPfJTpIzS1BWUuD1h2cweXT3xFCqiB1n7zGpgtSTItbWLmL9b6fYtjsaABcnE954fq7MOeORkVl8+c0RiopqABjlb88TjwVjKmP7S+Rt++bznLuCvDk6m7B4+eheyVt3kEgE8rLKqZcxTlMW5BXksLE3GhDB7Z40a7FgiR8z58gmzWKxhN17Yvj191CamtqQl5dj0UI/7rlrfLfeUbFYwsat5/n17zNIJAJWFnq88cJcHOy6V6kj4nJ4/dO9NDS1YmygyQcvLcBRRm5rdV0Tz3+yi8SMYpQUFXj94elMGeMi8z3X1Dfz9Oc7ScwqQUVJgXWPzGaCd+8K/oebTrAtVHrsPTBzFA/P7ftvcld4Iu9sPoZYIjDO1YaP7+97Vu6VSC0q59Gfd1Fa24DeMHW+uX8ew60GJ0Py37nDM0Y48c6iqYMSZN9X/Lv0HuRsx/u3celdLJGwITKOz0+epUUkQlVRkbUTx3KPv/dtK/oMJoaI538MbSIR352J4MfwSEQSCdqqKrwUPJEFw3ueKHMro6mtnXf2h7CzIxD+ZpTWBUHg51ORfHnkLIIAo+wt+WzFrEGbBFLV0MSTv+0lNqcIRXl53lw6hXl+7gN6rD9DYvh05ykAgr0deXdV33MJo9MKeOq7PTQ0t2Jvps83TyzAuJdcwtKqetZ+uoOswkqGqanwydq5+LjIXhDkFVfx9Ic7KCyrRWuYKh89Mx9P5+7zHGvqmnj14z3EJRcgLy/Hw6smsHzOyG6P5cLiat76YA+pHYrc0gUjefDuid2qlpWVDXz3w3FOnJRGiRkYaPLYI1MIGN99U4BIJObk8WS2bzlPRpr08ftTrr4S7e1i0pKLSIzNJTEuj6T4PBobWvu0778hJwd2jiZ4eFvh4WWFh5c1egZ9L+l3ZxNQV1dmxhwv5i/2w0QGAS+vqOe7749zqiN439BQk8cfDWb8uO4XG/GJ+bzz0V7KKxtQVlLgsQcnMXeGV7efWV5hFS98sJP8ompUVRR5be0sJo7qvgTc0trOm98d4FSkdBHw6IoJrJzd/fEBUs/yy9/u4+wFabLDi/dMYX4PebLQMW1r/3l+2BsOwILxHrx0x+Q+5+eGJmbx/K/7aWkX4WZlzDcPzUdPs/+JG6W1DTz28y5SispRUVTg/ZUzCB4xeKXxrREJvLs7BJFEgru5EV+tmouJdv8zSQcKQRDYEnWB9w79N0rvIA2ef/XAMc7lSFMxhpsa897sqTgb9T9u67+EIeL5H0JMQREv7ztKVqV0jvB0F0demxaE4Q0kaIONjLJKnvxnHxkdgfA3o7Te0i7i9e1H2R8vvcguH+3Ji7MnDtrItOyyKh79eZc0v09Vhc/vmc0ox+4jgXqCRCLwxZ7TbDguVftWTPTiuYWBfe7wPxGXwUs/HaBNJMbL3owvHp3Xa5ZhVmElaz/dQWlVPYY6Gnz5zEIceugCjk8t5PlPd1HX0IK5kTafvbAQK9Pum1wyc8t58YOdFJfVoaGuzJtPzZbZ9RxyOoWPvzxEU3MbWpqqvPjUTMaNcrhqO7FYwp69sfz6WyiNTa3Iy8uxcMFI7rlrPOrdzPhuaWln9/Yodm2L7PRHdjboLPXHQkbTTJfHaG4jKT6/k2imJBXS1irqso2aujL6hpr9Why2NLdRXlp31e1mlnodJNSKET42mFp0H6J/JbprjJKXlyMg0IXlq8bi4Ni9unbufCZff3OE4o6O+rFjHHjs0WBMjK+urNTUNvH+Zwc4FyXNZw0KcObZx6d3O8GorqGFNz/bR0R8DgAPLB/H3Yu77+wWSyR89edJ/jksVewWBnvy9N2TZJ4jRCIx7/9xjL2npR7xNQvGct/c3svL20Iv8MHGECSCQKCnPe89MBPVPg4LSMgp5vEfdlHT2IKloQ7fP7IACwOdPu17JRpb2njurwOcvihNJXl6dgD3DCDPVxaisgt48u99VDc2Y6Cpzld3zsVzkMb89hX/Lr0/PWU89469fUvvgiCwLT6JD46FUt/aiqK8PGvG+vHwOP8bFmV1q2GIeP4HIAgCP4VH8emJMwiAgYY6b0yfxDSXG2cUvx44n53Pwxt309TWflO61kGqtj70206icwpRkJfjpdlBrBjjOWiPn11Wxd3f/EN1Y/M1Tyz5aPtJNp6UXnzXzhvPPZNlKz//RuiFLJ7+bg8SQWCipx3vPzCr14tqfmk197+7mZr6ZmxM9fjymYU9zsi+kFbI4+u20dYuwtXOpKMjuXvlJyWzhLVv/kNjUxvmJjp8+NICbLrpRBcEgT82hfHb39IO+uFu5rz+/ByMDK9+HQ2NLbz51k5iYqWeQxcXU55aOw1Hh+5JVU5WOW+/tp38XGmnqp6+BnMXjmTOfB+0ephuJAgCedkVRIalExWeSWJsLu3tXbvLtXXUpeTQW6pS2juZoKDY/8VUZUU9ibF5JMblkhibR3ZG6VWxTWaWeviNdWDkGAdG+NrInK1+6bVHnc9i2+bzxERJyY28ghz3PRjE0ju6J34tLe38vTGMLVvPIxJJUFdX5tWX5zF61NVlbIlEYOuuSNb/HopYLMHGSp9P312Kgf7VyppILOG7Daf4Z590IbVguhdPPzBZ5jG96UA0X/99EkGAGQFuvPbQdJnbCoLADzvC+G2vNO3ggXmjeXDBWJmfyyWExGbw8s/SxdlYdxu+eHRen5XP3LJqHv52B0VVdRhqa/DnMysGNOVIJJbw0e7LE8wenDKKx2f0/tr7ioKqWh7bsJv00kqUFRX4ZtU8xjnJHvhwPfDv0vt8LzfenRvc7ylttxJK6xt461AIx9KknnYPU2O+WzwHE60bpyrfKhginrc5GlrbeGX/EQ5eTAekIbavBgeirXZ7emMu4VRaNk9s2UurSIyfjQWfL5l5Q0vrIFU6H/ljF+cz89FUVeHLO+cwyn7wiG9ZbQOrvt5CUXUdrhZGfP/AAvQHUIID2Hgylo+2nwTgrZVTmTe672X61Pwy7vv4H5pb25k12pU37pra6wm+tqGZ+9/dTF5JNS42xnz17EJ0hsm2HeQWVfHgm5uoa2hhtKcN762di5oMApSdX8Fjr22htr4ZT1cL3n9hXrf5nIIg8N0vJ/hnpzRyZsUifx64u/sJR7W1TTz/0hbS00tRVVVizeogZs/yQkHG+ww5ksjnHx2gpaUdfYNh3LM6kEnB7r2Ow7wQncNPXx0lLbmoy+2GxlqM8LGREk1vayyt9a+LgtNQ30LyhXwSYqTqampSYWcYPEiV1aV3jWPhyjE9ElCArIxS/vztNGdOSYPhxwY48dzLcxim2f25JSe3gk8/O0hSsnQs6ssvziEosPsQ+eSUIl5/bxfllQ2YGmvz2bplMn24e45e4OP1RxAEuHOBPw/dOUHmaz5+LpU3vtmPWCJwz/xRrFk6vsf3uPlIDJ9tPAnAWw9OZ8bY3sPao9MKePyrnbS0i1gy0ZMXVwT1+bssr21gzTfbySqpwtHMgN+eXMowtf5Nm7qEDadi+HiP1FLz0oIg7hjvNaDH6Q6NrW08t/kAp1KyUVFU4Pt7Fgzqua8vuNT1/u5BaUPhNDdHPl40A+VBjCC70RAEgcMp6bx+8Dg1zS3oa6jz1cJZ+FnduAl7twKGiOdtjKzKKh7btpeMiioU5eV5dWogd/gOnhp3s3A4OZ1ntx2gXSwhyNmOL5bM6rNHcbDQ2i7i8T/3cDY9F3VlJX65fxEjBrHkVNfcwj3fbiW9uAJrAx02PL4MvQFOWjqVkMlTP+1FIgisnTuee4P9+rxveU0Dq97fRFlNA/4ulnz9xIJeLQTtIjGPf7ydmNQCTPQ1+fW1O3oMhq+saWT1GxspLq/Dzd6Eb15ZKpN0FpbU8Mirm6isbsTV0YQv31gqswv6s++OsudAHACPPziJxfNGdvuYFRX1PPfiFnJzK9DWVuPD95fhJKN03NYm4oevj7F3p1Rl8/a14aU356MrI7fyEvKyy/nl62OcOy1VaJSUFRjhY8PIMQ6MHOtw3Yhmb2hsaCU+KpvI8AyiwjIo6yiJ6xtqcvdDQUyZ5SmTfIP0c963O4bvvzxKe7sYM3NdXnt3oczSu0gk5oOP9hNyIlmabPDUDGbO6P6cVFxayzOvbKGwuAYD/WF8+cEKLMy6twXsPhLPx+uPArBmZQCrFo6S+Zr3nLjA+z9Jt33+/iksmNzzOfHrf0L580AUigryfPP8YnyceycBIbEZPLd+L4IAzy0LZMUk2ekN/0ZRVR13fbqJiromxrna8OWavqum/8b3R87x3eFw5OTg41WzmDaAiWay0CYS8+RfezmVmo2akiLr71uIr033XuzriWMXM3hq6wHaxWImONrw1bI5qN7g68FgI7+mlke27iG1rAIFOTlemDKBu/28b1s7QX8xRDxvUxxNzeD5PYdpbGvDaJgGXy2ajY9F30LBb2Xsikvm5V1HkAgCMz2c+HDh9EHzUvYVbSIxT/69l1Mp1+eE29IuYs2PO4jJKsRAU50/H18+4HiUi/ml3PvFP7S0iVg41oPXlk/p88mrubWd+z/5h5S8MmxM9PjjhWVo9tJFKggCb/18mANnk9FQU+bnV5ZjbyHbKN/U0saj7/xDSnYp5sY6/PjmCpnl9fLKeh55dRPFZXXYWRnwzdvLelU65eTg+bUzmBncfYNIcXENz76wmeLiGgwMNPn4g2VYW3f/ektLannnte2kXiwGYOXd41h134QeiVlVRQN//nSSQ7tjkIgF5BXkmDHfh1WrA9G9wbmdvUEiEQg9lsSv3x6ntKOL38beiAcen8LIsQ49HjepKUW88+oOSktqUVJW4PGnpzNjtle324rFEr78+gj79scB8MjDk1m8sPvFUGVVA8+8+g/ZuRUYG2rxzcd3dGuTANi4O5LvNkgVvqcemMyiGbLJ3k/bzvLrjnPIy8nx4TPzGO8ju3tdIhF4+bt9hESlo6Whyq+vrcDKpHdf7B+Ho/hyx2nk5eT47JG5TBjRvf+4OyTllXD/F1ulqun4Eby8dNKASIcgCKzbEcKWsAsoKSjww4ML8B+k+e4gXYA/9ucewtJz0VBR5uf7Fg7qAryvOJORy2Ob99DSLsLfxoLv7pjHsAGkA9xKaG5v59X9x9ibJO0dmO3uzLszg1G/gWkCNwtDxPM2g1gi4avQcL4/GwHASEtzvlw467ZuILqEP8JjeP+Q9MKyyNudt+dOueHREyKxhGc27edYUgYqigp8d898Rtv3v9FHFsQSCc9s2M/xhAyGqSrz+6NLcTbr20i+f6Ooqo67P91MeV0jo12s+Pqh+X0m6WKJhOd+2MfJ+Ex0hqmx4cXlWBjq9LrfL3vOsX5HGArycnz+9AJGe9jI3FYklvD8J7sIj89GR1ONH99agaWMC3pNXROPvbaZnIIqzE10+Pbd5Rjodk/cfvv7DL9vlOYjPr92OrOmdj9JKTe3gmdf2ExlZQOmpjp8+tFyTEy6f48R4Rl88M4e6uua0dRS48XX5uI/5urmpEtoaW5j21/hbP3zLC3N7QCMnuDM/Y9Pwcrm1u5YbWsTsXdrJBt/DaWhTjov3tvfjtVPBGPvLDuip66umQ/f2U1EuNSjNm3mCB5/Znq3IzEFQWD9Tyf4Z6v0PHXPXeNZdWf3IepV1Y088cJG8gursTDT5asPV6Cv1/13/9OmM/yx7RwArz4+g+mB3VtKBEFg3Y+H2X8qCVUVRb55ZSnuDrIJU0tbOw9/sJWkrBIsjXX45bUVPVpHLj3Hu38dY+eZRNRUlPj1uaU4W/Z9+k9IfAbP/CJVTdfOG8+9U/peqbgSYomE5/7cz9ELGWioKPPbI0twtRi8KUTNbe088sduIrKklqNfH1iEm3n32a3XE1G5Baz5ezeNrW14mBnz050L0B2kVJGbBUEQ+DMqjg+OhSKSSHA2MuCbRXOw1tO52S/tumKIeN5GqG5q5tndBzmdJW2OuNvPm+cnB9xwRXCwIQgCX4aE8UOo9CJ112hvXpw28YbNW78EsUTCC1sOcfBCKkoKCnxz11zGO9kM2uMLgsDb246z7VwCSgoKrH9wAX4DVCdqG1u45/MtZJdW4WCqz29PLUOzH16xz7eF8ufRaJQVFVj/9OJew+FBOnv9tR8OAPDi3VNYGCR7dKYgCHzw81H2nEhARVmRb1+VfeFvbmnjiTf/4WJ6CUb6mnz77nJMjbpXgDdvj+D7X08C8PiaySye69vtdmnpJbzw0hZqa5uxtjbg4w+WYdDNyEuxWMKfv51m4x9nEARpmPrr7y7C2KT75xeLJRzZG8eG9SeoqmgAwMnNjAfXTmW4T/8aMNpa27kQlUNEaCpRZ9Op7Ygz6isUFORxGWHJqAnO+E9wwlAGqZaFutpmNv92mj3/RNDeLkZODibP9OTuh4IwkvH+JRKBTX+eZcMvoUgkAnYORrz+7iLMLa5OJhAEgb82hvHb76cBWLLIj4fWdK/slZXX8fjzGykpq8POxpAvP1guU+3+8tcTbDsQg4K8HB+8tEBm0oFIJObZT3Zx/kIOulpq/PjWHVgY68j8PCprG7nv7U0UV9bh6WTOt88tkjk96RLaxWIe+3Inkan5GOkM48+XVmDYxyEN0DX6bN1d05nl170ntje0tot46KedRGUWoK+pzp+PLxvQeF1ZaGxtY81vO4nNLUJbTZXfVi/G2XRgC+ZrQUJhCav/2klNUwu2Brr8smohZjq3//U+Kr+Qtdv3Ud7YhKaKCp/Mm06QY98V9NsNQ8TzNkFScSmPb99HQW0dqoqKrJsVzBwP2WHJtwvEEglv7w9hS5R0XN6Tk8ayZoL/Dfe6SCQCr24/wu6YZBTl5fnyzjkEug7uD/+7w+F8f+QccnLw6V2zB5zB19ImYs0324jPLsZYZxgbnl7ea9bmldgWeoH3/j4OwHv3z2C6f+/HUXx6IY98uI12kZiV031Zu3xij9v/uiOcn7aFIS8nxwdPzyXAt3v1UCSW8PKHuwiLzkJrmCrfrVvRbfc6wO4DsXz2rdS798BdAaxaNqbb7RIS83n5lW00NrXi5GjCh+8vRbub8n5NdSPvv72bmEhp9/ac+T489ERwtw1EgiAQGZbBz18dJTerHABjMx3ue3QyE6a493mRVFlWR8TpNCJCU4k9l0lLc1uf9usLbJ1M8A9wYtREF5yHW/RoEbgSJYXV/PZdCCePJAKgrKLIguWjWHbPeJlB8jFR2bz/5i5qappQ11Dh+VfmMG5C9wMAtu+I5NvvpcfbzBmePLV2WrevrbC4msef30hlVSMuTiZ8tm4ZGt1EXEkkAuu+Psjh0GRUVRT5+u1luMpY1DQ2t/HIO1tIyynDwliHH99aga6WbC91VmEl97+7icbmNmaMceXNB2V3xl9CXWML93y0hZySKlysjPjl2aWodaMCy8InO07x14kYqcf0ofmMdhlYB3l9cyv3freV1KJyrAx02PDYsgE3K3aHhpZWHvhlBwkFJehpqPHb6iWDNjyjP8gqr+K+DdspqWvARGsYv9y1EPvbdPzzlSitb2Dtjn3EFEitPo8FjOaxgNHI/wd9n0PE8zbAjgtJvHHwOK0iMVa62nyzaA4uxjd+tTnYaBOJeG77IQ4npyMnB2/MmsRyvxvfHCWRCLy16xjbIhNRkJfj0xWzBn1m8T9h8byzPQSA1xZNYunYgb1PsUTCs7/s48SFTDTVVPjtqaU4mPa9tBuenMsTX+9ELBF4eO4YVs8a3es+BWU13PfOJmrqmwn0deCDR+f0SLT2hybx7g+HAHju3sksDPbqdjtBEPjgu8PsD0lERVmRL99ciodz98rrkZAk3vtsP4IAK5eM4sF7uie+kVHZvP7mdlpbRYwYbsm6dxaj0U1OZHJiAe+8toOK8npUVZV48vmZTJ7a/Szx9JRifv7yKHEd8ULDtFS5474JzFni12uXu0QiIT2piPOhKUSEppFxsWu3u76RJv4BzvhPcMbCxqBfC66mxlZiwjOICE0l5UI+Esnl07OWjjojxzviH+CM7zhHNLV6L0mmJhXy05dHSeiIm9LWUWflAxOYuXBktyH8FeV1vPv6TpISCgBYsmI0968J6jYW6uCheD79/BASiUBQoCsvvTC72wlPOXkVPPHCJmrrmvH0sOCjt5Z0233f3i7mhfd3EhGfg46WGt+vuwNLGY1JFdUNrH5jEyUVdXg4mPL1q0tQ7cFHF5GUy9rPdiIWS/ocs1RQXsNdH2ympqGZQE97Pn5odp9tQhKJwEt/HOBwTBrqKkr8unYpLv0o2V+J8jppUkZhVR1uFkb8+vASNFQHzwtZ29zC/T9v52JRGQaa6mx4cCnWBr37YQcbxbX13L9hB1kVVeioq/LTnQsYbj44k5xuJtrEYt4/eoq/o+MBmGhvyyfzpt/2KTX/xhDxvIXRJhbz3tFTbPwPHoSNrW08vmUvYZl5KCnI89HC6czw6F4xuZ4QBIF1e06w6Vy8tBFh2Qxmeg7u6zh6IZ1nNuxDEODhqaN5ZFr3Sl1fXuv7W0/wz+l4lBUV+P7Rhfg69D2GIzW/jPs//oem1nZmjnLhnXt7V3NqGpp5oCM2ydXWmPUvLkW1BzUnMjGXpz7cgVgsYdUcPx5ZITv65pJfT15ejveen8d4v+5V0dCwNN58fzdiicCC2T6sfaj7LMfTZ1J59709tLeL8fez483XF1xFWgRBYOfWSH789jhisQRLK31ef3cRNnZXL+TEYgl//XiSTb+dRhBASUmBuUv9WXFfQI9ErqW5jagz6ZwPTSHydBo1VZdL6HJycjh7mOM3wZlRE5yxdzEdFHW/trqRqLPpRISmEn02nYb6ls775BXkcfe2wj/AmTFBrlj04EEVBIFzoWn88s0x8juC5G0djHjto2WYW15dTheJxPz8/Qm2b5HmYQ73tOSVtxag342t4eSpFN77YA8ikYTRo+1549X53fpD0zJKePKlzTQ2teHva8u61xZ0W/Juam7jsde3kJZViqmRNuvfv0PmbPecwkoefGMT9U2tBPo58u7anonhrlMJvPebVF1/Y/V0Zo3rPWYpPrOINZ9to00kZlmgF88vD+zzd9vWLuLR73cRmZ6PgZY6fzy9HPMBNhzmlldz1zdbqGpoZrSjFd89MB+lQYwgqmls5t6ft5FWUoGJ9jD+eHApFno3fhRzdWMzD/69k4TCUtSVlfh2xVzG2A2eH/9mYueFZF4/eIxWkRhLHW2+WTwH1/+A2HQJQ8TzFkVJnVR2jy2Uyu6PB4zm0f+I7F7d1Myav3ZxobAEdWUlvl4+h3H2NzagGKQX2Y8PhPLHmRjk5GDd4mnM8+n9AtMfRGbks+bHnbSLxSwePZzXF8sOwO4NPx+O4Jt9Z5GTg4/unUWwd9+jU0qq6rn7w02U1zQy0smCb57o/mJ+JVrbRDz2yXbi0wo7YpNWYNCDfy0jr5yH3tpMY3MbwWOcefPRWTKV0V2H4/jkx2MAPP/QVOYGd+8XjYjO5qW3tyMSSZg+xYMX1s7o9jGPHE3ko0/2I5EITAhw5pWX5l6l0rW1ivj4vb2cPC4dvTpxkitPvzir24lFdbXNfPDqdqLPSRtpAqd6cO8jkzAxl63uSCQSQvbF89tXR6gsq++8XV1DBZ+xDvhPcMZvvNN173YXi8Qkx+VxPjSViNNp5GWWdd4nJyfHlLle3PN4MPpGss+TYpGEQ3ti+OOHE9RWN6ExTIUX3lnIqPHdH3OnT6bwyXt7aWpqQ1dPgzffW4ybx9WLovMRmbzx1k7a2kR4eVnx7luLuv38E5ILePbVrbS0thMwxpE3X+o+cqiqppGHXt5IUWktTnbGfPP2sm7jtwBiLxaw9n2pXWT5DF/WrgqU+f4BvvnnNBsORKKoIM9Xzy5kpGvvpOZIVCov/iT1QT+9eAJ3BnfvQe4O9c2t3PfFP6QXVWBjpMvvTy8b8EjepPwS7v1uG81t7czwduaDO7r/3QwUlQ1N3PPjVrLKqzDT0eKPNUtuiteyobWNxzfvITwrHyUFBT5ZPINpbrf34JRLSC4p47Ftezvtde/OmsJcj4F5gG81DBHPWxCReQWs3bGfisYmtFRV+Hjuf8doXFJbz/1/7iCzvAptNVV+vHM+nhY3Pp5DEAS+PHKWn05GAvDmgiks8e95ZnN/kVpUzj3f/kNDSxuThzvw6V2zBtylv/tcEm/8fQSAFxYHsmJi33MD65tbuf+jLWQUVWJnqsdvz/cemySRCLy+/gBHzqdKY5NeXY69uWylrLy6gdWvb6S0sh4vF3O+fGmxTGJ76nw6r32yB4lE4L5lY7lvafelzPjEfJ57fSutrSICxzvz+vNzuvUG7t4Tw5dfSz+baVOH8+zTM67arrmpjTde2kpsdA4KCvKseWwK8xd3P9kpI7WYd57/h5KiGlRUFFn78hwmz5TdSAWQFJvLDx8dID2pEABDE23GB7szaoIz7j7WKN3E3MHigioiQlM5fyqFmI6OdFU1ZZY/MIEFq8ah0kOQfEVZHe++uJWLHeX0O1dPZOUD3Tf+FeRV8vZrO8jOLENVTYm33l+Cz0jbq7aLj8/jlde30dTUhquLGe+vW4JWNwpyVGwOL765nXaRmKmT3HnpqZndPm9+UTUPv7KRmrpm/D1t+PClBd1aAwCOhF3kjW86iOHdQSyZ5iPzvUskAq/+sJ9jEWkMU1Ph51eXY2feu5dww5Eovtgubaj68MFZBPv2fYFYWtPAPZ9tpri6nhG2pqx/bBFqA4zXCUvN5dGfdyGSSLgzwJvn500cVO98WV0Dd/+4lbzKGiz1tNmwZilGWjc+QqxNJOLZ7Qc5kpyBvJwcb86ezNKRg3suv1moaW7hmV0HOhuKV4304sUpE277huIh4nkLQRAEfouI4ePjpxELAs5GBny7eA5Wujo3+6UNCrIqqnhgww6Kausx1hrGL6sW4mB0c0zh3x0/x7fHwgF4ZW4Qd4zxGtTHL6isZdXXm6mob8LXzpz1Dy4ccAj+2eQcnli/C7FE4N4pI1k7L6DP+7aLxDz+9U4iUvIx0NbgjxeXY6rX++/ju21n+H1fBAoK8nz5zEL83WSrPY3NbTz89mbSc8uxMtXlx7dWoC0jiib+YgFPvbWVtnYxc4NH8Nya4G4vhhfTinn65S00Nbcxxs+Od17pnkxs3BTOz79Ku4IXzvflkYenXEVOGupbeOX5LSQnFKCqpsTbHyzF29em29d3dH88X72/j7ZWEabmurz20VLsnWR7x0qLqvn1iyOcOiRtjlPXUGHF6kDm3TmmV//nzcDF+HzWf7SflA4iaWSmwwNPTSNgqodMUtLeLmb9Z4fYu006Icp/nCPPv72gW7tBc3Mbb72yneiILJSUFHj1nYWM7UYlTUkt5sWXtlBX34KdrSEffbAMvW4ilM6Ep/P6e9Jjf+5ML55+RMbxklHM469voaVVxLQJbrzyuGyF74/d5/lhyxnk5OCDp+YxYaTs2KzWNhGPfrSNCxlFmOpr8ctrK3oclgDS8/hHm0+y5WQcyooK/PDUIrwc+p4DnFVSyT2fb6GuqZXA4XZ8cv+cAQfM749J4cW/DwKwduY4HpjsP6DHkYXimnru+fEfCqrrsDPU47fVizHQvPHRfmKJhLf2hfBPtPR3+PSUcawe7/efCGQXSyR8HXqO785K7Sw+FqZ8sWA2JjeB5A8WhojnLYLm9nae33OIwykZAMxxd+GdmVP+M2GyF4vLuG/DDqqbmrHR1+WXuxZifpNiMH45Fclnh84A8NzMCdwT0PdyWF9QUdfIqm+2UFBZi6OpAb8/ugStAfpyL+aXct8XW2lua2eWnwvv3Dm9zyUzQRB4848j7A1PRk1FiV+eXYqLVe9NC7tPJbCuw9/2+gPTmD1e9vhNsUTCcx9Lszp1tdT56a0VmMuIrMkpqOShlzfS0NjKeD973n2u+/Jpdm4Fjz+/kfqGFrxHWPHhm4u69QL+vuE0G/6Uzmi/c+VY7r074KoLTV1tE88/uZHM9FKGDVNl3SfLcfO4mgSIxRLWf36Y3VukkV5+Yx144Z2FMr2conYxm38+xT+/htLWKkJOTo5pC3y4+/HgfpfSBUEg62IxVeV1/dpPQUEeV29r1LppnuoJEomEkwcv8OvnR6gokz6nu7c1a9+Yh5Wd7OPj6L44vvpgfycpf/vzFVjZXu07a2sT8d6buzgbmoq8ghwvvT6fwMlXW1iys8t57sXNVFU1YmGuyxefreyWfB47mcy7n0g90isW+fPQfYHdvr5zsdm88L60KWjlfH8eXtW9v1gQBD78+Si7O6K+vnttGW72shcXNfXN3P/uJvJLa6Q+55eW9ticBB1NgD/s5VR8FtoaqvzxwnKsjPvehBObWchD326ntV3MkvEjeGXZ5D7v+29cOVrzWhobZaGwupa71m+lpLYeB2N9fl+95KbkawqCwBfHw1h/Wvobvm+sL89NvfqccLviWGomz+89RENrG3rqany9aPZtO2pziHjeAmhub2fNP7s5l5OPkrw8LwcHcofviP/MD6a0roHFP26kvL4RdzMjfrpzAXoagxfz0R+EJGfy+J97AFg7dRwPBg2uAiCWSFizfgfnM/Kx1Nfmj8eWYjjAlWlLm4ilH/xJXnmNNCB+Tf+aBA5HpvLSzwdQkJfji0fnMc7j6rLnv5FfWs0dr26gtV3cp47ezQej+fLPk71ewEUiMatf/Jv07DKGO5vx+RtLum1Samlp58EnN5CbX4mbixmfvrOkWw/g2bA0XntjBwAPrg5i+dKrRyiKRRJeemYTsdE56Ohq8OHnK7BzuDr4WiIR+GLdHg7viQNg5QMTuHN1oEyCX1xQxYcv/NOpGg4facNDz8/C3qV/lpHK0lpCdsdydEcU+Vf4MPsDVXVlxk0bTvBCX4b72yHfDytHS1Mb2/44w9bfTtPa0o6KqhIPPT+T6Yu6tyCA1Ibw9vP/UFpUg66eBh+tv6fbwHyxSMIn7+/j2OEElJQU+PSbVbi6X034CwurefaFTZSW1uHtZc1HHyzr1k6x73A8H391GIB3XpnPhLHdl68Pnkhk3TfSRIXPXluMv5dNt9uJxBKe+2Qn5+JzsDTRZcMHq3okk3kl1dz/7iZqG1q4c8ZInlgmu2nuEprb2lnz6TYSc0oYbmvKr88v7ZfV5sqA+U/un80Ur4F7F786eJafjkWgKC/PhseXMdxqcDvAcytquOenfyira2SMgxXr711ww4d/XMJvYdF8eDgUgJenT+SuMbLtFLcbcqtqeHz7PlLKylFVVGT9snmMsbn9GqqGiOdNRlNbOw/9s5tzufloKCvx47L5t+0qpju0tItY9ds/JBSW4mCox6YHlqOp2j+FZrCQW1HD0m/+pqG1jZVjvHh5btCgP8fPxyP48sBZ1JQV2fLUSmyNru4E7is+2xnKhpBoDLU12P7yXWj14su8EtUNzSx64w9qGppZM3s0a+b03kkvCAKPfLiV6JQC/N2t+PrZRT0uforKaln5wu+0tIp44f5g5k+W7YP87Z8wftkShramGn9+cY/M7uPPvj3C7gNx6Otp8Os396LTTf5mWVkdqx/6lfr6FpYs8uPhh7pXg9Z/c4xtm8+jqqbEVz/cg6391Wre/9o7y/imzjYOX0napO4OLRR3d3e3MWDIkDmMKRuDubuPwXwwNtxtuLu7tLRQ2lJ3byPn/XCS0pecpEmFdpDr9+sHyJOTJ3r+55b/rdMJzPtsM/+uO4NcLmPOh6PpZcJWCWDPlnP8+NEm8nILcXF14Lm3RtBzUHPLu5cL1RzddZlda09z5lB4sQWSUmVHcF0/qy42s9JzSdKPvgTwq+FJ31Ft6PdQO4JqWV7CkpyQwbfvriuu/+zWrykvvjsSVxOjTTMz8pj77GJuXE/Ey8eFr36ZRo0Q48fT6QQ+eHM1hw+G4+3jwoI/nsBLIhocHZPK9GcXUVCg5onHejBpovTFzk9/7mP5mhO4uTrw54+P4SvROQ/w7e+7WbP1LH4+rvz97TRJL1CA7NwCJs5eREpGLpNHdODZ8eZLWA6eu8Er361HLpPx5zsTaBJaunhLTM9mzHuLyS0o4tVxPZnY1zoR9OPmw/y+/QRerk6seWMKnqVMUzKFIAi8+vcWdpy/TrC3O6tmPVqhNksA1xNSmLBgGflqDU/36sCLA7tW6PGt4c/Dp/lixwHkMhm/PDqK7vVqV9leKpoCtYbn12xmf+RNHOzs+HncSLqE/rfEp014ViF5RWqeWbme47dicVYq+WPCQ/fFvHUDgiDw6pqtbLkYhrujA6uenkBIFY0Cyy9SM/Gn5YQnpNC6VhB/PjkGZQVajABcjE5gyrwVaHQ6PhjXn4c6mhYwpXH+ZhzTvl2BIMAPz4ykRzPrmsve/GMrW09co16QN0venGRRpHT9vgt8smgXDko7ln00hRp+HibXCoLAi5+u5uSlaNo0CebHN8eaFE3Xo5J48rV/0Gp1vPfyMPp1kzasP3w8gjc+EKOYX300jvataxut0Wp1zHp1KRcvxdKwQQA/fDdZsvZzz45LfPrBBgDe/nA0PXobd4MKgsD8L/5l0+pTyOUyZr//EH0GSTcl5OUWMv/jTezefA6AZm1q8dqnY/EL9JBcf/fjXDsXza61p9i/5Ty5JayOmrStTf/Rbek+uAXOEpN6Sjvu1bO32Ln2FAe2XCAv585xm7ULpd/odnQf3BwnEybwJdHpdKxdfIRFP+xEo9Hi4+/GnE/H0lyiOQhE8fna9L+IikzCx8+VL3+ZRpDE9KK8vEJeeHoRt6JSaNq8Jl/+8Kjk+7V9x0U+/3ILcrmM776eRDOJjni1Wsuzr/xDeGQibVvW4quPxklGpfMLipg66y/iEjMZ3q8Fc2YMMPm895+8ztxvN6KQy/jjo0k0rG1+FORbP21hx/Ew6gX7sPjdSZJepHdjGNjgoLRj1btTqOFjufVQkVrDhC+XEhmfyqC2Dfls2hCL73s3WfkFjPn6H+LTsxnetjGfTBxU5mOZYsu5a7y2QqwpnTd5BH2a1K3wx7AEQRB4c8NO1p69jKuDihVPjaeOT9mDANWNIo2G59ZsZl/ETVR2Cn4eN5KuoffeGaasWKPXqiZufp+SV6Tm6RV3ROef95noBPj5wAm2XAwTJwE9MqzKRKcgCLy/bhfhCSl4uzjxzcShFS46cwoKee2ff9HodAxs2YBRHUzXRZZGQZGGd//ZgSDAsA6NrRadBy/eYOuJa8hlMt6dOsAi0ZmcnsMPK8RO3GdGdzUrOgG27L/MyUvRKO3tmPukdMMHiCn2T37chlaro0fH+vTtKu2RmpqWw+ffiSescQ+1kxSdAIv/PsTFS7E4OSl5682RkiImIjyBrz/bAsD4yV1Mis6fv9nOptWnkMlg1jsjTYrOsIuxzBw3n92bzyGXy5j8bB8+//3xUkVncnwGK37ew9ODvmLWuPn8u/w4udkF+AZ6MOHZvvy+czZfL3+WQeM6Wi06QbRGatKmNi9+NIalR99mzjcTaNOtATKZjEunbvLdG6uY2PlDvnx1OWePXEen05k8llwuZ8y0bnz7z9PUqOVNSmIWc578k8Xzd6HVaI3Wu3s48fmCKYSE+pCSlM2cGYtJKBF9NeDkpOK9T8fi7KLi8sVYFny/Q/LxB/RvRr++TdHpBD76dCNZWflGa+ztFbw1exgqlR2nz99i5bqTksdydFDy+kxRVG3adYET56JMPu+e7evTp2MDtDqBT37dgUZr+jUCmDWpN27ODkTEpPD31lNm1xoY3a05bRvUpKBIw0f/7MKa+I3S3o73Jw1ALpOx7XQYe85HWHzfu3FzdODzSYORy2RsOn2VTaevlvlYphjaqhGPdhEdN15fuY1bKekV/hiWIJPJeG9YH1oHB5JdUMiMpRtIzzP+TP1XUdrZ8ePDw+hdL5RCjZbpKzdwWN/5fr9hE54VRG5REU+tWMeJ6FhcVEoWThxN6/tMdG66cJXv9xwB4M3BvegUWraZ5BXBsmPn2XTuGgq5jG8mDq0Uy4+P1+4hNjWTIE833hlbdq9OgJ+3HiUqKR1fN2dee7iXVffNyS8sHoc5qV8bmtYuPR0oCAJf/L2bnPxCmoT6M36Aeaum1IxcfvhnHwBPjelCcIDppom/1x7n+s0k3FwcePXpfpKviyAIfPH9NjKz8qkb6stTU6Xr586eu8U/S8XP1KyXBlFDYlJNZkYe772xmqIiDe071WXak8YTjgRB4Lfvd7J+udgl+vJbI+g/VLrhYs+Wc8ya+ivxMWn4Bbrz5cInmTS9DwozYv78sQjefOx3pvb8lEVfbyP2RjIqB3v6jmrDJ389xaJ9c5ny8kBq1K44Q2iVgz29hrfm44VP8tf+13ns1cHUrONLYYGaPRvO8MbU35jW6zOWzNtJUaHa5HHqN6nBjyuepf/I1uh0Akt/2ceb0/+iIM94tKeHlzOfL5hKzVreJCVk8tqMv0hKyDRaVzPYi9ffHYVMBpvXn+HfjWeN1shkMl56YQA1gjxJSsriq2+2Sgq0WsHePPdUHwB+X3yQyJvStbGtmwbz8GDxc/zZT9vJzSs0+ZxfmdYHV2cHwqOSWLbFvJj0cnNi1sReAPyx4RhRcWlm1wPI5TLentwflb2C41ej2XDkcqn3KUmzWgFM69cOgI9X7CYzt6CUe5imdWgNpvcX66E/WrObmJSMMh/LFK8O6U7rWkHkFBbx0pLN5BWZ/rxVJko7O+aNH06Quyu3UjOYuWwjhWpNleylMlDa2THv4WH0qV+HQo2WZ1Zu4NB9KD5twrMCyC0q4qnl6zkZfVsUnRNG06rGvfexrEyO34zhjfViZOOxLm2Z0OHej8E0cO5WHJ9vETs6Zw3qTrvQiq+f3XTqCptPixHGzyYNKnMHO8DFqHgW7z4NwJvj+1pV1wnww9pDJKbnUNPXnekjLJuQtOfUdfafiUShkPPm4wNKbQr4etFusvMKaRTqz/ghph0BrkclsWj1MQBefrKvybrOTdvOc+zUDeztFLz16jBJ/8+MjDw++VRstBgyqAV9eht3SWs1Oj56dx2JCZkE1fTk9XdGGjWqCILAwvm7WbNEtNJ64fWhDBwhLbT3/nuer95cg1ajo1v/pixY9RxNW5tOZ2k1WhZ9vZXXp/zGmUPhCIJAs/ahvPTJWJYceZtXvxxP6y71rWoAKgu+gR6Me6Y3v257lW9WzmTIhE44uzqQHJ/BPz/s5KUxP5ptZnJ0UvHKhw8z9/NxODmrOHfiBu+98I/kXHkvHxe++GkqQcFeJMZl8NqMv4o75UvSsXM9pj3VC4B532zjyqVYozVOTirefmskdnZyDh0OZ+MmY4EKMHxQS7p0qItao+Wjr7ZQZEJMTH+0O0H+7iSlZDN/8X6Tz9fL3bnYTP73NUeJjjcvJgd3aUzn5rUp0mj5eNHO/xtVaooQPw9mjBBrV79ZdYDkjJxS71OSZwZ3ok6AF6nZeXyxZq9V972bp/p1pE1oEHmFauYs2YpaaxzRLg/2CgXfTByKt4sT4QkpfLDOuihvReLj4swvj47C1UHFmeg45q7bbtH79V9BaWfHDw8Po2/9OhRpxcjnwcioqt5WhWITnuUkp7CIJ5ev41TMbVxVKhZOeJiW95nojExO5fnlm1BrdQxsUp/Z/S33nKxoUnPymLV0CxqtjgHN6jO1W8V3N0anZPDRWnEG+4yBnWgdarlf390UqjW8u2QHOkFgSLtG9GpuXX3U6fBYVh+4AMDbk/tbZDydmZPPl3+L+586pD31g81H4faduM7eE9dRKOS88fQAk/6CGo2WT0uk2E3VdcbGpTP/N/FE+tS0HtSRiALqdAKffbGZ1LQcaoV4M/PZfpLH+u2n3Zw7HYWjo5L3PxkraYW0+Jd9rPhLtGCaOXswQ0e3kzzWgR2X+PLNNeh0AoMfbscbXz6Ci5kxmUlx6bw26WdW/LwXQRAYNK4Df+yaw5dLZzBwbHucXe/9mFuZTEbj1rV4/oPRLD36NrO/Go+Htws3r8Xz/EPfs3Ot+eher8Et+OjnqTg6KTl34gYfvLREMlrq7evK5wumEBDkQXxsOq/N+IvUlGyjdRMmd6F7r0ZoNDref3MNKRJrGtQP4JmnxKa/BT/vJjIyUfJ5zX5hEO5ujtyISubPvw9J7t/RQcncZwcCsHHnBU6ejzL5XId0b0LH5rUoUmv49DfzYlImkzF3aj8cVfacD7/N2r3nTa4tycS+bWhSy5+c/EI+W7bHKjGmKpFy33LyGvsvRlp837uxU8j5bNJgXB1VXIxOYP62o2U+lin83Fz4ZuJQFHIZm85dY9kxy16jyqC+nw/zHhmGvULO1svhfLNL+vPyX0WpUPD9w8Po26AuRVotM1Zt5MB9JD5twrMcGETn6Zg4UXROHE3LGhVraVHVJGfn8tQ/68kqKKR1cCCfj7bcc7Ki0Wh1vLrsXxKzcqjj68VHYwZUuD2VWqNlzj//kleopm2dGjxVTnPmn/89yo2ENLxdnXhtTC+r7ltQpOGDv0XvzdHdm9O+oWWlDT+sOEBaVh61A714fISxJVFJsnIK+GqRmMZ/dFh76tcy7fn4z7oThOtT7K88JZ1i12h1fPz1FgoK1bRuEcLYkdIicM3ak5w4KRqSv/3WSBwlRiLu3HaRNXoPztlvDZecvb5941mW/iHarDzz8kBGjJN+vw7tusxnc1ai0+oYMKoNz789wmyU8uiuy8wc8R1XztzCycWBN354lBc/HmNVZ3llo1TZ02dkG+ZvfImWnetRmK/mmzkr+Wr2cvJzTaehm7QM4cMFU3BwVHLmaCQfvLSUoiLjCKNfgDtf/DwV/0B3bken8d4ryyks+H+RKpPJmP3GcGqH+pKWmsOHb62RPNboh9rRqVNd1GotH3y8gXypSKunM7NfEOs4l689wflLMZL7b9Ms5E7KfYHplLtMJmPOk/1xVNlz7losG/ZeMPmaAAT6uPHsmG4A/LjqIImpxiL6buwUct6Z0h87uZy95yLZfeZ6qfcpSfPagUzuI148f7R8Nxm5Za9ZDPR04/1x/QH4c+9Jjl+PLvOxTNEutCazBomBh8+37OfcrbgKfwxL6VQnhA9HiM/398OnWH6y6oRwZaBUKPh+9FD6N6xXLD73R9ys6m1VCDbhWUZyCgt5cvlazsTG4eagYtHE0bQIur9EZ16RmulL1xOXkUUtbw8WTBiJQxWOCfx+x2FO3IjBSWnPd48Ow1lVsdYhAD9uO8KlmETcHFV8NnFwuXzrTl2PZdFuMQL15iN9rZ7R/Mumo8QkZeDn4cKLD1sWZT5+6RabDl5GJoM3H+9f6uz2H5fuJzUjl5BATx57qJPJdWKKXYyivPRkX7w9pVPsy1Yd58q1OJydlCbHIYaFxfPbH/sAeHZGX+qEGovd62HxfPeFOAZx4pSudO9pHF29fD6aHz7dDIg+naMnSu//6N6rfPraCnRaHX2HteLFd0eZFJ1FhRp++mADH8z4i5zMfBq0CObHDS/SfbD58ZpSxEQkcv7Idav+Lh2PpKjAuvo5Lz83Pl74JFNeGohcLmP3+jM8/9D3RF4xLQqatanNB/Mno3Kw59Th63w0axlqifS2f6AHny2Ygqu7I+FX4vj+k81GUT1HJyXvfToGFxcHrly6zfxvtxsdRyaTMefVoXh7uxATk8aPC3ZJ7qt75/oM7t8cQYBPvvnXpKh8ZlJ3Av3cSUzJ5se/TKfcA33deWacXkwuPUBSKWJyTN+WNK8XSF6Bms8WW5ZOblDTl8cGtwfgs2V7ra7XnDGkC6H+XiRn5fLR8vKlsPu3qM/DnZohCPD60m2k51R8883Ubm0Y0Kw+Gq2Ol5duJiU7t8Ifw1JGtWrC873F8qMPtuxlX/iNKttLZaBUKPjuoSEMaFgPtVbLs6s3se8+EJ82O6UykFNYyBPL1nH2drxedD5Ms0Dzlh3/NTRaHc8t38i+8Jt4Ojmy/Mnx1PL2qLL97Lx0nZeWiCLjm4lDGdjc8lnJlnIsPJqnf12DIMC3U4fRr0XZzZ2z8goY99k/JKRnM6pTU96bZNr+RYorUQlM+Ww5OkHg22dH0LNl6Sn6/EI149/8i/iULMb2bcXsyX3Mrj956RYvfLIagJ/feYSWjaRrZdVqLU/O+YfIW8l071CPT14bKRntDLuewIxXRIulN14ZysA+xi4AubmFPPPsQuLiMujerQHvvfOQ0bEy0nOZ+eSfJCVm0bFLPd7/dKxRXWdSQibPT/2NjLRcuvVpzJufjpUUuccPhPHhS0vRaLT0HtKCVz8eI2lmDnA7KplPX1xSLNhGP9GDabMGYW/FmMy8nAL2bzzDtqVHCT9ftoiTi7sTfUa3Y9CEzoQ2tq5B8dLJm3w+aykpCZnYK+146vVhDJvU2WRm4NzxSN557m+KCjV07t2YN78aj52Eq8C5kzd5/fm/0WkFnnyhH2MnG/s5njwWyZuzlyMI8OKrgxk2yrgM5vz5aF55bRk6ncCbrw+nr9RnJK+Qx59bREJiJkP6N2fOS4Ml9372cgzPv7MCgK/eephOraVtorQ6HdPfW86liHi6tanDF6+MMpspuXE7lcnv/oNao+XD6UMY2Em6pKQkRWoNEz9ewo34NIZ1aswHj1lna3QlOpEpXy9Ho9Px3sT+jOpcdtu2vEI1479bys2kNHo1qcMPj4+o8MxQbmER4+cv40ZyGu3r1OT3xx8u8wjQ8iIIAm+s38G6c1dwUtqz+LGxNAu6v87Haq2WWev/Zfu1COwVCrH7vb51ziiVjc1OqRLJKijgcb3odL9PRacgCHyydR/7wkU/sQUTR1Sp6LyZnMabq8XGpmnd21aK6EzLyeONZdsQBBjTqXm5RKcgCHyyYg8J6dkE+3pYnWJXa7S8t3gnOkFgUPuGFolOgJ/XHiY+JQt/L9filKEp8gvUfP67Po3fv6VJ0QmwaPVRIm8l4+7qaHIOe2Ghmo++2oxWq6Nn14YMkGgUEgSBb7/fTlxcBn5+brw6a4jRsTQaLR+9s46kxCxq1PRi7tvGzUQFBWren72CjLRcQuv78+q7oyRF56nD1/noZVF09hjYjFc/etik6Nyz4QzPjxKjhG6eTrz/62M8NXeYRaJTEASunoniu9nLmNT2bX6Ys4Lw89HY2SsIaRBALSv+PP3cyMnMY+PCAzw74HNeGv4N25Yd/T8/T3M0ax/K/I0v0bFPY9RFGha8v56PZv5Ndmae5PpWHevy3g+PYq+04+jeq3w2ZyUatXFjSqv2ocyYJYqpP+bt4sRh45Ry+051efxpsZZz/nfbuXTBOFXesmUIj04SG3K+/X47t28bW/M4O6l4Y9YQZDL4d+dFDh2VTl+3bhrMmCGiuP18wXayTUQaFXI5r+trlw+ducHuY2GS6wzUqeHN48PFEpWvl+wlI7v0qKHS3o53pgwQu/yPXeXwpahS71OSJiH+zBwmvi6fr95HdHKGVfcviZPKni8nD8FeoWDflRssP1zxKWhnlZLvHh2Gk9Kekzdi+X7H4Qp/DEuRyWR8MKIfXeqGkFekZsaS9dzOsG5UbXXHXqHgm1FDGNSoPmqtludWb2JnWNltuKoaW8TTCtLy8nl86VquJCYVi86m95noBPjj8Cm+3HEQmQy+GzeMgU3KLsLKS25hERMWLCMyKY12oTX444kxFX5lLQgCz/+5gf1XblLH34vlL020qInHFFtOXuXNxdtQyGUsevkRmte2rtns9y3HWbDxCB7ODqx5fyqerqWPIr18I54nPhQjpN/NeoguLcyP0vz+730s33oaf29Xlnw+1eQkmKsR8Ux/fSlancAHrwynTxdpz855v+5m9YbTeHs5s3D+47hLNO1s3XaBL7/+VzQU/2YSzZoai92f5+1kzYoTODoqmffrNGrdNTdcEAQ+fXMN+3dext3DiR/+eoqAIA+j45w5FsG7z/2DukhDt35Nmfv5OMlIXkFeEQs+WM/ONWJJRPMOdXjt6wn4BJRuCJ6dnsvutSfZvuwYUWHxxf9fs64fA8d3pt+Y9niYmMRjCq1Wx7mDYWxddpRjOy6i1YgelI7OKnqOaMOgCZ1p0Cqk1AiWIAisX3SIP7/8F41ai1+QB3O/m0RjEx38pw6F8/6LS1CrRZE+59OxRvZSgiDw/Seb2br+DE7OKr5f9KTRaE1BEPjonXUc2HsVTy9nfvrzCbzveg20Wh2vzF7GhYsxNKgfwLzvpQcG/LJwP0tXH8fD3YmF8x/DS6K8o6BQzbRXFhMbn86gXk1463nTZux/rD3K76uP4OnmyJIvpuHpZvp7pdZomfLeP0TGpjKocyM+eMYyk/evVu5j6e6zBHi5surdKVZNEtLqdDw9bw2nI2JpViuAhS+Pw15Rdm/iJQfP8tn6fSjtFCx7aSINAo3HoJaX7RfDmbVU9Nf9btIw+jerunNFTkEhE/9cSXhiCvV8vVj6xCPlciOpjqi1Wl7dsI2tV8NRyGR8NWowQ5tI/ybfa2wRz0ogr0jNtCVruJKYhLezE38/Ova+FJ0Hr0fx5Q7RdHzOgJ5VKjoBPt20j8ikNHxdnflqwtBKSeesPX6J/VduorRT8MWjQ8olOpMzc/hkpdhR/szgTlaLzqiENH7dItoVzR7f2yLRqdMJfLJwlxgh7dyoVNF5JTKBFdtEe6fXHu9nUnRqtTo+nb8drU6gb9eGJkXnxSuxrN6gP96LgyVFZ0JiJvPmixHWx6f1kBSdp0/eKG4mmvP2CCPRCbBhxQn277yMQiHnrc/GSorO61fieP+FJaiLxPSxKdGZkZrDrEfms3PNKWQyGZOe78eni58uVXRmpGTz2wfreLT9u/zy3jqiwuJRquzpO6Y9X65+gV/3vsGY6X2sFp0ACoWctr0a89Yvj/PPyQ944s0R1KjjR35uIduWHeWlEd8wa9R3nDlwzWwtoEwm46HHuvP1imcJDPEmKS6DVyf8xJ4NZyTXt+vWgLe+nYidnYID2y/x4yebJI8587UhNG0VQl5uIe+/upyiQo3RmldfH0btOr6kp+UWm/7f/RzffH04bq4OhF9PYLGJDvbHHu1K3VBfMjLz+O4n6ZpQB5U9bz43CJkMtu27wvGzpuvfpozoQJ1gH9Kz8vn+730m1wGiDdjjepP3o9c4cdkyL8WZI7sS5O1GQlo289dbFwVUyOV8NHkgLo4qLt1KYNEuy8zsTTGxWyt6NA6lSN8wWZqRflkY2LwB07qL9mtvrt5BXBVGGl0cVPw6aRR+rs5EJKcxa/W/VWb5VFnYKxR8PWowI5s3RisIvLJ+K7vCyu6GUFXYhKcFCILAu9t2cy0pGR9nJ/55dCyN/CvOKLq6kFtYxLubxB/48e1aMLWzedPxyuZSbALrTovGzF9PGIKvq3RDS3koUGuYv13v/zi4Kw2Dyve+/rnzJLkFRTQN8efx/tZ3xP+86SgarY6uzWozqL1lV7L7z0ZwPSYZZ0clsyaWPqv+t9WHEQQY2LUxXVqbrhPaezScG9EpuLo4MOspabsjgD//EYXDkP7N6dRO+nh/LT5EQYGa5s1rMv4R4yYgrVbHz/PEz97Ih9vRtYfxc09Nyeavn/Wi/uUBtGhb22hNQV4Rn81ZSWGBmnZd6/PGV49Iis70lGzmPPoLN6/F4+njwmd/P82jLwwwmYoHcY76n59uZFqXD1j72z6KCtXUaVqDmR+NYcnpD3j120dp1rFuhdXTefi4MmZ6X37b9wZfrHqevg+3R6my59qZKN6c9BOvjZnHhaPm020Nmgczb/2L9BjSAp1Wx1ezV5i0XOrYoyFvfj0emUzG1tWnOLjjktEae3sF73w+Di8fF2JvpbJ6yRGjNY5OSt76YDR2dnJOHovk1Anjhg9fXzdmvSzWbq5ac1LShklpb8ebrwxFJoP9h8OIuCHtU9q8UY3ilPvvKw6bFBv2dgreeka0Ytpx5Co3YlMk1xloWieQh/uIfsW/bThmkYhxVNnz9mTxu7L6wAXiUozN980R6OXGXH1pzl+7TpXLWF4mk/Hh+AG4OzkQkZDKljMVP9UI4OWB3WgVEkhuYRE/bK+6lDtAgLsrv0wahcpOwaGIW2y8UDnPuSqxk8v5fPhAxrRshk4QmLNpO9HpGVW9LauwCU8LWHH2IhsuXkUhk/H96KHUvY/mw5Zk3t6jxGVmE+ThxmsDe1R4Qbo1CILAV/+KkdfhrRrRthJM4gFWHDlPclYugZ6uTOhWPlP8+LQsVh++CMCLI7pZHZ0Nj01mx6lwZDJ4YXR3i15/nU7gt/WicB7XrzUepYxpvBwRz7HzUSjkMp4c08XscRetEo/7yPC2uJs47sUrsZw5H42dnZxpE40bTgCio1PZuUsUMdOf6iNZj7lz20WibiTj6urA1Cekpxz9+eNu8nKLaNi0BsPHSov6n7/Ywu1bKfj4ufHap2OxlzKuT81hzqO/EB2RiLe/O18um0GLjubraDf/dZBpnd9n1YLdFOYX0aBlCB/9PZ0ft85m2NTuuLiXHpkuKzKZjOad6vHqd4+y8Mg7jHyiJ/YqOy6diGTOuHm8MXEBGammzcudXR2Y8+1EhkzoJNbZzl1lUnx27t2YcY+LDgrfv7+e5IQMozUeXs489aLYLLd84SHJyUa1avsw8mHRSuu3BbslPTS7d2tA0yY1KCrSsGSptO9k3VA/encXm3sWLzftTTl5dEdUSjuuXk8wO06zcZ0AerWvjyDAwrXHTK4zMHVYe+ztFJwPv83Jq9L2TnfTsXEtOjUOQaPV8du/xy26T0mGtGtMgyAfcgqK+Gt3+aKeXi5OPN5bfB9+2nEMtcSo1PJip5Dz+vBeAGw6d43Lt419Wu8ljQP9mNFTvLj9bNsB0sthUVVdkctkvD+4D21qBpFdWMhzqzdT8B+a4GQTnqVwKT6RD3fsA+CV3t1oH1I5AqiquRyXyOJj4lSR94b1wakc6eaKYO/VG5y8GYvKTsELA6UFTXnJLSjij93ibOgZ/TuhtCufVdTvO06g1mhpV78mHRqGWH3/nzeKJ9YB7RpSv4Zl9Vh7T18nIiYFZ0clEweWbqb/xxrxMQZ1b0JNfw+T6/YfDycqNhUXJxVjBps+7uJl+uP1bYa/n3Rdz6LFB9HpBLp0rkdjiS7tggI1f/0uWuJMmNJV0iT+8vkYdm0RmySenT1YUrwe2nmZbWtPi96Sn4zBzcNYDOp0Oj6ftZSYyCR8Atz5YskzpY65XDl/J/PfWk1+biF1m9XkvYVP8d2mWbTt1fieX5x5+bkx/b3R/HHwbYZN6YadvYKzB8N4/ZEfyZCIGhqQy+U89/5DDJvUGUEQ+OGtNYRJNP8ATH62Lw2a1SAnu4AvXl+NViJF23tgM5q1DqGwQM1v3++UPM7EKV1xdlFxIyKJ3RLRU5lMxhOPixcZW7aeI0FC5AJMGS9eIO0/HMbNW9JRSi8PZ0YNEC8cF606ajY6+fhoUZTsPh7GzdhUk+sA/DxdeahXcwB+XXvE4tTtjJHinjcfvcKtROtmm8vlMmYMFe+/dP9Z0rKlG8MsZUK3Vni7OnE7LYt1J60b7WkpzWoGMKyVeIHw5ZYDVZ7ifrxLW+r7eZOel88XOw5U6V4qC3uFgu8eGoqXkyPXkpL5YHv5pl/dS2zC0wwZ+QW8sGYzaq2Wvg3q8kQn06ME/8totDre2STWCA5p1oAe9c3XCFY2aq2Wr7eK0c4p3doQ5FE5jWVLDp0lPTefEB8Phrcz7sK2htiUDDYcFX/UZw41HUk0xeWoBPadj0Quk/HMMNN+miUpGe2cMKAN7i6lRzuPnr+JQi5j2ijTjyFGO8Vo0NihbXBxNtF4FBbPiTPi8SaNlT5eREQi+/ZfQyaDx6ZJRzLXrTpBSnI2/gHujJSYOqTV6ljwpejpOXBEKxo1NZ4klZyQwXfvrxf3/Hh3WnaQTvmv+nUf545EoHK05+OFTxJUy7zAX/bDDhZ+Jtp4PTprEPP+fZWO/ZpVaTYAxBGaMz8ey/ztr+Hl50ZUWDxzHvmR9GTTNXYymYxn3x1F14HN0ai1fPbSEnKzjVO5dvYK5n42DkcnJRdPR7HyD+MTt0wm49lXxQuAA7suc+6UcW2lm7sT4x8VvwsLf91nVA8K0KplLdq2qY1Go+Ovv6XTtKG1fOjZVXSy+HuF6ajnhFHtUdoruBgWxxkT5vMA9Wv53Yl6rit9ws/UoR1Q2Su4EBHHcQtrPZuHBtKjRR20OoFfN5ceWb2bXs3r0CTEn4IiDQt3nrT6/iVxVNoXD8L4ZefxSptt/uLArqjsFJy8Gcveq1Xrp6m0U/DBiH7IZLDu3BWO3bQsWv1fI8DNhW9GDUEuk7H6/CXWnK+cC4uKxiY8TaATBOZs3EZsZhbBHu58Przip+RUF5acOMfluCTcHFS8PqhXVW+HJUfOEZWSjreLE0/2bF8pj5Gek8+ivWJDzLMDO5e7aemXrcfR6HR0aVyL1nWtH7G5YINYKze0U2NqB1hWyrH7ZDg3bqfi4qhiwgArop3dzEc7D5+KJPJWMk6OSsYONX3cv5aLe+7fuylBgdLH+3ORKFp692pC3TrGRvEZ6bks/1s8zmNP90KpMo46b9twhoiwBJxdVDw+07jWVKvV8cUbq8nJyqdBsxpMebav5F6unIli8XeiLdfMdx8ipJ755sAl321j8Zdic8yU2UOZ9PLgavcbEFI/gM9XPY+3vzvR4QnMeeRH0iTmqhuQyWS8+PHD+NXwJCEmjXlvr5GMTgWFeDPzjeEA/P3THq5JREfrNghg6GjxYvynr7YVd+CX5KGx7fH1cyU5KYv1a6QF1OOPiRckO3dd4paJiObkR0ST8D0HrhJtIkrp4+nC8H6i0f/CVca1p//3mPqo565jpUc9fT1deKi3GE21Kuo5QtzztpPXiLhtvp70bmQyWfEF7MpD50nKtG4O/N2M7dycAA9XkjJzWHboXLmOZYogDzem6EcYf7Flf6UJXEtpHRzE+Hbi5+HdTbuqfD+VRZfQEF7sIX7W3tu2m6uJyVW8o9KxCU8T/HrkJHsjbqJUKJj38DDcHO4vWwYDcRlZfL9H/JF+pX+3SmngsYbEzBzm7xIF0osDuuLiIB1tKy8/7ThGdkEhDYN8GdSqfL6gUYlpbDkpFrHPGGJ9tPPM9ViOXrmFnVzOU0PNj7g0oNXpiqOdkwa1xdXZ/OezZLRz6ijTjyEIAgv1tZ0PD26Nm4nazvDIRI6eiEQul/GoRLMQwOUrtzl2XFwzbYq0r+iSvw6Tl1dEvQb+9O5nbCaelZnPogViQ9GUZ3rj4WX8+Vz15wEunorCwVHJ3M+kO9izM/L4/OWl6LQ6eo9oTb/RprMXgiDwzzdb+efrrQA8NncYE16wbgDAvaRmHT++WPU8PoEexFxPZM64eaRK1F0acHV3Yu63E5Er5Ozfcp4dq6UFYd/hreg1WGxK+nzuSnIlvESnTO+Dm7sjUZFJbJI4jkplz7SnegGwdPFhsiT8RBs3CqJrl/pipH2xdId7/br+dO1YD0GAf1aajiBOeqgD9nYKzl2O5dxl81HPnu3rWRz1nDKkPSqlHZduJHDkgmWTYxoG+9GvjRhZ/XmT9bPTuzSuRcvQQArVWv7YfsLq+5dEaWfHswNFcfLrrhOVMtEI4KleHfBzcyYmLZM/9pcvUlsRzOonntNupWbwy8HyvYbVmWe6dqBn3VAKNVpeWLOZ7ALTI3OrAzbhKcGxqBi+2y+KsXcH9aFJgOn51f9lBEHgw3/3klekpk1IEGPbNK/qLfHFlv3kFalpFRLIQ22NhUhFcCMxlZVHxXrB2SN6lGssJsDPW4+hEwR6NqtD89rWjU0VBKE42jmyW1Nq+npYdL+dx8OIik/DzVnFIwNKdx/4c+2daGdwgKfJdcfO3CT8RiKODvY8Mty0OFu8TNxznx6NCK4hHaH9Y6FYtzloQHNq1jReczs2jU3rxKjzU8/2lazbXPzzHrIy86ld14/hY4yj3zfDE/j7J1GYznxjGEEhxrPUBUHguzdWkxSXQVAtb55733haUsm1f3/1L0u+3QbAE2+OZNzM/pJrzREbkci3Ly7mjTHfWvX3zoR57FpxVLKu0hxBob58sep5/Gp4EhuZxJxx80iJzzC5vnHrWkx9Wezw/umDDdy6btwQIpPJeP6tEfgHeRAfm84fEqMw3dwdeUwfYV78y14y0ozHJ/Yd0Iw6df3IzSlk6WLpSORj07qL3esHrnE9IkFyzZQJonDatfcKt+Ol6yb9vF0Z2kec+rNotfkU9+MP6Y93LIyo2+ajnj4ezozVd7j/ut58DWlJpg/vjEwGe85GcOWWdU03MpmM54aJ9e1rj14iPq18VkUj2jWmUZAv2QWFLNhhvRC2BGeVkteG9gTgt/0niU7NqJTHsRRXBxVvDu4l7ufQSSKSzL/P/1XkMhlfjhxEDXc3bqVnMHfzjiqvszWHTXjeRWJ2Di+v/xedIDC6RRPGtKwc8VMd+PdSOHvDbmCvkPPB8H6SJ/57ydGIW2y7GI5cJuOtkdLdzxXB15sOotUJ9Gpah471rW8CKklEXArbz4iTUGYM7Wz1/U9ci+bM9dso7RQ8OcSyaKdGq+P3DeJJddKgdrg4mo8KX46I58g566KdDw1shYcJg+3IqGQOHr2OTHYnBXo3Z85Gce5cNPb2CiZLjFcEse5Pq9XRvmMd2rQzriuODE9gy1p9OcTswSjs/v/nSqfTMe/jjWg1Orr0aUy/EdICfMvSoxzZeQk7ewWvfz8JJxfp6LAgCPz1+WaW/SCm459+ZxRjppsfO3o3SbGpfPviYp7u+i7blxzizL6rVv2d2HmRr2YuZEaP9zm85axVJ4/AWj6i+Kzpxe2bycwZN49kM+JzzFM9ad21PoUFaj57aQmFEjPinV0dePWjhwHYuvqUZMp94MjW1GsUSG5OIX/ON/bbVCjkPPms+DpuXHuK+DjjPdUJ9aN3L7HOeuFfByX326h+IB3biXWTS8xEPR99qAMKhZxTF25x8dptk+sa1L4T9fxzXel1mI8OaY+D0o6rNxM5dM6yGsY6Qd4M7iA23RiaB62hfYNg2tcPRq3R8tt26zvkS6KQy5k9UhSFq45e4EZi5YiwQc0b0LleCEUaLZ9u2lflAmhgk/r0ahCKWqvjrY070eoq3s+0OuDh6MD3o4dir1CwMyyCP49Le/ZWB2zCswRqrZaX1m0hNTePhn4+vDuoT7Wr6aookrNz+WCLGCl6unsH6vkZR4ruJUUaDR9tELvyJnZuSeOgyokyHwm7xYGrN7GTy3lluHSzizX89O9RBAH6tapPo5rW7VkQBObro50P92iBv6dlhuPbj10jOiEddxcHxvWzPNo5sJRo58nzt7hyPR6V0o7xI4ybfAz8o2/w6Nm1IbVDjJtzBEHgz4Vibeewoa3w9zM2ZL96+Tb791xFJoMnZxiLO0EQmP/lVnQ6gR79mtJSwrNz18azXDkbjYOjkhlzh0p+V29cjePXT8TmoCdeG0o9CeP64j1/spEVeuH0zHsP8dBTpXuiGshIzuKXN1fwRIe32b7kEDqtjo4DW/Dqgsd47acnLP6bPHcELh5ORIfF8+HUn3hxwKec2X/F4n34B3vzxarnCQjxJi4qhdfG/EDS7TTJtXK5nNlfjcfTx4Wo8AR+lTCNB2jeLpR+I1ojCALzPtqI9i5LHoVCzszZoifn9o3nuHbZWOy16yBeXKjVWhb9tk/ycaZN6YZcLuPYsUiuXJEWjFPG6+smd18mIUm6nCDAz53BvcSAwaLV5sVecdTz6LVSo55ebk7F3zdrop5PD+uMQi7j0KWbnI+Ms+g+JXl2mLjHDccuE1OOUZoAHeoF07tpXbQ6ga82SQv88iKTyXhzRG/sFHIOhN1kz5WqNTiXyWS8O6wvLiol52LiWXjkdJXupzJpERTAm/3Fi4uv9hzkZHRsFe9IGpvwLME3+w5zOiYOF5WSeQ8Pw9G+ai2FKgtBEHhn0y4y8wtoEujH9B7WG51XNIsOniEqJR0fVyee6299naQlaHU6vtwopn/Hd21JbV/TIswSrsYksvt8BDIZTB9iWSd6SQ5dusmlmwk4KO14bJBlTVQarY4/Nogn08lD2uPsaH4kX8lo5zQLo50jB7TEy0O61jcqOpW9B68Bd0TA3Rw7HsmVq3GoVHZMmmC8RhAEftOnx/sPbkEdiSafvdsucvlcNCoHe5560TjVnZWRx+/fiKnfR2f0wTfAw2hNQV4Rn74kTjDq0LsxI6dKR14FQeD3D9ezWm9OP+PDhxn1RC/JtXeTm5XH4k838Fi7N1n3y27URRqad2nAN//O4f0lz9FvXGf6jO1o8d+kV4ex6PQnjJ81BJWTkvCzUbzx8HfMfegbrp6yLMrmX9OLz1c+R0CINwnRqcwZ9yOJsdLi09PHlVe/HA/Av8uOcXDrBcl1T84ahIubI5HX4tm0wrhWrkmLYPoNFVPR87/418i3UyaT8ZQ+6rln52XCr8UbHaNmTS8GDRDLff5YKG2B06xxDdq2qoVWq2PJKtMRwMmjO6KQyzh+NoqrEcaPZaBBbT96tLMi6jm4HU4O9oTdSmL/GcsEVYifByO6iEL4p43mm56kaF2nBl0b10arE/hlm/Ud8nfzyvDu2MnlHLx6kyNhlnXpW0uorxeP6Scafbp5H3lFxtH0e0mguyuvDxIF2fd7jnI9ybpmr/8SE9q0YESzRmgFgZfX/UtKjnH5S1VjE556dlyL4I9j4pXQp8MGUNurfKKkOrP+/JXiFPunDw0s1zzgiuB2eia/7BVPIrMH98C1khqK1h6/RERCKm6OKqYPsF4o3s2CLfq6ybaNqGflHGSdTuAnvYAc37sVPu6WNXVtPXKF2KRMPF0dGdu3VanrLY12nr0Uw8Vrt1HaK5g40rQI/melGOHt1rk+dUONI7w6ncBCfSf7QyPb4uXlYrTm2OHrXDwXjVJpx9QnehrdnpdbyG8/iN6QEx7vjp/ECMuF3+8gKyOPWnX9GDVJWgAveH89sTeS8fZ3Z9Zn4yQjooIg8Mv761irj8LN/HgsI0zYPpWkML+IVfO2M63tmyz9egv5uYXUb1mLj1e9yBcbXqFJB/OG9OZwcXdi2hujWHTqE0Y+3Qd7pR3nDl7j5UGf8f7k+URdNZ0+NuBXw4svVr9AUG0fEqJTeW3sPBKipSN6bbo1YNwzYnT3+zdXkxBjLFI9vJx5TH8BsPjHXaRKdM4/8Vw/nJyVhF+JY+fmc0a312sQQN+BYv3lbwt2S0YMH320K3Z2cs6eu8WZs1GS+506Qbww3brjIkkmvEtrBHgwoIeYujdYg5miuMPdgqinh6sjj/QXO7d/XX9E0hhfiieHdsTeTsGJazGcDLPe2sdQxvPvyWvcTJC+iLCUWr6ejNcPy/hy4/5KGaUJ8HTvjgR6uBKfkc2ve8tXJlARjG7dlJ71Q1Frtcxdtx21tuLN9KsDMpmMDwb3o56PF0k5ucxavxVNNSsvsAlP4FZaBnM3i9GTxzu2ZWCjqp1PXpkkZGbzyVYx6vd878409LdOMFUGn27aR4FaQ/s6NRmqNyGuaHIKCvlxmyjCpg/ohLtT+VwKLtyM5+BlMZI4fbD1InbvuQiuxSTh7KBkygDTae2SaDRa/tgo/oBPGdIeR5X5iPyVyASLop1AcbRzeL8W+EiIRYDY22ns3i92708xUdt54GAYEZFJODupeERqNKZGx+8/iyUVo8d1wM/f2KN16Z8HSEvJIbCmJw9LiMqr52PYukac6PL8WyMku9j3bDjDzrWnkMtlvPb1eNwluuEFQeDnd9ey4Q/99+GzRxhmovu+5H22/n2Qx9u/xR/vryE7PZfg+gG8tfAZftj1Bm17N62w8hxPPzdmfDKe349/yICJXZDLZRzdep4ZPT7gixl/kGKiwcaAb6AHn698nhp1/EiKTWPOuHnEm7IrenEAjVqFkJtdwGcvL0WjNj4pD364HQ2b1SQvt5Bfv9pqdLuXjwuP6jvY//hxFzkSHqGPPdULe3sF587c4uQx44hhgL87w4a2AuDPRQclxWnLZsG0bFYTtUbLstVmop4Pd0Qul3H4VCThN0w39jSs7V8c9VxoQdRz4sA2ODsqiYhJYe/p66WuB3EU5uhuYjT3pw2WWzIZaFYrgF7N66ITBH7ZWv6o5/T+nXBzVBGRkMq6E8bm/hWBk9Ke14f1AmDhwdPcSCqfYC4vMpmMD0b0w91RxeW4JH47WPVd95WFk9KeeQ8Px8nenmO3Yvh+v/WR9srEJjyB97fvIaewiLbBQbzSu3Km5FQXvttzhOyCQlrWDODxLpYJnsrkQkwCe6/ewE4u560RlVdTu/roRdJy8qjl48H4LuUbjQmweI8YHR/WoQm1/KyPji/eId5/Yt/WeJRi/G5g35lI4pIz8XJzKp4hbY4VW8XHGNC1sdloZ0RUMmcvx2BnJ2fiKNPRzrWbz6LTCXRqV4eG9aW791fo059jxrTHXWIC0ZFD4URHpeDq5sgjEqIyMyOP9cvEY0yfNQil0tjXc5G++af/yNY0k6j9zMsp4OePNgIw8bl+Jsdhbl9+jI0LDyCTyXjpy/EMmWS+xCM7PZf3Jy/g+5f/JjUhA79gb2bNm8bPB9+l2/C2lfbZ9Q/2ZtYP0/j50Ht0G9EGQRDYs+o4M3t9yKnd5kWDT6AHX6x8juB6/iTdTuejZ/5EXWTsZ2hnr2DutxNxcXMk7Hw0W1cYCzq5XM5zb41ALpexf9tFIiXS5SMf6UBIqA+Z6XlskDiGf4A7o/TuBMv+lj4ZPjqxCyqVHVeu3ObSZekatakTxN/pLTsukJdfJLkmJMiLvl3FC9lVW8w3WhiinjuPXiMl3bxnpruLY7Fv7tLtltcLPj6kPSp7Beci47hww3T63xTP6qOe28+GlbvD3d3Jodhe6Zedxyut4aZPk7r0bBiKRqtjwe7K6aS3Bn83F94aIpZ8/HTgBEnZ5fNHrc7U9fHi42FiluLXIycJq0blBQ+88DxxK5ZDN25hL5fz+fCqTztXJgmZ2Wy+INbnvTG4V7lN0yuClcfFmrIhLRtSz79yGpwEQWC9flTc1F5tsbcr33ucW1DEwctivd34HtaL2LiUTC7ejEcmg7E9Lb//rhNi9/zw7k1xKCXaWVik4dBZcY8P9TX/GPuOisft3LoO/j7SU6J0OoH9h8V1I4a0klwTG5tGWFg8crmMkcOljed3bxdn2Q8Z3goXV+Oo887N51CrtdRrFEin7sb+qtE3kjh/8iZyuYzJJoziNy85SnZGHjVCfRgv0bgEEH8rhV/fXwfAY68PZ6CJelUD4WejmNn7Q45tO4+90o6n3h/D78c+YMCELijK+XmylJAGgbz153R+2PkGdZsHk5maw1uP/MCfH65FZ0Y4ePm78+mymbh5OnPj8u1iq6i78a/pxRS9xdKqX/dJCtT6TYLo1l+sV9y80lhY2tkpmPCYOOt96/ozkrZQo8e1RyaDSxdiSJDouvfycqFXD1Ew7tlzVXKvbVqGUCPQg8JCDUdPmK61HNFfNBA/dCoSjZk55Q1r+9O0XiCCAPtPRZhcZ+ChXi2QyeBiRDyJqaZHlZbE192FPq3FbNrO0+EW3ackDWr40rZeDQQBdpy1/v53M7Zzc9wcVSRm5nD8euVM9pHJZDw/QLyg23k5gtSc8o3/rAiGNW9I6+BA1Fotfx87V9XbqVSGNmnI4Mb1EYDv9klPBqsKql55VCE6QeDz3WI92thWzQjx9KjaDVUyfx07i0ano33tmrSsGVjV2yEzv4CtF0Qx80jHFpX2OJdiEolMTENlp2BQq4blPt7+SzcoVGsJ8fWwupMdYKc+Pde2fk2LazvzC9Uc1htX921fuuH9iYu3yMsvwtfLhab1zL/X+4+L++nZyXSJycUrsaSk5uDspKR9m9qSa/bsFbuv27UNxUNiTnpWVj4n9OnVvgOaGd0uCALbNpwFYOhD0h6im1eKjS0dezbCT2JaUkFeEWv/FL/T42f0lRSFWq2Or17+h/zcQpp1rMvop813r6cnZfH2hHkkxaYRGOrLN1vn8PDMAShLEf+VRYPWtfl261yG6xugVn6/jfW/7DZ7H+8Ad57/bBwAqxbs4orEiEuAgWPb4+XnSnJ8BrvWSUfzhj0ilm3s2XxecuRmtz5NcHV3JDkxizMS6XQfXzda6j9De3ZKj/jr00cUt/sOXJUUjDKZjF7d9eJU3+wmRYtGNfB0dyI7p4CzZgzlAXp3ED//e4+XLup8PV1oWV+cULb7lOUicEA78bu76/R1i+tDSzKwjfj7tf1M+YWn0s6OIW3E13D9icobtdg4yI/mNQPQaHWsO131Ix1lMhlPdhOj7stPXSCnUDpifr/wQo8uyGUydl+/UW263B9o4bn58jUuxifirLTnue7lbzapzmTlF7DytBhterJr1afYATaduUqBWkODAB9ahlSeEDb8qPZtXh/XUjwvLWH7aVEsD2zTsEzpVUO0w3ASsoTD529SWKShhq87DWuVLnb3nRTFZO/29c36oUbfTuNmTCoKhZyu7U03xOzaJ4rKnl0borQ3Tn8LgsBuvfDs07ux5DEO7r2KWq2lTl0/QusaP4erF2OJiUpB5WBPTwlhmp9XyK6NojAd9oi0E8O/y4+RmZZLQLAXvYe3kl7z92GunLyJo4uKV7+dhMJM5F8QBL598S8yU7Kp3aQGP+55i/ota5lcf69QOtgz8/OJTP/4EQAWfriOm1fMn1S6DWlF34fbo9MJfPvqUiNbJAClyp4xT/YCYMXPeyRrPZu3rU1IXT8KC9Ts2nRW4hh29B0k1jNul2gygjsXHrt3XJKsd2zTuhaeHk5kZuZz+kyU5DH69RQ/Z8dP3iBbQgCDaPXUvUM9APYdM1+P2buD+H08ezWW9KzSI3OGC8DdJy0XgZ2b1MLFQUlSRg7nb1hvrdSvVX3kMhlXohPLba0EMKq92IC1+1IEmXnSr2FFYAgsrDp+oUyCu6Lp3aAOoT6eZBcUskp/XrxfqevjxbhW4vft010H0FUDY/kHVngWqDV8vVcMPT/TpQM+LlU7KrKyWXHqIrmFRdT386ZH/dpVvR0EQWDFCTHNPq5Di0qrjytQa9h6VhSKozo0KffxsvIKOHw1CoCBbawftRmbnMGVW4nIZbLitJslGNLsfds3KPW1Umu0HNSnC3t1ML/HfcfEk2bb5iEmx26q1Vr2HRIfv18v6dcwIjKRmJg0lEo7unaRfszd+uhWHwlRCbBdLyp79G2Cs4vxBcLefy+Ql1NIUIg3rTsZi+TCAjWrf9fbZc3oIxntLMgvYtk8sUb0sTnD8Q82X96xZeF+Tuy8iL3Kjjk/P4GziRGiVcXIp/vQcUAL1EUaPn/md4okTOBLMv390bh5ORMbmcSetack1wwe3xEPbxcSY9PZu9FYWMpksmLhv3nFCUnhOEBv5n903zUyM4xFXPeeDVEq7YiOSiFSYmqSQiGnZ099RHOvtI9pndq+1Knti1qj5cAR0+KvZ0fxe3bg+HWzE6GC/NxpWNsPnSBwwIJ0e5929a1Otyvt7ejVSvzs7rQiUmrAy9WJ9g2CgYqJejap6U+9AG+KNFq2nwsr9/FMMahFA1wdVMSmZ3EkonIsnKxBLpfxeBcxq/LX0TP3bYe7gRd6dMZZqeRSfCKbL5vOENwrHljhufDEGeKzsglyc2VaB+l6tPuFIo2GxcfE4vrHu1ZeE4Q1nI66zY2kNByV9gxvXTmd7AB7L0WSXVBIoKcrHeuVb0oRwN4LkWi0OuoGelMvyHpHgF36NHu7hjXxMjEZ6G7+LyU+R/0AADj6SURBVM1eipAEOHU5muy8QrzcnWjRMMjsWkOavVcn08c9efYmWdkFeHk606p5sOSa3XtEcdCpY12cnY1FY2JCJhfPRSOTITmTvSC/iP16YTpQYgKRIAhsWi7WFA4d1wG5xJjTbStPkJ6cjV+QB31GSn+ntyw+RHpSFv7BXgyaaL6uM+Z6PL+9uwqAx98eTWgTafP5qkQmk/Hy91Pw8HUl6mocCz9aZ3a9i7sTY2eItbFLv98uGdF0cFQy+nHRUmr5T7slI6N9h7XCwVFJzM1kLpw0TtvXbRBAvUaBaDQ69m4zjig5uzjQqasoCHdtl26Q6tNbvMg5dPg6BSYEtSHqucuMyX6bZiG4OKtIz8zjUpj5KGPvjuL3oDLT7f0N6fYz18vU1DOorZhu33Gm/EJRJpMxqoP4fVx/0vJBBdbiqLRnRGvxvVp1onpEGEe0aIyvixMJWTn8e6nyRHd1wMfFmWe6iOUFX+89TIHauH77XvJACs+UnFx+PSLWis3q3RUHidTh/cSmC9dIzsnD382Foc0qT+RZwwp9U9HQlg1xqSTfTriTZh/RrkmFjOA0jMcsS7QTYMcpQ5rd8lrTkmn2Rpak2U/oxWT7+mbn0McnZRIWmYhcLqO7Gd/JXfvEBo++PRpJpqV1OoG9hjV9pCOie/WiskWrWpIWSgd3XyE/r4igYC+atTa+QLh6Poab4QkoVXYMGGksTIsKNaz6dR8A457pjb1EN3x+biErF4iTiSa8MFByjQExgvgHhflqWvdszMinrRudeS/x8HXj5e+mArDu512lTjoaNrU7nr6uJESnskOiQQhg2KTOuHk6EXcrlf1bzhvd7uziQN9hrQDYJNG9DjBQX+qwbaP06E+Dp+feXZclI5FNm9QgIMCd/Pwijh2XbiDqoxeeZy9Ek2Ii6mhvr6BrO/HzbYjwm8JQ53nqSgyZOflm10LZ0u2dGtfC1UlFSmYu5yKsT7f3aVEPO7mc8LiUcnt6Agxr0xg7uZyL0QlEJlTeLPNxHcXyi71XI0nMrPpucpW9HY92FH9L/jh8uspHe1Y20zq0IcjNlfisbBaeqNpxmg+k8Pz+wFFyi9Q0D/RnWNPqIcQqC51O4I/DYpPAlE6tUd6jDlxzpOXksfOSmMoaV4lNRQnp2Ry9LqZ1RrYvf5o9PSef42HRwJ0if2uITkznWkwSCrmM3q3rWXw/w0nNkjS7Rqtjv76+s1cH86n8A/poZ4vGNfA00eSUl1/E4WPie9XXRJr94qUYkpOzcXZS0VFCwAqCwO4dYlTLIDbuZvvGcwAMGN5K8jkaxE2vwS1wdTeOFO9cc5LUxEy8/d3p/7C0JdTGhQfISsslqLYP/caYnxT1zxebiLgQjaunM6/++JhkhLU0NGoNWanZVv3lZZcudqToOLAFwx4Tzfi/nrmIrDTTJ3YHRyXjZvYDYPkP2ykqNI5+ODqrGDVN7E5fvmCPpDAcqk+3H9lzVdJQvtfA5tgrFdy8nkhEWILR7e071sXVzZG01BzOnzFOv8pkMnr3EoXl7r3STSmB/u40byJ2eu85YDqFaIjo7z9+3azACAn0om6wD1qtjkOnS59MVJZ0u72dgt6txO//jjKk292dHejUSKwz3l4BUU9vVye6Na4NwIaTldf8U8/fh7a1a6DVCaw5VTneodYyvn0LnJT2hCemcKgalABUJg72drzSW/Qp/vXICZKrcKLRAyc8w5NSWHVO/NC/3r8n8mqQdq5M9oXf4EZKGi4qJY+0bV7V2wFg/ZkrqLVamtbwp2kN43GJFcXG01cQBGhXtybB3h7lPt6OM+FodQINa/qWybvT0M3evlEInhZ6d+YXqjl0XrRFsiTNfu5aLJk5Bbi7ONC6sXRa3ICh2cJcmv3w8QgKCtXUCPKgkQnvzj36NHv37g0kfTdvRCQRdTMZe3sF3XsaX+jdjk7l4tlbyOUy+g0xtn7KSMvlkF64GjqqS6Iu0rDyF9GUfuzTvVCqjPeQm5VfPBJz0suDzFogXTp6nZXfi5ZDL3z9KN4S3fOlcXzbOSbWe5GxITOt+hsdOJ2vp/9GQW6h1Y/55PtjqFnPn9SEDH545R+zAmvIpK54B7iTHJfBtmXSfpojJnfBxc2RmBtJHN5unB6t0yCApq1rodPqig39S+Lm7khXvXDcLlEram+voGcfvbDcKS1EDBH0EydukJMj3fzSt6e4Ztd+aeslgA4ta+HoYE9SSjbXIo1FcEmKu9tPlG4OX+Z0e1t9pLTM6Xbx/v+eulYhkbpR7cV0+6bTVyttkhHcCTSsOXmxUh/HUtwdHRinPy/+fli65vl+YmjThjQP9Ce3SM0PB6rOV/WBE56f7xa7ugY2qke74BpVvZ1K5w/9l2l8uxaVmtK2FJ1OKPburEwLJUEQ2KCvWRpVAdFOgA3HxWjA8DI2Ke3Qd8OXpZs9yMI0u6E2rWf7+mZ9WlPSc7gUJo5e7NHRdGR0t76bvV/PJpKRSLVay359pMlQk2d0DL1o7NS1vqR35w5953PbTnXxlUjD71h/GrVaS4NmNWjQ1Pg7u2fDGZLiMvD0cWHQOOlu9/V/7CMnM4/gev70HClt1QTi7PUvnv0TQRDoP6EL3UeYXiuFRq3h97dW8M7D35BpYpyjOQRBYMffB3mh53tEXyt9NGZJHJxUzPn5SRR2cg5tOsOuFaZPLEoHe8Y/PwCAFfN2Uihhwu7s6lg8337Z/N2SXqGGJqOta05K1osOGNEKgL3bLkpGVvv2FyPgh/Zdk6zjrBPqR+3aPqjVWg4eko7u9e7eEIVcRtj1BGJuS6eeVSp7OrUOBWDf0VK62/V1nicu3iI3r/QLgLKk2zs0DsbNSUVadh5nrlv3PgP0aVkPR6U90ckZnLtpfbr+bno0CcXLxZGU7DwOh0WV+3imGNCsHh5ODiRk5nAwXNrS614ztXMb7ORyjt+M4eJt8xcl/3XkMhmv9xczI6vOXSK8ikzlHyjheTAyioN6s/jZfbpX9XYqnTPRcZyOjsNeIWdyJ+O6uKrgWGQ0MWmZuKiUDG5Zfk9NU5y5eZvolAwclfb0b1H+EagRcSlciU7ETi5nSDvryzNuJqRxPTYFO7m8OM1mCXfS7PVLTbNrdbpi8+vS0uwHj0cgCNCkfiB+3q6SazIy8ziht7Lp21PaIun0GbHxyNPTmdatjG2GtFode/TRrD79jZuKtFodOzeLNYRSTUVarY4teu9OqWinVqNl+U9iJHPMU71QORh7a2an5xbPYp/08iCz9knz5ywjKSaVwNo+zPhkvMl1UqTEpfHakM9Y9e0WAEbO6M+m1N/Zmr3I4r8vtr6Ol787t67e5vke77F7mXWmz/Vb1WLy3JEALJizjPioZJNrB47vhF9NL9KSstjyt/TjjJzaDUdnFVHhCRzdZVw72rVfUzy8nElNyubYfuNUd6t2ofgFuJOTXcARidubNK9JQKA7eXlFHDssLQj76i9oDA1sd+Ph7kS7NqKo3G0m6tmrsz7dfizcbJQwtIY3IYGeqDXa4iEM5ihTul2huGMmX4Z0u5NKyQB9nfnGY+VvCrJXKBjaRvyOV6anp9LOjofair8DK49XjyajQHdXhjYXz0V/HrZ8EtV/lXbBNRjYqN7/+Zjfax4Y4anR6fhM/yJPatfqvjeLB/hTH+0c3qIx/m7S87fvNYamouGtG+OkrDwDbkO0c2DLBjiplOU/3jHxx7hHszp4uVrWjV6SXXrvzo5NQnA3YVt0N/+XZrfANP5ieBypGbm4Oqlo19R8B/9e/bQic6bx+w+HodXqaFDXn1ombIf27BVP9L16SjceXTgXTWpKDi4uDnTobCy4zxyLJDU5Gzd3RzpKTCo6dfg6iXEZuLg50nOgcanIsd1XSIhJw83TiSHjpb141/y6l7zsAmo3CqS7viFGin1rT7Bn1XHkchmzFzyBk0R01hSndl7g2c5vc/lIOE5ujrz1z3M8+9VklA5K5HK5xX8tezRmwdGPaNWrCQW5hXzx5C9899yfkhFJU4x9fiBNO9UjP7eQL5/9U7IrHcBeacfEF8Wo58oFu8iXSO+7ujsxYrI4eWbdQuOTlFJpx6DRoi/wZokmI4VCTv9hYvmEVLpdLpfRp/8dT08peuuF57nz0aSmSteuFne377tiUlR2blMHpb2C2IQMIm6ZFuQymYw+hu72E5Z1t7coR3f77rPXy5R2HtlRfF22nwkjv9C8jZZFx9NnhvZduUG6BY1VZWVsB/F7fDD8JrfTMyvtcazBYK20/cp1YtIyqnYz94DZfbpjL5dz8MYtDkRG3fPHf2CE57oLV7ienIqHowMzuxlHTu43olLT2R0mFscbvlRVTXJ2LnuvinuqzKaivEI128+LJ4CK8O5Ua7VsOSVGa0Z2KtvxDFENQ22XJRy5cCfN3rh26bWwhpq0bm3rmh0LmpKeUzzFpXdn0/sxRI/69ZKOdhYUqDl0WB+RNZFm36MXEz16N5Ks/zQYjPcZ3ELydkO0c+CoNpLRzE3/iPWJgx/piIOT8QVGVnouG/4UvT0fnTXYZJNQelIWP85eCsD4l4fQxEyX/91s/GUXbz30NZkp2dRrWYv5hz+g+0PSKX9L8PR355ONrzH5zYeQyWRsXbiPWf0+IjfLMjGgUMiZveBxnFwduHIikjULdppc2/fhDgTW8iEzNYfNiw9Jrhk2qQsKOzmXT0Vx46pxWnfwmHbI5TLOHb9BzE1jQTdAL/bPnrhBUoKx0DD4up48Finp+RkU6EGTxkHodAL7TEQ0u3Wqh0plR8ztdMIjjH1BAZwclXRqUweAXWamHcEdM/mj56LIL8UbFaBfGdLt7RsG4+HsQHp2PqfDrR9Z2bpuDYJ9PcgrVLPzXPk9PRsG+dK4ph8arY5/z1ae12MtH0861wtBEGD1yerRZNQwwJfu9WqjEwQWHTW+QLrfCPH04NH2rYA75Yf3kgdGeK45L37An+rcDndHyyMZ/1V2X41EEKBr3VrU86ucGejWcvT6LbQ6gaY1/GgQYL0HpqVcuBVPXqGaAA9X2oSWv443LDaZtOw83JxUdGlS2+r7Z+TkExEn2pT0bGm5oDlzTZxE06NVHYu8V8/p13drY/4xzl2ORRCgQR1/gvw9JNcUFmm4pBcZPUwYwoeFxVNQoMbb24XGjaX9Qs+ejhKP0cdYvOp0QvFIxd6DjKOZRYVqzh4VSwcGSIzQzMnK5+IJMSI82ES089iOixTkFVGrQQBdBpm+2Pl38QFyMvOo2zyYia8ONbnubs4fuMpPr/6NIAgMfbIP3+55m6A65W+YUyjkPPrGQ3yycTbuPq5EnIvi6+m/WdxIEhDiUzzVaO1POyXnrgPY2St45Dmxw33P2pOSa3wC3Omgn0Z1bLdxWtc/yJPWncRo9slDxgIooIYnTVsGIwii+LybWrV9qB3qi1ar4+L5aMk9dOsmpkLPmbjdyUlFe326/ewF6TUAvfQR/jOXTa8BqF/LFz8vF4rUGq7dlBayJenRWvzOXbmRQEGRZdFHO4Wc7i1EIXwqzPpRhjKZjMF6T8+j18w/H0sZqvdUPhJWuR3eo/Tp9oOVWE9qLVP05Wg7rpp3PrhfeLZrR5zs7bmenMrFuNI/4xXJAyE8k7JzOBsbD8Dw+9w+ycDRm+IPUXWYUmTg7C1RyLSvY77burxcjBYLxFvVDqwQs/wLN8XPTovaQdgrrLejCotJAqCmr7vFaXaA8BgxetQ4tHQho9ZouRkrittGpay/GiE+n6YNTI8pvXEzGa1Wh4e7EwH+7tL7ixBf50YNpV/njPRcEhMykcmgkYQwjb2VQm5OISqVHfUbGu8l/PJt1Gotnt4uhNTxNbr98qmb6HQCNUJ98K8h7TJwQl8X2GVwS5OfBa1Wx7a/DwIwekZ/7Cz09U2JS+OTKfPR6QT6TezK899NRelQ/rKOkrTp04wPVs/Czl7B4Q2nWP39Vovv22dsRzz93MhIzubo1nMm13Ua0ByZTEbUtXiSTDTntOkqXnxckBCOAC07iKLvsoQtEkDTlmLpx7VL0gKrUVPx8xF2LV7y9sb6z8d1iSlHxWv0n+ewCNMNIk3qi2sibiajlmiGMiCTyWio/x5dv5Vkcp2BAG9XPFwd0eoEbty23AuzcS3xMcJiTKf+zdGmrnhhfaECGowAWoWK78PF6IRKFV/tQ8VhDGHxyeRWk1npHWrXRGmnIDk7l5sp6VW9nUrH3dGBnvXE7+3O8NIndVUkD4Tw3B0eiQC0DAogwE26keJ+okij5fQtsVOyU2jlijxrMAjP1rXMT9MpLxeixZNX8wqa/14sPEOl7YRK41q0eFJpFFx6V7oBnU4gQn+/BiGl3+9WXBpqjRZnRyWBvsad4SUJixRP3o3rmn4+166LJ+8G9fxNCjaDCGhgwmbJICJqhnjj7GIsuK9eFEVIgyY1UNgZ/xRdPiuKmKata0nuwSCCWphIi6uLNJzRd9x37Gvc2GTg9J7LJN9Ox9XT2eIudnWRho8nzycjOYvQZsE8//20SpsI1qh9XaZ/+SgAf76zkvMHTDfQlMTO3o6Bj4q+fVsXHzS5zt3LhcZtawNw0kQDT4uOYmTu6pkoye70pq3FxrLLZ29JCpbGzUWhcfWSdAd3w0bib0K4CeFZr54o0JKSs8iQSMcDNNSvMSc8awR44OrigFqj5UaM+Y7e+rXEi51wC4SnTCajQYh+fbTlIrKx/rt9Lab0x5CiWa0AZDK4nZpFalb5fRkbBflip5CTnptPbFrl1V/6u7sQ5OGGThC4ECP9nt9rVPZ2tAkWP4dHb1RMBLm6M6CR3k/22r2N8j4QwnNHmKjm+ze0vJv4v8yF2/HkqzV4OTtS36/yUtrWkJVfQESSGAloVUGCUApBEIojns1DKsYj9EKUIeJZtn0bIp6NLBCQBuJSMsktKEJpp6BWQOmeodf1zRL1a/mZFUBarY6wG6JgbFTPtPA0RDMbmlsTLq6pb0p4XhEvNAyi4m4M0a9GzaXLIS6fFX/8DaLmbi7qp9kYRJHR/U/eID+nEA8fF+q3NH0B9u9fYtNMv0c6o5SoI5XitzeWceXYdZzdnXhn2Qs4OFWuVdmwJ/vQd0JXdFodn0yZT0qcZRNrBk/ujkwm4+z+q8TdMC1u2hv8Mk0Iz+C6fnh4u1BUqCH8gnE9Yv2mNbBX2pGRlktctHHEr2Ez8T2+FZlEnkQTU8PG4ncr/Go8Op3xCdDZWUXNml7imuvSwrKB/rN6Oy6DHBM+qDKZjEZ1xd+Fq2YEKojfJbjz3SqNBsGi8LwebbmIrF/DB5kMUjJzScm0Xji6OKqoGyCWUl2IKr8VkMrejkZB4vO4eKtyrYUMAYizURUTra0IOtURI/PHblpfc/tfpGfd2tgrFESlZRCRUnlTq+7mvheemfkFHL8lnuAeFOF59Ib4pekUGlwhYyIrgvPR8QgChHh74OMqPSWnIkjIyCY1Ow87uZzGNcsvPFOycolLy0ImE6MLZeGa/kTU0IqIZ5g+ylI32Ac7C6ZNGdKBhiiNKW7dTiO/QI2jgz21aniZXGdo0GhoQlTm5RUSo0/tN2ggvebaFTG61aiJtPA0RL8aNzOega7T6bhiiHi2MRaeOVn5ROqFbfMO0sLz5G7RiaBdryYmm4qS49I5sUN0Whgy1TKLtb0rj7LhJ7Fh57Xfn6mQms7SkMlkvPDDNEKbBZORnMXHk+ebrNssiX+wN+300d6tf5uOenbQrzl/KFyyg14mkxUL/AsS4yuVSrtij1VDpLok3j6u+Ae6o9MJhEs0KIXW9UOptCMnp4DbsdKi2hBZN1zw3I27m2NxWUi4GVHZSB/pv1aa8NRHMG/GpqIx4Qzwf/vTC9UwC4UqgJODsngYRVgZo54tQkXRXlHpdkOm6GLMPRKet6qP8OysLwM7ERVTJmP//xouKhVdQ0WxvePavUu33/fCc2/EDTQ6HQ18vQn1tn7azH+R4zcNwtO8pc695EzUvUqz66NwQT44WFirZ/Z4+jR73QBvXBytj2rlFRRxK0msF2oUYl4UlsSQrjNEUUpdXxzxNL8+TD+1pUGov0k/y8JCNTf1xzOkL+8m8kYSggA+Pq54eRpfSAiCwLWr4msnJTzz84q4FamPBEsIz+jIZHKyC3BwVFK3obGwLVnf6W2iBtUQvevQ17QTwfZ/DqHTCTTv0oDg+qVHtKMux/LtzD8AmPDaCDoNuXf+uA5OKt5Z9gLO7k5cOXad399YbtH9hkzpAcCOZUdMitXQxkH4BHpQWKDmwjHpE1BzfUmDqTpPwwXCJQnhCdCwqfg+X7toXOdpZ6egnv59vnZFWog0qK+vuTQjGC1Jtxsi/aVNMAr0dcfZUYlaoyXKggizoSTmekyyZNTW5H4M6XYrUvQlMWRiDJmZ8mLIFFV6xLO2+LtwPiah2oi8poH+uKiUZOYXcjWhbO/Hf40B+oDczrDSR8RWFPe98DSo+Acl2plXpOa8vpGqUyU38VjDvarvNPxYtggpW3TyborT7KFlS7Nfv50iCjR3Z7zdLI/0huujpJbUdwqCQIQ+4tmglOlGhvRiIxOCEiDyZjJanYCHuxO+PtI10Yaok0EM3E3c7XSys/KxVyqoI/FY4Vfj0OkEfP3d8PY1fgxD1Kxxy2DJ8Zal1XfGRaUQG5mEwk5Omx7SDYVajZZt/4gWQkOm9pBcU5LczDw+nPQDhXlFtOnTjMlvjS71PhVNUB1/Xvv9GQDW/7SDvStLH3vXYUBzfAI9yEzJ5sgWaasYmUxWnG4/KdG5DlbUeZ6Rro9rrC+pMFXn2Vh/gWKIlN+NoaTDVKod7qTbTVkqATTWr7kZnUKBGf9LuVxWfCFnSbo9JMATlb2C/EI1sUkZpa43YMiElDfieflWImpt6ZHZ0mheSzze1dtJqC2I9JaV+v7euKiU5BYWEZ5QNRN07sZOIad9bfEC6dgDUufZp0Fd5DIZVxKTiMm4N76q97XwzCtSc/BGFPDgCM/Tt26j1uoI8nAj2FM6EnSvUWu1XIwVTxaVLjyLG4sqSHgWNxaVbd9X9QLSmsYiuNPQ0KCUCCZAcloOmTkFKBRyatcwb511VR/laWSmsShMf9I211hkOPmbbCzSR63q1Q/A3t5YOBqiXo2aG0c74U7UrKz1nSf3iGn2pu3r4OzmKL1m1yVS4tJx93ah6zDzkUtBEPh6+m/EXk/At6YXcxfOMDsBqTLpNKQ142cPB+DbmX8Qddm8FY/CTsHASWKTkaGeVYoOxXWelyUbDYLr+uHpY7rOs0krMcNy+1YKGRJG74bI9rVLsZLHb9REFKamIp719Rc5iYlZZJrwNLUk4unr5YKXhxNancD1m+bF3p06z9JFoZ1CTt2aYk19mBV1noZMyDUr7lOS2n5euDqqKFBriIgrv4Cr5eOBm6OKIo2W8PjKi/op5HJa6NP61Srdrm/IfVDqPL2cHGkfIn73dobdm3R7pf5yjhgxgpCQEBwcHAgMDGTy5MnExd27D9jBG1EUarTU9HCjsb/lac7/MoZuvM6hwZXWZWstV+OSKVBrcHNUUcfXdF1heVFrtVyJFX+8K6KjXa3VciVaFGEtapdNyJalsSgtK4/kjFxkMqhXs/TPrUGk1gryQiVhwm5ArdYSqR+h2NhM05DhpN3IhKgECNd3tJtqLLpqaCwy4e95Vd9YJFXfCf/f0X43ltR3ntBH7dr3Md3NXtxUNL4LSpX5pqI1P2zj8MbT2CvteGvJ87ibiATfK6a8/TCtezelMK+IDyf9QIGJZhoDgyZ3Qy6Xcf5QGLEmooGtujXAXmVHYkwa0RK2RTKZjOYd9el2iTpPVzdHauuF3+VzxtGieg0DsbOTk5GWS2JchtHths/KjYhEyYiqi7MDNYLEcilTdZ4lG4yycwok14gNRoZ0u3n/wnqGTvUoy0RhQ4NQtSJtboh4xqZkkp0nvWdzyOUymut/n87fLH+6XSaTFV+4X7hXDUbVSHgaGoxO37pNUSVGfKsThsDcvarzrFTh2bt3b1auXElYWBhr1qwhMjKSMWPGVOZD/h8G9T6gYelzru8XDFdp1TLNHhJUqc1OEfGpFKg1uDqoqO1b/nre67dTxOM5qqjtVzbBfPmm+MPduJQU+P89rv6kVdPPA2fH0n0hr+m71EtLs9+ISaFIrcXFWUWNAA+T6wyNGQ1MiNP8/CJiYvSNRSaEpyFd2liivlMQhDsRTwnhmZyQQVJcBnKFnEYtjG8vrb4zP7eQC8fEKU6m6juTYlM5tVscKjF4svmmooLcQpZ+vgGAZ76YRKN2lg8BqCwUCjlzF87AJ8iT2OsJ7FxiunEIwLeGF+37iSb9WxdLRz0dnFS0MBjB75Ge191CL/RN1nmWsFW6G6XKjrp6P86rEn6eAYHueHg4odHoiDDh12loZDNV51mywei6GVFpuPAqrcGoYag+DR6VZFHdZn39BWa4FdFLDxdHgrxFC7Qrt8pm5F1c51kBwhPuZIwMDiGVRZtqKDzr+3nj7exEvlpTXLZ2v2MQnmdj40jOKb8tV2lUqvB8+eWX6dSpE7Vq1aJLly7MnTuXY8eOoVZL19UUFhaSlZX1f39lpUirZc918cexf8OqP1HcCzLyCriaIP7gdaxW/p2iCDEUk1cWhjR702D/ChG4d4zjA8t0vJz8QiLjRYHW3Ioa0TAr6jsBrt4QTw5NzKTPoUR9Z90AkxdihYVqom6J6TpzjUU6nYC3lwve3i5Gt6vVWiL1wqFRU2OrpMT4TNLTcrGzkxc3lJTEYKNUt1EgjhI2RaXVd547FI6mSEtAiDfBJp7Dtr/FpqKW3RpS00y9K8DeVUfJzcwjMNSPoU/0Nrv2XuLh68bYl8UpSxt/3V2qD9/gKaLA3rn8CEUmxkB26CdGiE+YrPMUX3OTdZ5txGiRKSP5xnpbJakGI5lMVtyIZrLOU/9emYp4goUNRvrvSmmWSnVr+qC0tyMnr5DYxNJNxQ1entZ0tgM0r6PvJL9RNqFnKAWqsAYjfZ1nZQvP5sEBKOQy4jOyScjMrtTHshSZTFZ8/nxQ0u2Bbq40D/RHQPQ9r2zuWZFSWloaS5YsoUuXLtjbS6e1Pv30U9zd3Yv/goPLLp6ORcWQU1iEr7MTrWtWruCpLhyPikEQoJ6vF36uxoKgKhAE4R4ax+v9O8toe2R0PP2PePMyptkvRyUiCBDk7YaPu/WNRQ0tiJIKgsCVSMuEp6Gj3VyaPcKSxiJ9fWf9BtKC7UZEImq1Fjd3RwKDPIxuN/h31mkQIDl/3RAta1bG+s4T+mhdh75NJQW2VqNl+5LDQOlNRYIgsOmXXQAMfbKPSVumqqL/pG44OKuIvnqbC6XMH2/frxk+QZ5kpeVyePMZyTUd9KUJl0/eICfT2Ki9Zh1fs3WehohnxLU4CvKMbZkMNb3XLpswki8WnqY62/URTwsajMLMrDF0tkfHpZGbZ7pMwc5OQYPaopi8WkpaHqB+sC8yGaRm5pKSYXnkqLl+OEVZI5bN9BOQYlMyScuWNti3hubB4n6iktPJLEP631KcVUoaBoiv75lq5OdpsFV6UBqMoISZ/D2o86z0X9E5c+bg7OyMt7c30dHRbNiwweTa119/nczMzOK/mJiyX23sj7gJQF99x9aDgGFaUcdqZKMUk5ZJSnYe9goFzWpWjCA0RXF9Z3DFPM5lQ31nGTvar+rv38zKiUeRsWLEsX5w6eb/SWnZZGTno1DIi+vRTBGub6QwGGhLPrZ+jbnGohuRhjXSzys8TDx5NmwkPUozXC8qGjWTNo6/el783huaVUpSkFdUan3n2YNhALTvLZ1mP384jNSEDNx9XOky1HxTUdTlWCIvRKN0sGdgKSn5qsDZ3Yk+47sAsHPJIbNrFXYKBuknGe1ZfVxyTUCIN8H1/dFpdZw/ct3o9pJ1npdP3zS63S/QAx9/N7QaHdclopaN9JZKEdfi0WqMLXQMDUbXw6QFmKGmOD4hk1wTda2GiGeEGcN8T3cnAvQTvkprMGpSRy9ko0oXno4qe0L8xTIfw/fYEprpf2OuljHV7ubkQJ0AsRzI8LtVHjxdHAn2FksWrsaWrenJUtqGiu/5mSjpi5GqoLO+zvN8bAJFmtL9cu8HBjSsD4hBuwJ15T5nq4Xn3LlzkclkZv+uXbtz5T179mzOnj3Ljh07UCgUTJkyxWRKSKVS4ebm9n9/ZcVgC9A0sPLNnasLCVliqqKOT/XxK03MFLtba3i6VYivpjlS9Vf6QV5l/9yUJEU/gq6Gd9ncAQyTSAz1W5aSru/Y9fMqvYElTR+V8nZ3MttYBJCWLu7H38f0fgxr/Mw0z6TpIzm+JkZzZqTpHyfQQ/L2dH3Hs6nbU5PFEpugEOMO/bTkLHQ6AQcnpWR9p1arI1nfuBJqorEpRp+mbdqxHvalvGa3roonw/qta+PmXT3H7bbp0wyA6Guln7hb6a2lYsxEA0Mbiq9bcpx0arlGLfGCKDXRuBRKJpMRGCwKoNRk49SpX4D4nmk0OrKzjTvT/fW3p6dLRwtdXR1w0EfJMyQisgC++s93RqZ053vxXvSf8TQTxzEQoD9eaeuKH99TzDZl5Jh//JIYfiPSsvPK7GkZ5CW+dhUxOhMgyFPcU2pO+SOo5gjV1+MnZhk7IVQVNTzcsFfI0eh0pOZa/j7+l6nt5YGTvT0anY6E7Mote7BaCbzyyitMmzbN7Jo6de5EInx8fPDx8aFBgwY0btyY4OBgjh07RufOna3erDUkZetPoC6VNyWnupGm/4J4OUvbx1QFabnij5aXS+XuSRAEsvQpIXcn47ng1qLWasktEFOF7s5lO15Wrn4/Vr4fOfrUn6sFYxiz9J27rhbsMVu/H1eJuenFj51T+prcHP3+XKX3l5WlP4ar9DGy9cLaVcLmSBAEsjPE2908jG/P1Itady/p73VWWg46rQ6ZTIaHj3S5SWp8BgB+NUtvGIvTR8PuxXSismLYW3wpkTsQm4xAfA0EQZCMSHv66QVZknSNveG1z0yTFgpu7k4AZEkINYWdHGcXFbk5hWRn5uNx1/ABw2cmN6cQrUaHws44NuLq6kBBgbr4c2h0u4v4uczJLUCnE0zWZxu+M6a63+9el1XKuuL1+u9ttpkU/t0YfmMEAXLyi8r0m+Ohv09FpcbdnCr2eKbwchY/L2mVLHCtQSaT4enkSFJ2Lmm5eQS6V8+LzopEJpPh5+pMVFoGSdm51PaqvACW1cLT19cXX9+yWRPp9FdyhYWWfyHLSmKOPqpSTWod7wWpBpGn/yJXBwxXy5W9p7xCNRr956sihGfJk4ZrGSYWAWQUC0/L91NYpCm28LBkUpJBTLqZEYogNvwU6JtBXJ1NHzdbn740JzwNJ2oXE8/LEMlydZUW3MXC09349rzcwuLxhK7uxp8Zg9hx95L+Xqfro2zu3s6SxvMAqQkZACYnHpUkXu8YEFjHOh/We0lgqPh7nJWaQ05GLi4epi+2Pf3EKJa6SEN2ei5uEq+jl35NepJ01MPN0yA8pSNrbp564ZkuLSRc3RxF4SnhxVnyM5OTU4C7h/FnwMXZgeTkbJOC0cXljojLyy/CxcTn3SBQDd8hUxi+W6WtK358J8uOWxJ7OwVOKnvyCtVk5uSXSXi6GYSnFY9rDvd7JTxdxPe4siOr1uLt7KQXng9GxBNEvRSVlkFiduVGnyutxvP48eP8+OOPnDt3jlu3brFnzx4mTJhA3bp1Kz3aqdZqi0XYAyU8i0VeNYp46vfk7VK5wjMrX/xxtFcoKiSln6H/sXF1VKEoY0OJIeLpZsVJxHAfhVxmkZWSIQpTmvA0nARlMnA2E0k1RDxdXMytKdSvKS2iKX17VqbpiGd2hvh5UTnY4yDx/DPTxNsN4uZu0vVROk8TZQBwR3h6BpQuPOP0dYKBodVXeDq5OuKhf76lRT2VKnvc9BFLw+twN4bXLj25lIiniXS4uYgn3LngyJJIhSvs5DjphaKUMIU7n80cE8JTpbRDqS+hMBfNvBPxNB8IMXy3siwUdIbvuzXCs+T9yir0KloouukvfA2/rZWFtyHiWc0EniFYkpJb+fZC1QU/F1EvJeX8R4Wnk5MTa9eupW/fvjRs2JAnnniCFi1asH//flSqskWQLMXgQ2Uvl+PpVH1EWGVSqNaQVSD+gPq6VB+xfa/S/5nFaXZVhXi2Go7nUcY0O5RMtVsvPN2cHSx6HlmGCGUpj2E4ATs7qcxO3MmxIOJZLE5NptL1qXYTE4MMgsJNIqJZLEoloqFQesQzTR/xNCc80xLE+m9vM16mBuL1wjPITENWdcAQkY0z01BjwEv/vFMTpMfj3RGe0hFPw2tvMuKpj1IaLiKMbte/76aEpSHdbijZuBvDBY9ZUWmIZppdY5lAdLVSSLrry4qsjTx66H8jLRW4Ro9bSRHPrEqPeIrPO7ewqNKbWqzBRx8sSakAl4D/Cv6u4kVlZUc8K63bo3nz5uzZs6eyDm+WJL3w9HVxfmA62g1iW2mnwL2MqeHKoDgKW8kRz8wKrO+EEgKwHMcrS6o908ooaZZe2Je2PttKgWpKeKrVWgr0HpCmUpg52aaFp04nkGOmxjNLH0Vz95ROFxuibKZqPNOTRDFlSBdLkZZomfAszC8iNV5ssAmqxhFPEPd39XhEsVA2h3eAB1FXbhe/Dnfj5a9vdEmSvt3w2mel50rWiRqEZ6YJ4Wl437NNNP+4ujmQmJBJjkTzEZSMeJqOVLo4O5CalmsyKgqW13gWRzxzCkzWxf7fekNNqNURT/F5ZVhYS3o3xRHP/1iq3dVBhZ1CjkarIy03jyCPimkOLS9+ehF2LwzVqwsGG8bE7Mp9ztXLlK6CMKj1BynNnqIXeD4uztVqSpOhuaiyU+0VLTzLIhpLotMJZYp4Zuo7YUtLnRuwtLmoNEF59zpT9ZslLWycTQhPc6n23JwCDKYWLhK3G9KzpiOe5oVpccTTT7oZoCCvsNif0ruUVLshbe3s7oSriQhrdcEQkbWkwcjwvNNMptrF1y4zNRetxMhAQ42nVqMrvoj4v9tLiXgaPhemI556YZotLXgMn+EcE7f/3xoz4tRQ65xdyrhRwzqtTiAv39ib9G7KKjwNTYhljTC6V3Bz0b0SnjKZ7E66vRrVefroG5OTK1mEVSf8i4XnfzTVXpU8iMIzuTjKW30aiwDScgyp9nsjPMsToSxJcYd8GUsEcgsK0ekVllU1noaaTQvvYzhpWlrjaa6xCO6cqF1N1HiWlrIXBIGs4uYi4z0ZxIaDo31xHV5JDA0ppoRlVnFXu4nmIn2Np6mIpyHKp3K0x8lE85MBg4gLDPWrVhdzUhhqUOMsEJ5e+qYqUzWebl4uyBVyBEEgM9X4BKRU2eGo/xxlSdR5uumbm0qNeJoSnqXcbki155gRdoZofEWk2h2U9ij1deNZpYhUsL4m1IBBOFpjwyR1/8wKqpW8IzwrvxnYkBFLK+Nzrwx8H8SIp15s/2drPKsSg/A0hMofBFKy75QXVCfScu6NnVJFRzzLezxDustBaYfKimYnaxuSLG4usiDiqdHqiiM6phqH7tR3SgvTvLwidFpRcEs2D5lpLIKSEU/pC5U7qXbp29NKaS4y1Hd6BXiUKibj9JNqgupW7zQ7lKjxtGC6Tmk1ngqFHA/9KNTSLZWkhKdeOJoSnu6lCU/zEVFDbbGpiCiUiHiaEX+uFtSKGrgjJksXRgYBaKn9ktH9LBC3kvev6OaiexTxhDs9AIam4OpAccTzARKehmBdUnZuqSN4y8N9KTwNNZ4PZMSzGontIo22uOHJu5Ijnln5+sifYwUJz+Iaz7LVy2aWMVVvbXr+jlA1v09LajxLptFLq980aaWkFwtKpR0qlfE4THMenlAi4ilhowOW2ymZFJ6G+k5/D8nbS3In4lm9G4sAgvR7TI1Lp6jAfDrYUNtqqsYT7tgupZnqbDdjqWRIxefnFVFUZNwsUnqNpz7lbKq5SP/ZNDW5CEo2IJU/1Q53vl+WiMmyp9rLF7E0CMWCIg2FFdCk467/Lc3KL6hUEQJ3SrGqU6rdEMRJqUZ7qmwMEc8irZaMSnQzuC+FZ3HEsxp1d1c2BuHpU40inun6H1CFXFZhgtAUFWkeD3eEo0cZU+13hKd19y8WkhbWeBb7eJbyOHcinmY8PPVrHB3tsTPhgVnc9V6qOXwpt5uo4SyOeJoUnqU0F+mFkqFB5m6KPTytsFIKqsYengbcfV1xdHFAEAQSopLNri2txhNK9/I0ZyLv7KIqNm2XinqWlkp3K67xNN/1bj7iaXmq3WA0b447Xp6WiFRxbaFaS0GRutT1BtxdytccJFq/ia97RUQpDUJWo9WRV2j58ygLxan2amSpZChbyy0sIs+K9/G/jNLOrtgJqDLrPCt3hmE5MVxlZWVJX3WbIi4pBW1BAS4IVt/3v0pcUgrawgKcq9Fzjk5IRltYgIeLIzk5lTuCKzE1DW1hAfaCpkKef3JqGtqish8vPjEFbVEBDgqdVfdPShYf1w7LHjctLQONWgO6QrPrk1PS0KgLsJNpTa5LSExGoy7Awd3O5JqkpBQ0mgLs7KWPEx+XjEZTgErlInl7YnwyGm0B9irpz2lyYioabSEKpfHxCwvUxZ8jmZ3x7QX5RWRlilE8hUr6dyP2VhwaXRGOHvalvr63ImLQCEW4+jlVm++UOXxC3Ll5OYvrl2/gEWR60orSWY5GV0RifDIZGRnIJXxqHd0UaHSFxEbHSz53lbMcja6Q+NtJkrc7OMvJysgjNjYR+7uuQWQKDRptAakp6ZL3ldtp0GgKSElOlX7dBTUaTQGpadL3B5DLNWjUBSSnpJlcI2jFNQDxiclmswEqhQ6NuoCExBSysgJMrgPxvCVoi9BpdcTGJVk0+hZAoVOjLSogJdX0nkvDSSGQUVDA7YRkHGRlG71pQBAEFFo1RRotMQmJxSM5KwNHtGgLC4hPSq423zVBEFBq1eRrtNyIiyfEy6Oqt3RP8FTISSko4EZcPEFWOOQY3jdLouMyobJj6OUgNjaW4ODgqt6GDRs2bNiwYcOGjVKIiYmhZs2aZtdUa+Gp0+mIi4vD1dXVqq7SrKwsgoODiYmJwc2teniCPajY3ovqg+29qB7Y3ofqg+29qD7Y3ovqQVnfB0EQyM7OJigoSDKLUpJqnWqXy+WlKmdzuLm52T7A1QTbe1F9sL0X1QPb+1B9sL0X1Qfbe1E9KMv74O5uWTnGfdlcZMOGDRs2bNiwYaP6YROeNmzYsGHDhg0bNu4J96XwVKlUvPvuu6hU1Wdm+YOK7b2oPtjei+qB7X2oPtjei+qD7b2oHtyL96FaNxfZsGHDhg0bNmzYuH+4LyOeNmzYsGHDhg0bNqofNuFpw4YNGzZs2LBh455gE542bNiwYcOGDRs27gk24WnDhg0bNmzYsGHjnmATnjZs2LBhw4YNGzbuCfe98BwxYgQhISE4ODgQGBjI5MmTiYuLq+ptPXBERUXxxBNPEBoaiqOjI3Xr1uXdd9+lqKioqrf2QPLxxx/TpUsXnJyc8PDwqOrtPFDMnz+f2rVr4+DgQMeOHTlx4kRVb+mB48CBAwwfPpygoCBkMhnr16+v6i09kHz66ae0b98eV1dX/Pz8GDVqFGFhYVW9rQeSn376iRYtWhRPLOrcuTNbt26tlMe674Vn7969WblyJWFhYaxZs4bIyEjGjBlT1dt64Lh27Ro6nY5ffvmFy5cv8+233/Lzzz/zxhtvVPXWHkiKiooYO3YsM2bMqOqtPFCsWLGCWbNm8e6773LmzBlatmzJwIEDSUpKquqtPVDk5ubSsmVL5s+fX9VbeaDZv38/M2fO5NixY+zcuRO1Ws2AAQPIzc2t6q09cNSsWZPPPvuM06dPc+rUKfr06cPIkSO5fPlyhT/WA+fjuXHjRkaNGkVhYSH29vZVvZ0Hmi+//JKffvqJGzduVPVWHlgWLVrESy+9REZGRlVv5YGgY8eOtG/fnh9//BEAnU5HcHAwzz//PHPnzq3i3T2YyGQy1q1bx6hRo6p6Kw88ycnJ+Pn5sX//fnr06FHV23ng8fLy4ssvv+SJJ56o0OPe9xHPkqSlpbFkyRK6dOliE53VgMzMTLy8vKp6GzZs3BOKioo4ffo0/fr1K/4/uVxOv379OHr0aBXuzIaN6kFmZiaA7bxQxWi1WpYvX05ubi6dO3eu8OM/EMJzzpw5ODs74+3tTXR0NBs2bKjqLT3wREREMG/ePJ555pmq3ooNG/eElJQUtFot/v7+//f//v7+JCQkVNGubNioHuh0Ol566SW6du1Ks2bNqno7DyQXL17ExcUFlUrF9OnTWbduHU2aNKnwx/lPCs+5c+cik8nM/l27dq14/ezZszl79iw7duxAoVAwZcoUHrAKg0rD2vcC4Pbt2wwaNIixY8fy1FNPVdHO7z/K8l7YsGHDRnVg5syZXLp0ieXLl1f1Vh5YGjZsyLlz5zh+/DgzZsxg6tSpXLlypcIf5z9Z45mcnExqaqrZNXXq1EGpVBr9f2xsLMHBwRw5cqRSQsgPGta+F3FxcfTq1YtOnTqxaNEi5PL/5LVPtaQs3wtbjee9o6ioCCcnJ1avXv1/9YRTp04lIyPDlompImw1nlXPc889x4YNGzhw4AChoaFVvR0bevr160fdunX55ZdfKvS4dhV6tHuEr68vvr6+ZbqvTqcDoLCwsCK39MBizXtx+/ZtevfuTdu2bVm4cKFNdFYw5fle2Kh8lEolbdu2Zffu3cUiR6fTsXv3bp577rmq3ZwNG1WAIAg8//zzrFu3jn379tlEZzVDp9NVilb6TwpPSzl+/DgnT56kW7dueHp6EhkZydtvv03dunVt0c57zO3bt+nVqxe1atXiq6++Ijk5ufi2gICAKtzZg0l0dDRpaWlER0ej1Wo5d+4cAPXq1cPFxaVqN3cfM2vWLKZOnUq7du3o0KED3333Hbm5uTz22GNVvbUHipycHCIiIor/ffPmTc6dO4eXlxchISFVuLMHi5kzZ7J06VI2bNiAq6trca2zu7s7jo6OVby7B4vXX3+dwYMHExISQnZ2NkuXLmXfvn1s37694h9MuI+5cOGC0Lt3b8HLy0tQqVRC7dq1henTpwuxsbFVvbUHjoULFwqA5J+Ne8/UqVMl34u9e/dW9dbue+bNmyeEhIQISqVS6NChg3Ds2LGq3tIDx969eyU//1OnTq3qrT1QmDonLFy4sKq39sDx+OOPC7Vq1RKUSqXg6+sr9O3bV9ixY0elPNZ/ssbThg0bNmzYsGHDxn8PW5GdDRs2bNiwYcOGjXuCTXjasGHDhg0bNmzYuCfYhKcNGzZs2LBhw4aNe4JNeNqwYcOGDRs2bNi4J9iEpw0bNmzYsGHDho17gk142rBhw4YNGzZs2Lgn2ISnDRs2bNiwYcOGjXuCTXjasGHDhg0bNmzYuCfYhKcNGzZs2LBhw4aNe4JNeNqwYcOGDRs2bNi4J9iEpw0bNmzYsGHDho17wv8A+9zan5AUxc4AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -2297,7 +2446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 58,
    "id": "abd902e8",
    "metadata": {
     "execution": {
@@ -2311,7 +2460,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -3307,7 +3456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 62,
    "id": "d13a6775",
    "metadata": {
     "execution": {
@@ -3320,6 +3469,668 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cylinders",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "weight",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "acceleration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "year",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "2e9790b3-da72-498b-a031-742722360a67",
+       "rows": [
+        [
+         "0",
+         "18.0",
+         "8",
+         "307.0",
+         "130",
+         "3504",
+         "12.0",
+         "70",
+         "1",
+         "chevrolet chevelle malibu"
+        ],
+        [
+         "1",
+         "15.0",
+         "8",
+         "350.0",
+         "165",
+         "3693",
+         "11.5",
+         "70",
+         "1",
+         "buick skylark 320"
+        ],
+        [
+         "2",
+         "18.0",
+         "8",
+         "318.0",
+         "150",
+         "3436",
+         "11.0",
+         "70",
+         "1",
+         "plymouth satellite"
+        ],
+        [
+         "3",
+         "16.0",
+         "8",
+         "304.0",
+         "150",
+         "3433",
+         "12.0",
+         "70",
+         "1",
+         "amc rebel sst"
+        ],
+        [
+         "4",
+         "17.0",
+         "8",
+         "302.0",
+         "140",
+         "3449",
+         "10.5",
+         "70",
+         "1",
+         "ford torino"
+        ],
+        [
+         "5",
+         "15.0",
+         "8",
+         "429.0",
+         "198",
+         "4341",
+         "10.0",
+         "70",
+         "1",
+         "ford galaxie 500"
+        ],
+        [
+         "6",
+         "14.0",
+         "8",
+         "454.0",
+         "220",
+         "4354",
+         "9.0",
+         "70",
+         "1",
+         "chevrolet impala"
+        ],
+        [
+         "7",
+         "14.0",
+         "8",
+         "440.0",
+         "215",
+         "4312",
+         "8.5",
+         "70",
+         "1",
+         "plymouth fury iii"
+        ],
+        [
+         "8",
+         "14.0",
+         "8",
+         "455.0",
+         "225",
+         "4425",
+         "10.0",
+         "70",
+         "1",
+         "pontiac catalina"
+        ],
+        [
+         "9",
+         "15.0",
+         "8",
+         "390.0",
+         "190",
+         "3850",
+         "8.5",
+         "70",
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+         "8.0",
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+         "plymouth 'cuda 340"
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+         "15.0",
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+         "toyota corona mark ii"
+        ],
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+         "15.5",
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+         "plymouth duster"
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+         "200.0",
+         "85",
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+         "16.0",
+         "70",
+         "1",
+         "ford maverick"
+        ],
+        [
+         "18",
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+         "4",
+         "97.0",
+         "88",
+         "2130",
+         "14.5",
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+         "3",
+         "datsun pl510"
+        ],
+        [
+         "19",
+         "26.0",
+         "4",
+         "97.0",
+         "46",
+         "1835",
+         "20.5",
+         "70",
+         "2",
+         "volkswagen 1131 deluxe sedan"
+        ],
+        [
+         "20",
+         "25.0",
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+         "110.0",
+         "87",
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+         "17.5",
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+         "peugeot 504"
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+        [
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+         "107.0",
+         "90",
+         "2430",
+         "14.5",
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+         "2",
+         "audi 100 ls"
+        ],
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+         "17.5",
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+         "saab 99e"
+        ],
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+         "26.0",
+         "4",
+         "121.0",
+         "113",
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+         "12.5",
+         "70",
+         "2",
+         "bmw 2002"
+        ],
+        [
+         "24",
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+         "6",
+         "199.0",
+         "90",
+         "2648",
+         "15.0",
+         "70",
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+         "amc gremlin"
+        ],
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+         "215",
+         "4615",
+         "14.0",
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+         "1",
+         "ford f250"
+        ],
+        [
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+         "307.0",
+         "200",
+         "4376",
+         "15.0",
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+         "chevy c20"
+        ],
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+         "11.0",
+         "8",
+         "318.0",
+         "210",
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+         "13.5",
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+        ],
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+        ],
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+         "15.5",
+         "71",
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+         "chevrolet vega 2300"
+        ],
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+         "95",
+         "2228",
+         "14.0",
+         "71",
+         "3",
+         "toyota corona"
+        ],
+        [
+         "32",
+         "19.0",
+         "6",
+         "232.0",
+         "100",
+         "2634",
+         "13.0",
+         "71",
+         "1",
+         "amc gremlin"
+        ],
+        [
+         "33",
+         "16.0",
+         "6",
+         "225.0",
+         "105",
+         "3439",
+         "15.5",
+         "71",
+         "1",
+         "plymouth satellite custom"
+        ],
+        [
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+         "17.0",
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+         "250.0",
+         "100",
+         "3329",
+         "15.5",
+         "71",
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+         "chevrolet chevelle malibu"
+        ],
+        [
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+         "19.0",
+         "6",
+         "250.0",
+         "88",
+         "3302",
+         "15.5",
+         "71",
+         "1",
+         "ford torino 500"
+        ],
+        [
+         "36",
+         "18.0",
+         "6",
+         "232.0",
+         "100",
+         "3288",
+         "15.5",
+         "71",
+         "1",
+         "amc matador"
+        ],
+        [
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+         "14.0",
+         "8",
+         "350.0",
+         "165",
+         "4209",
+         "12.0",
+         "71",
+         "1",
+         "chevrolet impala"
+        ],
+        [
+         "38",
+         "14.0",
+         "8",
+         "400.0",
+         "175",
+         "4464",
+         "11.5",
+         "71",
+         "1",
+         "pontiac catalina brougham"
+        ],
+        [
+         "39",
+         "14.0",
+         "8",
+         "351.0",
+         "153",
+         "4154",
+         "13.5",
+         "71",
+         "1",
+         "ford galaxie 500"
+        ],
+        [
+         "40",
+         "14.0",
+         "8",
+         "318.0",
+         "150",
+         "4096",
+         "13.0",
+         "71",
+         "1",
+         "plymouth fury iii"
+        ],
+        [
+         "41",
+         "12.0",
+         "8",
+         "383.0",
+         "180",
+         "4955",
+         "11.5",
+         "71",
+         "1",
+         "dodge monaco (sw)"
+        ],
+        [
+         "42",
+         "13.0",
+         "8",
+         "400.0",
+         "170",
+         "4746",
+         "12.0",
+         "71",
+         "1",
+         "ford country squire (sw)"
+        ],
+        [
+         "43",
+         "13.0",
+         "8",
+         "400.0",
+         "175",
+         "5140",
+         "12.0",
+         "71",
+         "1",
+         "pontiac safari (sw)"
+        ],
+        [
+         "44",
+         "18.0",
+         "6",
+         "258.0",
+         "110",
+         "2962",
+         "13.5",
+         "71",
+         "1",
+         "amc hornet sportabout (sw)"
+        ],
+        [
+         "45",
+         "22.0",
+         "4",
+         "140.0",
+         "72",
+         "2408",
+         "19.0",
+         "71",
+         "1",
+         "chevrolet vega (sw)"
+        ],
+        [
+         "46",
+         "19.0",
+         "6",
+         "250.0",
+         "100",
+         "3282",
+         "15.0",
+         "71",
+         "1",
+         "pontiac firebird"
+        ],
+        [
+         "47",
+         "18.0",
+         "6",
+         "250.0",
+         "88",
+         "3139",
+         "14.5",
+         "71",
+         "1",
+         "ford mustang"
+        ],
+        [
+         "48",
+         "23.0",
+         "4",
+         "122.0",
+         "86",
+         "2220",
+         "14.0",
+         "71",
+         "1",
+         "mercury capri 2000"
+        ],
+        [
+         "49",
+         "28.0",
+         "4",
+         "116.0",
+         "90",
+         "2123",
+         "14.0",
+         "71",
+         "2",
+         "opel 1900"
+        ]
+       ],
+       "shape": {
+        "columns": 9,
+        "rows": 392
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -3518,7 +4329,7 @@
        "[392 rows x 9 columns]"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3539,7 +4350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 64,
    "id": "f7857be6",
    "metadata": {
     "execution": {
@@ -3550,322 +4361,671 @@
     },
     "lines_to_next_cell": 0
    },
-   "outputs": [],
-   "source": [
-    "Auto = pd.read_csv('Auto.data', delim_whitespace=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a373fcb4",
-   "metadata": {},
-   "source": [
-    " Both `Auto.csv` and `Auto.data` are simply text\n",
-    "files. Before loading data into `Python`, it is a good idea to view it using\n",
-    "a text editor or other software, such as Microsoft Excel.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bba17c66",
-   "metadata": {},
-   "source": [
-    "We now take a look at the column of `Auto` corresponding to the variable `horsepower`: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "7912c27e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.328873Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.328782Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.331187Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.330966Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      130.0\n",
-       "1      165.0\n",
-       "2      150.0\n",
-       "3      150.0\n",
-       "4      140.0\n",
-       "       ...  \n",
-       "392    86.00\n",
-       "393    52.00\n",
-       "394    84.00\n",
-       "395    79.00\n",
-       "396    82.00\n",
-       "Name: horsepower, Length: 397, dtype: object"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto['horsepower']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af443415",
-   "metadata": {},
-   "source": [
-    "We see that the `dtype` of this column is `object`. \n",
-    "It turns out that all values of the `horsepower` column were interpreted as strings when reading\n",
-    "in the data. \n",
-    "We can find out why by looking at the unique values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "f8078632",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.332465Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.332343Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.334500Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.334302Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['100.0', '102.0', '103.0', '105.0', '107.0', '108.0', '110.0',\n",
-       "       '112.0', '113.0', '115.0', '116.0', '120.0', '122.0', '125.0',\n",
-       "       '129.0', '130.0', '132.0', '133.0', '135.0', '137.0', '138.0',\n",
-       "       '139.0', '140.0', '142.0', '145.0', '148.0', '149.0', '150.0',\n",
-       "       '152.0', '153.0', '155.0', '158.0', '160.0', '165.0', '167.0',\n",
-       "       '170.0', '175.0', '180.0', '190.0', '193.0', '198.0', '200.0',\n",
-       "       '208.0', '210.0', '215.0', '220.0', '225.0', '230.0', '46.00',\n",
-       "       '48.00', '49.00', '52.00', '53.00', '54.00', '58.00', '60.00',\n",
-       "       '61.00', '62.00', '63.00', '64.00', '65.00', '66.00', '67.00',\n",
-       "       '68.00', '69.00', '70.00', '71.00', '72.00', '74.00', '75.00',\n",
-       "       '76.00', '77.00', '78.00', '79.00', '80.00', '81.00', '82.00',\n",
-       "       '83.00', '84.00', '85.00', '86.00', '87.00', '88.00', '89.00',\n",
-       "       '90.00', '91.00', '92.00', '93.00', '94.00', '95.00', '96.00',\n",
-       "       '97.00', '98.00', '?'], dtype=object)"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.unique(Auto['horsepower'])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46fb50cb",
-   "metadata": {},
-   "source": [
-    "We see the culprit is the value `?`, which is being used to encode missing values.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71e7a9fe",
-   "metadata": {},
-   "source": [
-    "To fix the problem, we must provide `pd.read_csv()` with an argument called `na_values`.\n",
-    "Now,  each instance of  `?` in the file is replaced with the\n",
-    "value `np.nan`, which means *not a number*:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "aad623ae",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.335798Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.335712Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.338898Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.338695Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "40952.0"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto = pd.read_csv('Auto.data',\n",
-    "                   na_values=['?'],\n",
-    "                   delim_whitespace=True)\n",
-    "Auto['horsepower'].sum()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8aa30cf6",
-   "metadata": {},
-   "source": [
-    "The `Auto.shape`  attribute tells us that the data has 397\n",
-    "observations, or rows, and nine variables, or columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "id": "ae1cad4e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.340150Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.340081Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.341961Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.341744Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(397, 9)"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a699c56e",
-   "metadata": {},
-   "source": [
-    "There are\n",
-    "various ways to deal with  missing data. \n",
-    "In this case, since only five of the rows contain missing\n",
-    "observations,  we choose to use the `Auto.dropna()` method to simply remove these rows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "52eb7971",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.343152Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.343088Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.345337Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.345111Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(392, 9)"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto_new = Auto.dropna()\n",
-    "Auto_new.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55a60e42",
-   "metadata": {},
-   "source": [
-    "### Basics of Selecting Rows and Columns\n",
-    " \n",
-    "We can use `Auto.columns`  to check the variable names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "id": "44829ab6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.346569Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.346495Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.348509Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.348265Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n",
-       "       'acceleration', 'year', 'origin', 'name'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto = Auto_new # overwrite the previous value\n",
-    "Auto.columns\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a397a994",
-   "metadata": {},
-   "source": [
-    "Accessing the rows and columns of a data frame is similar, but not identical, to accessing the rows and columns of an array. \n",
-    "Recall that the first argument to the `[]` method\n",
-    "is always applied to the rows of the array.  \n",
-    "Similarly, \n",
-    "passing in a slice to the `[]` method creates a data frame whose *rows* are determined by the slice:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "id": "3aad1bdb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-05T01:13:24.349852Z",
-     "iopub.status.busy": "2024-06-05T01:13:24.349784Z",
-     "iopub.status.idle": "2024-06-05T01:13:24.353582Z",
-     "shell.execute_reply": "2024-06-05T01:13:24.353356Z"
-    },
-    "lines_to_next_cell": 0
-   },
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
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@@ -3933,637 +5093,1000 @@
        "      <td>1</td>\n",
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-       "    mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
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-       "1  15.0          8         350.0       165.0  3693.0          11.5    70   \n",
-       "2  18.0          8         318.0       150.0  3436.0          11.0    70   \n",
-       "\n",
-       "   origin                       name  \n",
-       "0       1  chevrolet chevelle malibu  \n",
-       "1       1          buick skylark 320  \n",
-       "2       1         plymouth satellite  "
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        "      <td>1</td>\n",
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-       "      <td>toyota starlet</td>\n",
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+       "      <td>2790.0</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>82</td>\n",
        "      <td>1</td>\n",
-       "      <td>plymouth champ</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>345</th>\n",
-       "      <td>35.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>1760.0</td>\n",
-       "      <td>16.1</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>honda civic 1300</td>\n",
+       "      <td>ford mustang gl</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>346</th>\n",
-       "      <td>32.3</td>\n",
+       "      <th>393</th>\n",
+       "      <td>44.0</td>\n",
        "      <td>4</td>\n",
        "      <td>97.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>2065.0</td>\n",
-       "      <td>17.8</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>subaru</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>347</th>\n",
-       "      <td>37.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>1975.0</td>\n",
-       "      <td>19.4</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 210 mpg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>348</th>\n",
-       "      <td>37.7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>62.0</td>\n",
-       "      <td>2050.0</td>\n",
-       "      <td>17.3</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota tercel</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>349</th>\n",
-       "      <td>34.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>1985.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>mazda glc 4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>350</th>\n",
-       "      <td>34.7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>2215.0</td>\n",
-       "      <td>14.9</td>\n",
-       "      <td>81</td>\n",
-       "      <td>1</td>\n",
-       "      <td>plymouth horizon 4</td>\n",
+       "      <td>52.00</td>\n",
+       "      <td>2130.0</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>82</td>\n",
+       "      <td>2</td>\n",
+       "      <td>vw pickup</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>351</th>\n",
-       "      <td>34.4</td>\n",
+       "      <th>394</th>\n",
+       "      <td>32.0</td>\n",
        "      <td>4</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>2045.0</td>\n",
-       "      <td>16.2</td>\n",
-       "      <td>81</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>84.00</td>\n",
+       "      <td>2295.0</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>82</td>\n",
        "      <td>1</td>\n",
-       "      <td>ford escort 4w</td>\n",
+       "      <td>dodge rampage</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>352</th>\n",
-       "      <td>29.9</td>\n",
+       "      <th>395</th>\n",
+       "      <td>28.0</td>\n",
        "      <td>4</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>65.0</td>\n",
-       "      <td>2380.0</td>\n",
-       "      <td>20.7</td>\n",
-       "      <td>81</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>79.00</td>\n",
+       "      <td>2625.0</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>82</td>\n",
        "      <td>1</td>\n",
-       "      <td>ford escort 2h</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>353</th>\n",
-       "      <td>33.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>2190.0</td>\n",
-       "      <td>14.2</td>\n",
-       "      <td>81</td>\n",
-       "      <td>2</td>\n",
-       "      <td>volkswagen jetta</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>355</th>\n",
-       "      <td>33.7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>107.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>2210.0</td>\n",
-       "      <td>14.4</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>honda prelude</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>356</th>\n",
-       "      <td>32.4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>108.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>2350.0</td>\n",
-       "      <td>16.8</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota corolla</td>\n",
+       "      <td>ford ranger</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>357</th>\n",
-       "      <td>32.9</td>\n",
+       "      <th>396</th>\n",
+       "      <td>31.0</td>\n",
        "      <td>4</td>\n",
        "      <td>119.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>2615.0</td>\n",
-       "      <td>14.8</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 200sx</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>358</th>\n",
-       "      <td>31.6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>2635.0</td>\n",
-       "      <td>18.3</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>mazda 626</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>359</th>\n",
-       "      <td>28.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>141.0</td>\n",
-       "      <td>80.0</td>\n",
-       "      <td>3230.0</td>\n",
-       "      <td>20.4</td>\n",
-       "      <td>81</td>\n",
-       "      <td>2</td>\n",
-       "      <td>peugeot 505s turbo diesel</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>360</th>\n",
-       "      <td>30.7</td>\n",
-       "      <td>6</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>3160.0</td>\n",
-       "      <td>19.6</td>\n",
-       "      <td>81</td>\n",
-       "      <td>2</td>\n",
-       "      <td>volvo diesel</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>361</th>\n",
-       "      <td>25.4</td>\n",
-       "      <td>6</td>\n",
-       "      <td>168.0</td>\n",
-       "      <td>116.0</td>\n",
-       "      <td>2900.0</td>\n",
-       "      <td>12.6</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota cressida</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>362</th>\n",
-       "      <td>24.2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>146.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>2930.0</td>\n",
-       "      <td>13.8</td>\n",
-       "      <td>81</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 810 maxima</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>363</th>\n",
-       "      <td>22.4</td>\n",
-       "      <td>6</td>\n",
-       "      <td>231.0</td>\n",
-       "      <td>110.0</td>\n",
-       "      <td>3415.0</td>\n",
-       "      <td>15.8</td>\n",
-       "      <td>81</td>\n",
+       "      <td>82.00</td>\n",
+       "      <td>2720.0</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>82</td>\n",
        "      <td>1</td>\n",
-       "      <td>buick century</td>\n",
+       "      <td>chevy s-10</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>364</th>\n",
-       "      <td>26.6</td>\n",
-       "      <td>8</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>3725.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>81</td>\n",
-       "      <td>1</td>\n",
-       "      <td>oldsmobile cutlass ls</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>365</th>\n",
-       "      <td>20.2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>3060.0</td>\n",
-       "      <td>17.1</td>\n",
-       "      <td>81</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ford granada gl</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>366</th>\n",
-       "      <td>17.6</td>\n",
-       "      <td>6</td>\n",
-       "      <td>225.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>3465.0</td>\n",
-       "      <td>16.6</td>\n",
-       "      <td>81</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chrysler lebaron salon</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>367</th>\n",
-       "      <td>28.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>2605.0</td>\n",
-       "      <td>19.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevrolet cavalier</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>368</th>\n",
-       "      <td>27.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>2640.0</td>\n",
-       "      <td>18.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevrolet cavalier wagon</td>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>397 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  cylinders  displacement horsepower  weight  acceleration  year  \\\n",
+       "0    18.0          8         307.0      130.0  3504.0          12.0    70   \n",
+       "1    15.0          8         350.0      165.0  3693.0          11.5    70   \n",
+       "2    18.0          8         318.0      150.0  3436.0          11.0    70   \n",
+       "3    16.0          8         304.0      150.0  3433.0          12.0    70   \n",
+       "4    17.0          8         302.0      140.0  3449.0          10.5    70   \n",
+       "..    ...        ...           ...        ...     ...           ...   ...   \n",
+       "392  27.0          4         140.0      86.00  2790.0          15.6    82   \n",
+       "393  44.0          4          97.0      52.00  2130.0          24.6    82   \n",
+       "394  32.0          4         135.0      84.00  2295.0          11.6    82   \n",
+       "395  28.0          4         120.0      79.00  2625.0          18.6    82   \n",
+       "396  31.0          4         119.0      82.00  2720.0          19.4    82   \n",
+       "\n",
+       "     origin                       name  \n",
+       "0         1  chevrolet chevelle malibu  \n",
+       "1         1          buick skylark 320  \n",
+       "2         1         plymouth satellite  \n",
+       "3         1              amc rebel sst  \n",
+       "4         1                ford torino  \n",
+       "..      ...                        ...  \n",
+       "392       1            ford mustang gl  \n",
+       "393       2                  vw pickup  \n",
+       "394       1              dodge rampage  \n",
+       "395       1                ford ranger  \n",
+       "396       1                 chevy s-10  \n",
+       "\n",
+       "[397 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto = pd.read_csv('Auto.data', sep = '\\s+')#delim_whitespace=True)\n",
+    "Auto"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a373fcb4",
+   "metadata": {},
+   "source": [
+    " Both `Auto.csv` and `Auto.data` are simply text\n",
+    "files. Before loading data into `Python`, it is a good idea to view it using\n",
+    "a text editor or other software, such as Microsoft Excel.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bba17c66",
+   "metadata": {},
+   "source": [
+    "We now take a look at the column of `Auto` corresponding to the variable `horsepower`: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "7912c27e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.328873Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.328782Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.331187Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.330966Z"
+    },
+    "lines_to_next_cell": 0
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      130.0\n",
+       "1      165.0\n",
+       "2      150.0\n",
+       "3      150.0\n",
+       "4      140.0\n",
+       "       ...  \n",
+       "392    86.00\n",
+       "393    52.00\n",
+       "394    84.00\n",
+       "395    79.00\n",
+       "396    82.00\n",
+       "Name: horsepower, Length: 397, dtype: object"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto['horsepower']\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af443415",
+   "metadata": {},
+   "source": [
+    "We see that the `dtype` of this column is `object`. \n",
+    "It turns out that all values of the `horsepower` column were interpreted as strings when reading\n",
+    "in the data. \n",
+    "We can find out why by looking at the unique values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "f8078632",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.332465Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.332343Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.334500Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.334302Z"
+    },
+    "lines_to_next_cell": 0
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['100.0', '102.0', '103.0', '105.0', '107.0', '108.0', '110.0',\n",
+       "       '112.0', '113.0', '115.0', '116.0', '120.0', '122.0', '125.0',\n",
+       "       '129.0', '130.0', '132.0', '133.0', '135.0', '137.0', '138.0',\n",
+       "       '139.0', '140.0', '142.0', '145.0', '148.0', '149.0', '150.0',\n",
+       "       '152.0', '153.0', '155.0', '158.0', '160.0', '165.0', '167.0',\n",
+       "       '170.0', '175.0', '180.0', '190.0', '193.0', '198.0', '200.0',\n",
+       "       '208.0', '210.0', '215.0', '220.0', '225.0', '230.0', '46.00',\n",
+       "       '48.00', '49.00', '52.00', '53.00', '54.00', '58.00', '60.00',\n",
+       "       '61.00', '62.00', '63.00', '64.00', '65.00', '66.00', '67.00',\n",
+       "       '68.00', '69.00', '70.00', '71.00', '72.00', '74.00', '75.00',\n",
+       "       '76.00', '77.00', '78.00', '79.00', '80.00', '81.00', '82.00',\n",
+       "       '83.00', '84.00', '85.00', '86.00', '87.00', '88.00', '89.00',\n",
+       "       '90.00', '91.00', '92.00', '93.00', '94.00', '95.00', '96.00',\n",
+       "       '97.00', '98.00', '?'], dtype=object)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.unique(Auto['horsepower'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46fb50cb",
+   "metadata": {},
+   "source": [
+    "We see the culprit is the value `?`, which is being used to encode missing values.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71e7a9fe",
+   "metadata": {},
+   "source": [
+    "To fix the problem, we must provide `pd.read_csv()` with an argument called `na_values`.\n",
+    "Now,  each instance of  `?` in the file is replaced with the\n",
+    "value `np.nan`, which means *not a number*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "aad623ae",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.335798Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.335712Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.338898Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.338695Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_208199/134681464.py:1: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
+      "  Auto = pd.read_csv('Auto.data',\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "40952.0"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto = pd.read_csv('Auto.data',\n",
+    "                   na_values=['?'],\n",
+    "                   delim_whitespace=True)\n",
+    "Auto['horsepower'].sum()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8aa30cf6",
+   "metadata": {},
+   "source": [
+    "The `Auto.shape`  attribute tells us that the data has 397\n",
+    "observations, or rows, and nine variables, or columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "ae1cad4e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.340150Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.340081Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.341961Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.341744Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(397, 9)"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto.shape\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a699c56e",
+   "metadata": {},
+   "source": [
+    "There are\n",
+    "various ways to deal with  missing data. \n",
+    "In this case, since only five of the rows contain missing\n",
+    "observations,  we choose to use the `Auto.dropna()` method to simply remove these rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "52eb7971",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.343152Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.343088Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.345337Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.345111Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(392, 9)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto_new = Auto.dropna()\n",
+    "Auto_new.shape\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55a60e42",
+   "metadata": {},
+   "source": [
+    "### Basics of Selecting Rows and Columns\n",
+    " \n",
+    "We can use `Auto.columns`  to check the variable names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "44829ab6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.346569Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.346495Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.348509Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.348265Z"
+    },
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n",
+       "       'acceleration', 'year', 'origin', 'name'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto = Auto_new # overwrite the previous value\n",
+    "Auto.columns\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a397a994",
+   "metadata": {},
+   "source": [
+    "Accessing the rows and columns of a data frame is similar, but not identical, to accessing the rows and columns of an array. \n",
+    "Recall that the first argument to the `[]` method\n",
+    "is always applied to the rows of the array.  \n",
+    "Similarly, \n",
+    "passing in a slice to the `[]` method creates a data frame whose *rows* are determined by the slice:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "3aad1bdb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.349852Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.349784Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.353582Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.353356Z"
+    },
+    "lines_to_next_cell": 0
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cylinders",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "weight",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "acceleration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "year",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "f6b9d6fd-efe8-4a86-aeef-c4d14915c790",
+       "rows": [
+        [
+         "0",
+         "18.0",
+         "8",
+         "307.0",
+         "130.0",
+         "3504.0",
+         "12.0",
+         "70",
+         "1",
+         "chevrolet chevelle malibu"
+        ],
+        [
+         "1",
+         "15.0",
+         "8",
+         "350.0",
+         "165.0",
+         "3693.0",
+         "11.5",
+         "70",
+         "1",
+         "buick skylark 320"
+        ],
+        [
+         "2",
+         "18.0",
+         "8",
+         "318.0",
+         "150.0",
+         "3436.0",
+         "11.0",
+         "70",
+         "1",
+         "plymouth satellite"
+        ]
+       ],
+       "shape": {
+        "columns": 9,
+        "rows": 3
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
        "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>369</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>2395.0</td>\n",
+       "      <th>0</th>\n",
        "      <td>18.0</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevrolet cavalier 2-door</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>370</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>2575.0</td>\n",
-       "      <td>16.2</td>\n",
-       "      <td>82</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
        "      <td>1</td>\n",
-       "      <td>pontiac j2000 se hatchback</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>371</th>\n",
-       "      <td>29.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>2525.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>82</td>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
        "      <td>1</td>\n",
-       "      <td>dodge aries se</td>\n",
+       "      <td>buick skylark 320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>372</th>\n",
-       "      <td>27.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>151.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>2735.0</td>\n",
+       "      <th>2</th>\n",
        "      <td>18.0</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>pontiac phoenix</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>373</th>\n",
-       "      <td>24.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>2865.0</td>\n",
-       "      <td>16.4</td>\n",
-       "      <td>82</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
        "      <td>1</td>\n",
-       "      <td>ford fairmont futura</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>374</th>\n",
-       "      <td>36.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>1980.0</td>\n",
-       "      <td>15.3</td>\n",
-       "      <td>82</td>\n",
-       "      <td>2</td>\n",
-       "      <td>volkswagen rabbit l</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>375</th>\n",
-       "      <td>37.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>2025.0</td>\n",
-       "      <td>18.2</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>mazda glc custom l</td>\n",
+       "      <td>plymouth satellite</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>376</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>68.0</td>\n",
-       "      <td>1970.0</td>\n",
-       "      <td>17.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>mazda glc custom</td>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
+       "0  18.0          8         307.0       130.0  3504.0          12.0    70   \n",
+       "1  15.0          8         350.0       165.0  3693.0          11.5    70   \n",
+       "2  18.0          8         318.0       150.0  3436.0          11.0    70   \n",
+       "\n",
+       "   origin                       name  \n",
+       "0       1  chevrolet chevelle malibu  \n",
+       "1       1          buick skylark 320  \n",
+       "2       1         plymouth satellite  "
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Auto[:3]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b42c876",
+   "metadata": {},
+   "source": [
+    "Similarly, an array of Booleans can be used to subset the rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "ae1e9a3a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-05T01:13:24.354951Z",
+     "iopub.status.busy": "2024-06-05T01:13:24.354871Z",
+     "iopub.status.idle": "2024-06-05T01:13:24.366222Z",
+     "shell.execute_reply": "2024-06-05T01:13:24.365980Z"
+    },
+    "lines_to_next_cell": 0
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cylinders",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "weight",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "acceleration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "year",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "4ba65194-8830-4f75-8274-90a324cadf17",
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+         "3160.0",
+         "19.6",
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+         "volvo diesel"
+        ],
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+         "116.0",
+         "2900.0",
+         "12.6",
+         "81",
+         "3",
+         "toyota cressida"
+        ],
+        [
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+         "24.2",
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+         "120.0",
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+         "13.8",
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+         "datsun 810 maxima"
+        ],
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+         "231.0",
+         "110.0",
+         "3415.0",
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+         "1",
+         "buick century"
+        ],
+        [
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+         "26.6",
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+         "350.0",
+         "105.0",
+         "3725.0",
+         "19.0",
+         "81",
+         "1",
+         "oldsmobile cutlass ls"
+        ],
+        [
+         "365",
+         "20.2",
+         "6",
+         "200.0",
+         "88.0",
+         "3060.0",
+         "17.1",
+         "81",
+         "1",
+         "ford granada gl"
+        ],
+        [
+         "366",
+         "17.6",
+         "6",
+         "225.0",
+         "85.0",
+         "3465.0",
+         "16.6",
+         "81",
+         "1",
+         "chrysler lebaron salon"
+        ],
+        [
+         "385",
+         "25.0",
+         "6",
+         "181.0",
+         "110.0",
+         "2945.0",
+         "16.4",
+         "82",
+         "1",
+         "buick century limited"
+        ],
+        [
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+         "38.0",
+         "6",
+         "262.0",
+         "85.0",
+         "3015.0",
+         "17.0",
+         "82",
+         "1",
+         "oldsmobile cutlass ciera (diesel)"
+        ],
+        [
+         "388",
+         "22.0",
+         "6",
+         "232.0",
+         "112.0",
+         "2835.0",
+         "14.7",
+         "82",
+         "1",
+         "ford granada l"
+        ]
+       ],
+       "shape": {
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+        "rows": 11
+       }
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+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
        "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>377</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>105.0</td>\n",
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-       "      <td>2125.0</td>\n",
-       "      <td>14.7</td>\n",
-       "      <td>82</td>\n",
+       "      <th>341</th>\n",
+       "      <td>23.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>173.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>2725.0</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>81</td>\n",
        "      <td>1</td>\n",
-       "      <td>plymouth horizon miser</td>\n",
+       "      <td>chevrolet citation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>378</th>\n",
-       "      <td>36.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>2125.0</td>\n",
-       "      <td>17.3</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>mercury lynx l</td>\n",
+       "      <th>360</th>\n",
+       "      <td>30.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>145.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>3160.0</td>\n",
+       "      <td>19.6</td>\n",
+       "      <td>81</td>\n",
+       "      <td>2</td>\n",
+       "      <td>volvo diesel</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>379</th>\n",
-       "      <td>36.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>2160.0</td>\n",
-       "      <td>14.5</td>\n",
-       "      <td>82</td>\n",
+       "      <th>361</th>\n",
+       "      <td>25.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>168.0</td>\n",
+       "      <td>116.0</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>81</td>\n",
        "      <td>3</td>\n",
-       "      <td>nissan stanza xe</td>\n",
+       "      <td>toyota cressida</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>380</th>\n",
-       "      <td>36.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>107.0</td>\n",
-       "      <td>75.0</td>\n",
-       "      <td>2205.0</td>\n",
-       "      <td>14.5</td>\n",
-       "      <td>82</td>\n",
+       "      <th>362</th>\n",
+       "      <td>24.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>146.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>2930.0</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>81</td>\n",
        "      <td>3</td>\n",
-       "      <td>honda accord</td>\n",
+       "      <td>datsun 810 maxima</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>381</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>108.0</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>2245.0</td>\n",
-       "      <td>16.9</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota corolla</td>\n",
+       "      <th>363</th>\n",
+       "      <td>22.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>231.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>3415.0</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>buick century</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>382</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1965.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>honda civic</td>\n",
+       "      <th>364</th>\n",
+       "      <td>26.6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>3725.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>oldsmobile cutlass ls</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>383</th>\n",
-       "      <td>32.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1965.0</td>\n",
-       "      <td>15.7</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>honda civic (auto)</td>\n",
+       "      <th>365</th>\n",
+       "      <td>20.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>3060.0</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford granada gl</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>384</th>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>91.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1995.0</td>\n",
-       "      <td>16.2</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>datsun 310 gx</td>\n",
+       "      <th>366</th>\n",
+       "      <td>17.6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>225.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>3465.0</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chrysler lebaron salon</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>385</th>\n",
@@ -4590,18 +6113,6 @@
        "      <td>oldsmobile cutlass ciera (diesel)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>387</th>\n",
-       "      <td>26.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>156.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>2585.0</td>\n",
-       "      <td>14.5</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chrysler lebaron medallion</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>388</th>\n",
        "      <td>22.0</td>\n",
        "      <td>6</td>\n",
@@ -4613,129 +6124,13 @@
        "      <td>1</td>\n",
        "      <td>ford granada l</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>389</th>\n",
-       "      <td>32.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>144.0</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>2665.0</td>\n",
-       "      <td>13.9</td>\n",
-       "      <td>82</td>\n",
-       "      <td>3</td>\n",
-       "      <td>toyota celica gt</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>390</th>\n",
-       "      <td>36.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>2370.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>dodge charger 2.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>391</th>\n",
-       "      <td>27.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>151.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>2950.0</td>\n",
-       "      <td>17.3</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevrolet camaro</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>392</th>\n",
-       "      <td>27.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>2790.0</td>\n",
-       "      <td>15.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ford mustang gl</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>393</th>\n",
-       "      <td>44.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>2130.0</td>\n",
-       "      <td>24.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>2</td>\n",
-       "      <td>vw pickup</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>394</th>\n",
-       "      <td>32.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>2295.0</td>\n",
-       "      <td>11.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>dodge rampage</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>395</th>\n",
-       "      <td>28.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>2625.0</td>\n",
-       "      <td>18.6</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ford ranger</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>396</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>119.0</td>\n",
-       "      <td>82.0</td>\n",
-       "      <td>2720.0</td>\n",
-       "      <td>19.4</td>\n",
-       "      <td>82</td>\n",
-       "      <td>1</td>\n",
-       "      <td>chevy s-10</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "      mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
-       "338  27.2          4         135.0        84.0  2490.0          15.7    81   \n",
-       "339  26.6          4         151.0        84.0  2635.0          16.4    81   \n",
-       "340  25.8          4         156.0        92.0  2620.0          14.4    81   \n",
        "341  23.5          6         173.0       110.0  2725.0          12.6    81   \n",
-       "342  30.0          4         135.0        84.0  2385.0          12.9    81   \n",
-       "343  39.1          4          79.0        58.0  1755.0          16.9    81   \n",
-       "344  39.0          4          86.0        64.0  1875.0          16.4    81   \n",
-       "345  35.1          4          81.0        60.0  1760.0          16.1    81   \n",
-       "346  32.3          4          97.0        67.0  2065.0          17.8    81   \n",
-       "347  37.0          4          85.0        65.0  1975.0          19.4    81   \n",
-       "348  37.7          4          89.0        62.0  2050.0          17.3    81   \n",
-       "349  34.1          4          91.0        68.0  1985.0          16.0    81   \n",
-       "350  34.7          4         105.0        63.0  2215.0          14.9    81   \n",
-       "351  34.4          4          98.0        65.0  2045.0          16.2    81   \n",
-       "352  29.9          4          98.0        65.0  2380.0          20.7    81   \n",
-       "353  33.0          4         105.0        74.0  2190.0          14.2    81   \n",
-       "355  33.7          4         107.0        75.0  2210.0          14.4    81   \n",
-       "356  32.4          4         108.0        75.0  2350.0          16.8    81   \n",
-       "357  32.9          4         119.0       100.0  2615.0          14.8    81   \n",
-       "358  31.6          4         120.0        74.0  2635.0          18.3    81   \n",
-       "359  28.1          4         141.0        80.0  3230.0          20.4    81   \n",
        "360  30.7          6         145.0        76.0  3160.0          19.6    81   \n",
        "361  25.4          6         168.0       116.0  2900.0          12.6    81   \n",
        "362  24.2          6         146.0       120.0  2930.0          13.8    81   \n",
@@ -4743,59 +6138,12 @@
        "364  26.6          8         350.0       105.0  3725.0          19.0    81   \n",
        "365  20.2          6         200.0        88.0  3060.0          17.1    81   \n",
        "366  17.6          6         225.0        85.0  3465.0          16.6    81   \n",
-       "367  28.0          4         112.0        88.0  2605.0          19.6    82   \n",
-       "368  27.0          4         112.0        88.0  2640.0          18.6    82   \n",
-       "369  34.0          4         112.0        88.0  2395.0          18.0    82   \n",
-       "370  31.0          4         112.0        85.0  2575.0          16.2    82   \n",
-       "371  29.0          4         135.0        84.0  2525.0          16.0    82   \n",
-       "372  27.0          4         151.0        90.0  2735.0          18.0    82   \n",
-       "373  24.0          4         140.0        92.0  2865.0          16.4    82   \n",
-       "374  36.0          4         105.0        74.0  1980.0          15.3    82   \n",
-       "375  37.0          4          91.0        68.0  2025.0          18.2    82   \n",
-       "376  31.0          4          91.0        68.0  1970.0          17.6    82   \n",
-       "377  38.0          4         105.0        63.0  2125.0          14.7    82   \n",
-       "378  36.0          4          98.0        70.0  2125.0          17.3    82   \n",
-       "379  36.0          4         120.0        88.0  2160.0          14.5    82   \n",
-       "380  36.0          4         107.0        75.0  2205.0          14.5    82   \n",
-       "381  34.0          4         108.0        70.0  2245.0          16.9    82   \n",
-       "382  38.0          4          91.0        67.0  1965.0          15.0    82   \n",
-       "383  32.0          4          91.0        67.0  1965.0          15.7    82   \n",
-       "384  38.0          4          91.0        67.0  1995.0          16.2    82   \n",
        "385  25.0          6         181.0       110.0  2945.0          16.4    82   \n",
        "386  38.0          6         262.0        85.0  3015.0          17.0    82   \n",
-       "387  26.0          4         156.0        92.0  2585.0          14.5    82   \n",
        "388  22.0          6         232.0       112.0  2835.0          14.7    82   \n",
-       "389  32.0          4         144.0        96.0  2665.0          13.9    82   \n",
-       "390  36.0          4         135.0        84.0  2370.0          13.0    82   \n",
-       "391  27.0          4         151.0        90.0  2950.0          17.3    82   \n",
-       "392  27.0          4         140.0        86.0  2790.0          15.6    82   \n",
-       "393  44.0          4          97.0        52.0  2130.0          24.6    82   \n",
-       "394  32.0          4         135.0        84.0  2295.0          11.6    82   \n",
-       "395  28.0          4         120.0        79.0  2625.0          18.6    82   \n",
-       "396  31.0          4         119.0        82.0  2720.0          19.4    82   \n",
        "\n",
        "     origin                               name  \n",
-       "338       1                   plymouth reliant  \n",
-       "339       1                      buick skylark  \n",
-       "340       1             dodge aries wagon (sw)  \n",
        "341       1                 chevrolet citation  \n",
-       "342       1                   plymouth reliant  \n",
-       "343       3                     toyota starlet  \n",
-       "344       1                     plymouth champ  \n",
-       "345       3                   honda civic 1300  \n",
-       "346       3                             subaru  \n",
-       "347       3                     datsun 210 mpg  \n",
-       "348       3                      toyota tercel  \n",
-       "349       3                        mazda glc 4  \n",
-       "350       1                 plymouth horizon 4  \n",
-       "351       1                     ford escort 4w  \n",
-       "352       1                     ford escort 2h  \n",
-       "353       2                   volkswagen jetta  \n",
-       "355       3                      honda prelude  \n",
-       "356       3                     toyota corolla  \n",
-       "357       3                       datsun 200sx  \n",
-       "358       3                          mazda 626  \n",
-       "359       2          peugeot 505s turbo diesel  \n",
        "360       2                       volvo diesel  \n",
        "361       3                    toyota cressida  \n",
        "362       3                  datsun 810 maxima  \n",
@@ -4803,45 +6151,18 @@
        "364       1              oldsmobile cutlass ls  \n",
        "365       1                    ford granada gl  \n",
        "366       1             chrysler lebaron salon  \n",
-       "367       1                 chevrolet cavalier  \n",
-       "368       1           chevrolet cavalier wagon  \n",
-       "369       1          chevrolet cavalier 2-door  \n",
-       "370       1         pontiac j2000 se hatchback  \n",
-       "371       1                     dodge aries se  \n",
-       "372       1                    pontiac phoenix  \n",
-       "373       1               ford fairmont futura  \n",
-       "374       2                volkswagen rabbit l  \n",
-       "375       3                 mazda glc custom l  \n",
-       "376       3                   mazda glc custom  \n",
-       "377       1             plymouth horizon miser  \n",
-       "378       1                     mercury lynx l  \n",
-       "379       3                   nissan stanza xe  \n",
-       "380       3                       honda accord  \n",
-       "381       3                     toyota corolla  \n",
-       "382       3                        honda civic  \n",
-       "383       3                 honda civic (auto)  \n",
-       "384       3                      datsun 310 gx  \n",
        "385       1              buick century limited  \n",
        "386       1  oldsmobile cutlass ciera (diesel)  \n",
-       "387       1         chrysler lebaron medallion  \n",
-       "388       1                     ford granada l  \n",
-       "389       3                   toyota celica gt  \n",
-       "390       1                  dodge charger 2.2  \n",
-       "391       1                   chevrolet camaro  \n",
-       "392       1                    ford mustang gl  \n",
-       "393       2                          vw pickup  \n",
-       "394       1                      dodge rampage  \n",
-       "395       1                        ford ranger  \n",
-       "396       1                         chevy s-10  "
+       "388       1                     ford granada l  "
       ]
      },
-     "execution_count": 83,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "idx_80 = Auto['year'] > 80\n",
+    "idx_80 = (Auto['year'] > 80) & (Auto['cylinders'] > 4)\n",
     "Auto[idx_80]\n"
    ]
   },
@@ -4855,7 +6176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 77,
    "id": "2b543957",
    "metadata": {
     "execution": {
@@ -4869,6 +6190,283 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "72c170a4-6cab-41bd-9a00-bf37fb78ffa2",
+       "rows": [
+        [
+         "0",
+         "18.0",
+         "130.0"
+        ],
+        [
+         "1",
+         "15.0",
+         "165.0"
+        ],
+        [
+         "2",
+         "18.0",
+         "150.0"
+        ],
+        [
+         "3",
+         "16.0",
+         "150.0"
+        ],
+        [
+         "4",
+         "17.0",
+         "140.0"
+        ],
+        [
+         "5",
+         "15.0",
+         "198.0"
+        ],
+        [
+         "6",
+         "14.0",
+         "220.0"
+        ],
+        [
+         "7",
+         "14.0",
+         "215.0"
+        ],
+        [
+         "8",
+         "14.0",
+         "225.0"
+        ],
+        [
+         "9",
+         "15.0",
+         "190.0"
+        ],
+        [
+         "10",
+         "15.0",
+         "170.0"
+        ],
+        [
+         "11",
+         "14.0",
+         "160.0"
+        ],
+        [
+         "12",
+         "15.0",
+         "150.0"
+        ],
+        [
+         "13",
+         "14.0",
+         "225.0"
+        ],
+        [
+         "14",
+         "24.0",
+         "95.0"
+        ],
+        [
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@@ -5031,7 +6629,7 @@
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@@ -5044,6 +6642,613 @@
    "outputs": [
     {
      "data": {
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+         "71",
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+         "100.0",
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+         "19.0",
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+         "250.0",
+         "88.0",
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+         "15.5",
+         "71",
+         "1"
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+         "18.0",
+         "6",
+         "232.0",
+         "100.0",
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+         "350.0",
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@@ -5243,7 +7448,7 @@
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@@ -5255,7 +7460,7 @@
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@@ -5275,7 +7480,7 @@
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@@ -5298,7 +7503,7 @@
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-   "execution_count": 88,
+   "execution_count": 84,
    "id": "04e94eee",
    "metadata": {
     "execution": {
@@ -5312,6 +7517,85 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
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+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
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+        {
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+         "type": "float"
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        "<div>\n",
        "<style scoped>\n",
@@ -5391,7 +7675,7 @@
        "ford torino            10.5    70       1  "
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      },
-     "execution_count": 88,
+     "execution_count": 84,
      "metadata": {},
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@@ -5411,7 +7695,7 @@
   },
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+   "execution_count": 85,
    "id": "5cd1291d",
    "metadata": {
     "execution": {
@@ -5425,6 +7709,85 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
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+        {
+         "name": "cylinders",
+         "rawType": "int64",
+         "type": "integer"
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+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
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+        {
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+        {
+         "name": "acceleration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "year",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        }
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+       "conversionMethod": "pd.DataFrame",
+       "ref": "a75c0323-38b8-4e30-a24c-6361e02c515e",
+       "rows": [
+        [
+         "amc rebel sst",
+         "16.0",
+         "8",
+         "304.0",
+         "150.0",
+         "3433.0",
+         "12.0",
+         "70",
+         "1"
+        ],
+        [
+         "ford torino",
+         "17.0",
+         "8",
+         "302.0",
+         "140.0",
+         "3449.0",
+         "10.5",
+         "70",
+         "1"
+        ]
+       ],
+       "shape": {
+        "columns": 8,
+        "rows": 2
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -5504,7 +7867,7 @@
        "ford torino            10.5    70       1  "
       ]
      },
-     "execution_count": 89,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5523,7 +7886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 86,
    "id": "5f1f3970",
    "metadata": {
     "execution": {
@@ -5537,6 +7900,338 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "224e617e-6a30-4f51-be0d-2e7cd609674b",
+       "rows": [
+        [
+         "chevrolet chevelle malibu",
+         "18.0",
+         "307.0",
+         "130.0"
+        ],
+        [
+         "buick skylark 320",
+         "15.0",
+         "350.0",
+         "165.0"
+        ],
+        [
+         "plymouth satellite",
+         "18.0",
+         "318.0",
+         "150.0"
+        ],
+        [
+         "amc rebel sst",
+         "16.0",
+         "304.0",
+         "150.0"
+        ],
+        [
+         "ford torino",
+         "17.0",
+         "302.0",
+         "140.0"
+        ],
+        [
+         "ford galaxie 500",
+         "15.0",
+         "429.0",
+         "198.0"
+        ],
+        [
+         "chevrolet impala",
+         "14.0",
+         "454.0",
+         "220.0"
+        ],
+        [
+         "plymouth fury iii",
+         "14.0",
+         "440.0",
+         "215.0"
+        ],
+        [
+         "pontiac catalina",
+         "14.0",
+         "455.0",
+         "225.0"
+        ],
+        [
+         "amc ambassador dpl",
+         "15.0",
+         "390.0",
+         "190.0"
+        ],
+        [
+         "dodge challenger se",
+         "15.0",
+         "383.0",
+         "170.0"
+        ],
+        [
+         "plymouth 'cuda 340",
+         "14.0",
+         "340.0",
+         "160.0"
+        ],
+        [
+         "chevrolet monte carlo",
+         "15.0",
+         "400.0",
+         "150.0"
+        ],
+        [
+         "buick estate wagon (sw)",
+         "14.0",
+         "455.0",
+         "225.0"
+        ],
+        [
+         "toyota corona mark ii",
+         "24.0",
+         "113.0",
+         "95.0"
+        ],
+        [
+         "plymouth duster",
+         "22.0",
+         "198.0",
+         "95.0"
+        ],
+        [
+         "amc hornet",
+         "18.0",
+         "199.0",
+         "97.0"
+        ],
+        [
+         "ford maverick",
+         "21.0",
+         "200.0",
+         "85.0"
+        ],
+        [
+         "datsun pl510",
+         "27.0",
+         "97.0",
+         "88.0"
+        ],
+        [
+         "volkswagen 1131 deluxe sedan",
+         "26.0",
+         "97.0",
+         "46.0"
+        ],
+        [
+         "peugeot 504",
+         "25.0",
+         "110.0",
+         "87.0"
+        ],
+        [
+         "audi 100 ls",
+         "24.0",
+         "107.0",
+         "90.0"
+        ],
+        [
+         "saab 99e",
+         "25.0",
+         "104.0",
+         "95.0"
+        ],
+        [
+         "bmw 2002",
+         "26.0",
+         "121.0",
+         "113.0"
+        ],
+        [
+         "amc gremlin",
+         "21.0",
+         "199.0",
+         "90.0"
+        ],
+        [
+         "ford f250",
+         "10.0",
+         "360.0",
+         "215.0"
+        ],
+        [
+         "chevy c20",
+         "10.0",
+         "307.0",
+         "200.0"
+        ],
+        [
+         "dodge d200",
+         "11.0",
+         "318.0",
+         "210.0"
+        ],
+        [
+         "hi 1200d",
+         "9.0",
+         "304.0",
+         "193.0"
+        ],
+        [
+         "datsun pl510",
+         "27.0",
+         "97.0",
+         "88.0"
+        ],
+        [
+         "chevrolet vega 2300",
+         "28.0",
+         "140.0",
+         "90.0"
+        ],
+        [
+         "toyota corona",
+         "25.0",
+         "113.0",
+         "95.0"
+        ],
+        [
+         "amc gremlin",
+         "19.0",
+         "232.0",
+         "100.0"
+        ],
+        [
+         "plymouth satellite custom",
+         "16.0",
+         "225.0",
+         "105.0"
+        ],
+        [
+         "chevrolet chevelle malibu",
+         "17.0",
+         "250.0",
+         "100.0"
+        ],
+        [
+         "ford torino 500",
+         "19.0",
+         "250.0",
+         "88.0"
+        ],
+        [
+         "amc matador",
+         "18.0",
+         "232.0",
+         "100.0"
+        ],
+        [
+         "chevrolet impala",
+         "14.0",
+         "350.0",
+         "165.0"
+        ],
+        [
+         "pontiac catalina brougham",
+         "14.0",
+         "400.0",
+         "175.0"
+        ],
+        [
+         "ford galaxie 500",
+         "14.0",
+         "351.0",
+         "153.0"
+        ],
+        [
+         "plymouth fury iii",
+         "14.0",
+         "318.0",
+         "150.0"
+        ],
+        [
+         "dodge monaco (sw)",
+         "12.0",
+         "383.0",
+         "180.0"
+        ],
+        [
+         "ford country squire (sw)",
+         "13.0",
+         "400.0",
+         "170.0"
+        ],
+        [
+         "pontiac safari (sw)",
+         "13.0",
+         "400.0",
+         "175.0"
+        ],
+        [
+         "amc hornet sportabout (sw)",
+         "18.0",
+         "258.0",
+         "110.0"
+        ],
+        [
+         "chevrolet vega (sw)",
+         "22.0",
+         "140.0",
+         "72.0"
+        ],
+        [
+         "pontiac firebird",
+         "19.0",
+         "250.0",
+         "100.0"
+        ],
+        [
+         "ford mustang",
+         "18.0",
+         "250.0",
+         "88.0"
+        ],
+        [
+         "mercury capri 2000",
+         "23.0",
+         "122.0",
+         "86.0"
+        ],
+        [
+         "opel 1900",
+         "28.0",
+         "116.0",
+         "90.0"
+        ]
+       ],
+       "shape": {
+        "columns": 3,
+        "rows": 392
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -5657,7 +8352,7 @@
        "[392 rows x 3 columns]"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5677,7 +8372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 93,
    "id": "a9429e4b",
    "metadata": {
     "execution": {
@@ -5691,6 +8386,50 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "2780b563-67f0-49b3-b7f1-01d97e5677fc",
+       "rows": [
+        [
+         "amc rebel sst",
+         "16.0",
+         "304.0",
+         "150.0"
+        ],
+        [
+         "ford torino",
+         "17.0",
+         "302.0",
+         "140.0"
+        ]
+       ],
+       "shape": {
+        "columns": 3,
+        "rows": 2
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -5745,13 +8484,13 @@
        "ford torino    17.0         302.0       140.0"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Auto_re.iloc[[3,4],[0,2,3]]\n"
+    "Auto_re.iloc[[3,4],[0,2,3]]"
    ]
   },
   {
@@ -5764,7 +8503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 97,
    "id": "1fd20b7d",
    "metadata": {
     "execution": {
@@ -5778,6 +8517,96 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cylinders",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "displacement",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "horsepower",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "weight",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "acceleration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "year",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "15820715-16dc-47fb-8462-a192331665cd",
+       "rows": [
+        [
+         "ford galaxie 500",
+         "15.0",
+         "8",
+         "429.0",
+         "198.0",
+         "4341.0",
+         "10.0",
+         "70",
+         "1"
+        ],
+        [
+         "ford galaxie 500",
+         "14.0",
+         "8",
+         "351.0",
+         "153.0",
+         "4154.0",
+         "13.5",
+         "71",
+         "1"
+        ],
+        [
+         "ford galaxie 500",
+         "14.0",
+         "8",
+         "351.0",
+         "153.0",
+         "4129.0",
+         "13.0",
+         "72",
+         "1"
+        ]
+       ],
+       "shape": {
+        "columns": 8,
+        "rows": 3
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -5798,28 +8627,58 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>year</th>\n",
        "      <th>origin</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>ford galaxie 500</th>\n",
        "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>429.0</td>\n",
+       "      <td>198.0</td>\n",
+       "      <td>4341.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>70</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford galaxie 500</th>\n",
        "      <td>14.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>351.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>4154.0</td>\n",
+       "      <td>13.5</td>\n",
+       "      <td>71</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ford galaxie 500</th>\n",
        "      <td>14.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>351.0</td>\n",
+       "      <td>153.0</td>\n",
+       "      <td>4129.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>72</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -5827,20 +8686,26 @@
        "</div>"
       ],
       "text/plain": [
-       "                   mpg  origin\n",
-       "name                          \n",
-       "ford galaxie 500  15.0       1\n",
-       "ford galaxie 500  14.0       1\n",
-       "ford galaxie 500  14.0       1"
+       "                   mpg  cylinders  displacement  horsepower  weight  \\\n",
+       "name                                                                  \n",
+       "ford galaxie 500  15.0          8         429.0       198.0  4341.0   \n",
+       "ford galaxie 500  14.0          8         351.0       153.0  4154.0   \n",
+       "ford galaxie 500  14.0          8         351.0       153.0  4129.0   \n",
+       "\n",
+       "                  acceleration  year  origin  \n",
+       "name                                          \n",
+       "ford galaxie 500          10.0    70       1  \n",
+       "ford galaxie 500          13.5    71       1  \n",
+       "ford galaxie 500          13.0    72       1  "
       ]
      },
-     "execution_count": 92,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Auto_re.loc['ford galaxie 500', ['mpg', 'origin']]\n"
+    "Auto_re.loc[['ford galaxie 500']]#, ['mpg', 'origin']]\n"
    ]
   },
   {
@@ -5857,7 +8722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 98,
    "id": "df6fa9de",
    "metadata": {
     "execution": {
@@ -5871,6 +8736,283 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "weight",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "origin",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "7e7491e7-3a1c-4dc0-9d8e-ba727e37b618",
+       "rows": [
+        [
+         "plymouth reliant",
+         "2490.0",
+         "1"
+        ],
+        [
+         "buick skylark",
+         "2635.0",
+         "1"
+        ],
+        [
+         "dodge aries wagon (sw)",
+         "2620.0",
+         "1"
+        ],
+        [
+         "chevrolet citation",
+         "2725.0",
+         "1"
+        ],
+        [
+         "plymouth reliant",
+         "2385.0",
+         "1"
+        ],
+        [
+         "toyota starlet",
+         "1755.0",
+         "3"
+        ],
+        [
+         "plymouth champ",
+         "1875.0",
+         "1"
+        ],
+        [
+         "honda civic 1300",
+         "1760.0",
+         "3"
+        ],
+        [
+         "subaru",
+         "2065.0",
+         "3"
+        ],
+        [
+         "datsun 210 mpg",
+         "1975.0",
+         "3"
+        ],
+        [
+         "toyota tercel",
+         "2050.0",
+         "3"
+        ],
+        [
+         "mazda glc 4",
+         "1985.0",
+         "3"
+        ],
+        [
+         "plymouth horizon 4",
+         "2215.0",
+         "1"
+        ],
+        [
+         "ford escort 4w",
+         "2045.0",
+         "1"
+        ],
+        [
+         "ford escort 2h",
+         "2380.0",
+         "1"
+        ],
+        [
+         "volkswagen jetta",
+         "2190.0",
+         "2"
+        ],
+        [
+         "honda prelude",
+         "2210.0",
+         "3"
+        ],
+        [
+         "toyota corolla",
+         "2350.0",
+         "3"
+        ],
+        [
+         "datsun 200sx",
+         "2615.0",
+         "3"
+        ],
+        [
+         "mazda 626",
+         "2635.0",
+         "3"
+        ],
+        [
+         "peugeot 505s turbo diesel",
+         "3230.0",
+         "2"
+        ],
+        [
+         "volvo diesel",
+         "3160.0",
+         "2"
+        ],
+        [
+         "toyota cressida",
+         "2900.0",
+         "3"
+        ],
+        [
+         "datsun 810 maxima",
+         "2930.0",
+         "3"
+        ],
+        [
+         "buick century",
+         "3415.0",
+         "1"
+        ],
+        [
+         "oldsmobile cutlass ls",
+         "3725.0",
+         "1"
+        ],
+        [
+         "ford granada gl",
+         "3060.0",
+         "1"
+        ],
+        [
+         "chrysler lebaron salon",
+         "3465.0",
+         "1"
+        ],
+        [
+         "chevrolet cavalier",
+         "2605.0",
+         "1"
+        ],
+        [
+         "chevrolet cavalier wagon",
+         "2640.0",
+         "1"
+        ],
+        [
+         "chevrolet cavalier 2-door",
+         "2395.0",
+         "1"
+        ],
+        [
+         "pontiac j2000 se hatchback",
+         "2575.0",
+         "1"
+        ],
+        [
+         "dodge aries se",
+         "2525.0",
+         "1"
+        ],
+        [
+         "pontiac phoenix",
+         "2735.0",
+         "1"
+        ],
+        [
+         "ford fairmont futura",
+         "2865.0",
+         "1"
+        ],
+        [
+         "volkswagen rabbit l",
+         "1980.0",
+         "2"
+        ],
+        [
+         "mazda glc custom l",
+         "2025.0",
+         "3"
+        ],
+        [
+         "mazda glc custom",
+         "1970.0",
+         "3"
+        ],
+        [
+         "plymouth horizon miser",
+         "2125.0",
+         "1"
+        ],
+        [
+         "mercury lynx l",
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+         "1"
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+         "2160.0",
+         "3"
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+         "3"
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+         "3"
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+        [
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+         "3"
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+         "3"
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+        [
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+         "1"
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@@ -6257,7 +9399,7 @@
        "chevy s-10                         2720.0       1"
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      },
-     "execution_count": 93,
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@@ -6277,7 +9419,7 @@
   },
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    "id": "b57f8503",
    "metadata": {
     "execution": {
@@ -6291,6 +9433,283 @@
    "outputs": [
     {
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+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
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+         "rawType": "object",
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+        {
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+         "3"
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+        [
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+         "3"
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+         "2160.0",
+         "3"
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+        [
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+         "3"
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+        [
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+         "3"
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+         "1"
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+        [
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+         "3015.0",
+         "1"
+        ],
+        [
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+         "1"
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@@ -6677,7 +10096,7 @@
        "chevy s-10                         2720.0       1"
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      },
-     "execution_count": 94,
+     "execution_count": 99,
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@@ -6701,7 +10120,7 @@
   },
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    "id": "965b5b74",
    "metadata": {
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@@ -6715,6 +10134,203 @@
    "outputs": [
     {
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+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
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+        {
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+         "1975.0",
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@@ -6957,7 +10573,7 @@
        "chevy s-10                         2720.0       1"
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@@ -6981,7 +10597,7 @@
   },
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    "id": "37ba9d44",
    "metadata": {
     "execution": {
@@ -6995,6 +10611,283 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "name",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
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+         "3"
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+        [
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+         "3"
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+        [
+         "ford pinto (sw)",
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+         "1"
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+        [
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+         "2019.0",
+         "3"
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+        [
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+        [
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@@ -7339,7 +11232,7 @@
        "ford ranger            2625.0       1"
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+     "execution_count": 103,
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@@ -7523,7 +11416,7 @@
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    "id": "35700d0c",
    "metadata": {
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@@ -7537,6 +11430,72 @@
    "outputs": [
     {
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@@ -7599,7 +11558,7 @@
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@@ -7619,7 +11578,7 @@
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@@ -7676,7 +11635,7 @@
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@@ -7693,15 +11652,15 @@
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      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[102], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m subplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(\u001b[43mhorsepower\u001b[49m, mpg, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mo\u001b[39m\u001b[38;5;124m'\u001b[39m);\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'horsepower' is not defined"
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[109]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m fig, ax = subplots(figsize=(\u001b[32m8\u001b[39m, \u001b[32m8\u001b[39m))\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m ax.plot(\u001b[43mhorsepower\u001b[49m, mpg, \u001b[33m'\u001b[39m\u001b[33mo\u001b[39m\u001b[33m'\u001b[39m);\n",
+      "\u001b[31mNameError\u001b[39m: name 'horsepower' is not defined"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -7725,7 +11684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 110,
    "id": "dae69229",
    "metadata": {
     "execution": {
@@ -7739,7 +11698,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -7767,7 +11726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 111,
    "id": "9fbba9c1",
    "metadata": {
     "execution": {
@@ -7781,7 +11740,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -7838,7 +11797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 112,
    "id": "c0e67313",
    "metadata": {
     "execution": {
@@ -7851,7 +11810,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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drYsvvlgJCQkaPny4fvzjH+vEiRPBfJTwZISBF1980ejfv7/x9NNPG//85z+NG264wRg6dKhx+PBhh+2Li4uN2NhY48EHHzQ+/vhj4+677zbi4uKMvXv3BjnywPB2PK666ipj3bp1RklJibFv3z7juuuuM5KSkoxPP/00yJEHhrfjYVNZWWmcdNJJxty5c43LLrssOMEGibdjcuzYMeP00083LrroImPbtm1GZWWlsXnzZqO0tDTIkQeGt+Px/PPPG/Hx8cbzzz9vVFZWGm+99ZYxatQo47bbbgty5IHxxhtvGHfddZfxyiuvGJKMV1991WX7iooKIyEhwVixYoXx8ccfG4WFhUZsbKzx5ptvBifgIOA9Y8Z7pifeNWa8Z8x4z5jxngm+VatWGaeddprxxRdf2L+sVqv9/E033WSkp6cbGzduND744APjjDPOMM4888wQRhxe3P2dXbt2rZGUlGS89tprxj/+8Q/j0ksvNTIzM40vv/zS3uaCCy4wpk2bZrz33nvGu+++a2RlZRkFBQVBfpLw4W5Mr732WuOCCy4w/Z09cuSIqQ1j+o0FCxYYzzzzjPHRRx8ZpaWlxkUXXWSMHTvWaGlpsbdx99/5iRMnjMmTJxvz5883SkpKjDfeeMNITU01Vq5cGYpHCithkfiaPXu2sXTpUvv3HR0dxujRo401a9Y4bP+9733PuPjii03H5syZY3z/+98PaJzB4u14dHfixAljyJAhxnPPPReoEIPKl/E4ceKEceaZZxq/+93vjGuvvTaqfhkxDO/H5IknnjDGjx9vtLe3ByvEoPJ2PJYuXWqce+65pmMrVqww8vLyAhpnKHjyC8lPfvIT47TTTjMdu/LKK40FCxYEMLLg4j1jxnumJ941ZrxnzHjPOMd7JjhWrVplTJs2zeG5xsZGIy4uznj55Zftx/bt22dIMnbs2BGkCCNH97+znZ2dxsiRI41f/epX9mONjY1GfHy8UVRUZBiGYXz88ceGJGPXrl32Nv/3f/9nWCwW47PPPgta7OHKWeLL1XuRMXWttrbWkGRs2bLFMAzP/jt/4403jJiYGKOmpsbe5oknnjASExONY8eOBfcBwkzIlzq2t7dr9+7dmj9/vv1YTEyM5s+frx07dji8ZseOHab2krRgwQKn7SOJL+PRXVtbm44fP65hw4YFKsyg8XU87r//fg0fPlxLliwJRphB5cuYvP7668rNzdXSpUs1YsQITZ48Wb/4xS/U0dERrLADxpfxOPPMM7V79277MpWKigq98cYbuuiii4ISc7iJ5s9UifdMd7xneuJdY8Z7xoz3TO9F82dqMO3fv1+jR4/W+PHjdfXVV6u6ulqStHv3bh0/ftw0xqeccorGjh3LGHugsrJSNTU1pvFLSkrSnDlz7OO3Y8cODR06VKeffrq9zfz58xUTE6P3338/6DFHis2bN2v48OGaOHGibr75ZtXX19vPMaauNTU1SZL9/7U8+e98x44dmjJlikaMGGFvs2DBAjU3N+uf//xnEKMPP/1CHUBdXZ06OjpMPxxJGjFihP71r385vKampsZh+5qamoDFGSy+jEd3d9xxh0aPHt3jfzAikS/jsW3bNq1fv16lpaVBiDD4fBmTiooK/f3vf9fVV1+tN954Q+Xl5frBD36g48ePa9WqVcEIO2B8GY+rrrpKdXV1Ouuss2QYhk6cOKGbbrpJP/3pT4MRcthx9pna3NysL7/8UgMHDgxRZP7Be8aM90xPvGvMeM+Y8Z7pvWh/zwTDnDlz9Oyzz2rixIn64osvtHr1as2dO1cfffSRampq1L9/fw0dOtR0TbS8twLNNkau3vs1NTUaPny46Xy/fv00bNgwxtiJCy64QJdffrkyMzN14MAB/fSnP9WFF16oHTt2KDY2ljF1obOzU7feeqvy8vI0efJkSfLov3Nnn7W2c31ZyBNf8K+1a9fqxRdf1ObNmzVgwIBQhxN0R48e1TXXXKOnnnpKqampoQ4nbHR2dmr48OH6n//5H8XGxmrmzJn67LPP9Ktf/SrifyHxxebNm/WLX/xCjz/+uObMmaPy8nL98Ic/1M9+9jPdc889oQ4PCGt9/T0j8a5xhPeMGe8Z+NuFF15o//PUqVM1Z84cjRs3Tr///e9JHCIs/cd//If9z1OmTNHUqVM1YcIEbd68Weedd14IIwt/S5cu1UcffaRt27aFOpSoEfLEV2pqqmJjY3vsRnD48GGNHDnS4TUjR470qn0k8WU8bB566CGtXbtWf/vb3zR16tRAhhk03o7HgQMHVFVVpUsuucR+rLOzU9LX/4JQVlamCRMmBDboAPPl78ioUaMUFxen2NhY+7FTTz1VNTU1am9vV//+/QMacyD5Mh733HOPrrnmGl1//fWSvn4Zt7a26sYbb9Rdd92lmJiQrwIPKmefqYmJiVHxP9O8Z8x4z/TEu8aM94wZ75nei/b3TCgMHTpUJ598ssrLy/Wtb31L7e3tamxsNM0GiZb3VqDZxujw4cMaNWqU/fjhw4c1ffp0e5va2lrTdSdOnNCRI0cYYw+NHz9eqampKi8v13nnnceYOnHLLbfoL3/5i7Zu3aoxY8bYj48cOdLtf+cjR47sseOw7bO3L4+pJIX8rdu/f3/NnDlTGzdutB/r7OzUxo0blZub6/Ca3NxcU3tJeuedd5y2jyS+jIckPfjgg/rZz36mN99807ROOtJ5Ox6nnHKK9u7dq9LSUvvXpZdeqnPOOUelpaVKT08PZvgB4cvfkby8PJWXl9t/MZOkTz75RKNGjYroX0Yk38ajra2txy8dtl/WDMMIXLBhKpo/UyXeM93xnumJd40Z7xkz3jO9F82fqaHS0tKiAwcOaNSoUZo5c6bi4uJMY1xWVqbq6mrG2AOZmZkaOXKkafyam5v1/vvv28cvNzdXjY2N2r17t73N3//+d3V2dmrOnDlBjzkSffrpp6qvr7cnFxlTM8MwdMstt+jVV1/V3//+d2VmZprOe/LfeW5urvbu3WtKKL7zzjtKTEzUpEmTgvMg4SqkpfX/7cUXXzTi4+ONZ5991vj444+NG2+80Rg6dKh9N4JrrrnGuPPOO+3ti4uLjX79+hkPPfSQsW/fPmPVqlVRt828N+Oxdu1ao3///sYf/vAH03axR48eDdUj+JW349FdtO20ZRjej0l1dbUxZMgQ45ZbbjHKysqMv/zlL8bw4cONBx54IFSP4FfejseqVauMIUOGGEVFRUZFRYXx9ttvGxMmTDC+973vheoR/Oro0aNGSUmJUVJSYkgyHnnkEaOkpMQ4ePCgYRiGceeddxrXXHONvb1tm/kf//jHxr59+4x169ZF3TbzvGfMeM/0xLvGjPeMGe8ZM94zwfejH/3I2Lx5s1FZWWkUFxcb8+fPN1JTU43a2lrDMAzjpptuMsaOHWv8/e9/Nz744AMjNzfXyM3NDXHU4cPd39m1a9caQ4cONf70pz8ZH374oXHZZZcZmZmZxpdffmnv44ILLjBycnKM999/39i2bZuRnZ1tFBQUhOqRQs7VmB49etS4/fbbjR07dhiVlZXG3/72N2PGjBlGdna28dVXX9n7YEy/cfPNNxtJSUnG5s2bTf+v1dbWZm/j7r/zEydOGJMnTzbOP/98o7S01HjzzTeNtLQ0Y+XKlaF4pLASFokvwzCMwsJCY+zYsUb//v2N2bNnG++995793Nlnn21ce+21pva///3vjZNPPtno37+/cdpppxl//etfgxxxYHkzHuPGjTMk9fhatWpV8AMPEG//fnQVbb+M2Hg7Jtu3bzfmzJljxMfHG+PHjzd+/vOfGydOnAhy1IHjzXgcP37cuO+++4wJEyYYAwYMMNLT040f/OAHRkNDQ/ADD4BNmzY5/EywjcG1115rnH322T2umT59utG/f39j/PjxxjPPPBP0uAON94wZ75meeNeY8Z4x4z3zDd4zwXfllVcao0aNMvr372+cdNJJxpVXXmmUl5fbz3/55ZfGD37wAyM5OdlISEgwvv3tbxtffPFFCCMOL+7+znZ2dhr33HOPMWLECCM+Pt4477zzjLKyMlMf9fX1RkFBgTF48GAjMTHRWLx4cVT9A5C3XI1pW1ubcf755xtpaWlGXFycMW7cOOOGG26w/2OBDWP6DUdjKcn0WenJf+dVVVXGhRdeaAwcONBITU01fvSjHxnHjx8P8tOEH4th9MH51gAAAAAAAIh6Ia/xBQAAAAAAAAQCiS8AAAAAAABEJRJfAAAAAAAAiEokvgAAAAAAABCVSHwBAAAAAAAgKpH4AgAAAAAAQFQi8QUAAAAAAICoROILAAAAAAAAUYnEFwAAAAAAQTRv3jzdeuutoQ4D6BNIfAEAAAAAACAqkfgCAAAAACCCtbe3hzqEoOlLzwr/IPEFAAAAAECQdXZ26ic/+YmGDRumkSNH6r777rOfq66u1mWXXabBgwcrMTFR3/ve93T48GH7+fvuu0/Tp0/X7373O2VmZmrAgAGSpD/84Q+aMmWKBg4cqJSUFM2fP1+tra326373u9/p1FNP1YABA3TKKafo8ccft5+rqqqSxWLRiy++qDPPPFMDBgzQ5MmTtWXLFlPcW7Zs0ezZsxUfH69Ro0bpzjvv1IkTJyRJf/nLXzR06FB1dHRIkkpLS2WxWHTnnXfar7/++uv1n//5n/bvt23bprlz52rgwIFKT0/X8uXLTTFnZGToZz/7mRYtWqTExETdeOONvRl29EEkvgAAAAAACLLnnntOgwYN0vvvv68HH3xQ999/v9555x11dnbqsssu05EjR7Rlyxa98847qqio0JVXXmm6vry8XH/84x/1yiuvqLS0VF988YUKCgr0X//1X9q3b582b96syy+/XIZhSJKef/553Xvvvfr5z3+uffv26Re/+IXuuecePffcc6Z+f/zjH+tHP/qRSkpKlJubq0suuUT19fWSpM8++0wXXXSRZs2apX/84x964okntH79ej3wwAOSpLlz5+ro0aMqKSmR9HWSLDU1VZs3b7b3v2XLFs2bN0+SdODAAV1wwQW64oor9OGHH+qll17Stm3bdMstt5hieuihhzRt2jSVlJTonnvu8dvPAH2DxbD9VwAAAAAAAAJu3rx56ujo0Lvvvms/Nnv2bJ177rk677zzdOGFF6qyslLp6emSpI8//linnXaadu7cqVmzZum+++7TL37xC3322WdKS0uTJO3Zs0czZ85UVVWVxo0b1+OeWVlZ+tnPfqaCggL7sQceeEBvvPGGtm/frqqqKmVmZmrt2rW64447JEknTpxQZmamli1bpp/85Ce666679Mc//lH79u2TxWKRJD3++OO644471NTUpJiYGM2cOVMFBQW6/fbb9e1vf1uzZs3S6tWrVV9fr6amJo0ZM0affPKJsrOzdf311ys2Nla//e1v7TFt27ZNZ599tlpbWzVgwABlZGQoJydHr776qv9/EOgTmPEFAAAAAECQTZ061fT9qFGjVFtbq3379ik9Pd2e9JKkSZMmaejQodq3b5/92Lhx4+xJL0maNm2azjvvPE2ZMkXf/e539dRTT6mhoUGS1NraqgMHDmjJkiUaPHiw/euBBx7QgQMHTHHk5uba/9yvXz+dfvrp9vvu27dPubm59qSXJOXl5amlpUWffvqpJOnss8/W5s2bZRiG3n33XV1++eU69dRTtW3bNm3ZskWjR49Wdna2JOkf//iHnn32WVNMCxYsUGdnpyorK+33OP30030bZEBSv1AHAAAAAABAXxMXF2f63mKxqLOz0+PrBw0aZPo+NjZW77zzjrZv3663335bhYWFuuuuu/T+++8rISFBkvTUU09pzpw5Pa7zp3nz5unpp5/WP/7xD8XFxemUU07RvHnztHnzZjU0NOjss8+2t21padH3v/99LV++vEc/Y8eOtf+5+7MC3mDGFwAAAAAAYeLUU0/VoUOHdOjQIfuxjz/+WI2NjZo0aZLLay0Wi/Ly8rR69WqVlJSof//+evXVVzVixAiNHj1aFRUVysrKMn1lZmaa+njvvffsfz5x4oR2796tU0891R7bjh071LViUnFxsYYMGaIxY8ZI+qbO169//Wt7ksuW+Nq8ebO9vpckzZgxQx9//HGPmLKystS/f3/fBhDohhlfAAAAAACEifnz52vKlCm6+uqr9eijj+rEiRP6wQ9+oLPPPtvlkr/3339fGzdu1Pnnn6/hw4fr/fffl9VqtSetVq9ereXLlyspKUkXXHCBjh07pg8++EANDQ1asWKFvZ9169YpOztbp556qn7961+roaFB//Vf/yVJ+sEPfqBHH31Uy5Yt0y233KKysjKtWrVKK1asUEzM1/NqkpOTNXXqVD3//PN67LHHJEn5+fn63ve+p+PHj5tmfN1xxx0644wzdMstt+j666/XoEGD9PHHH+udd96xXwv0FokvAAAAAADChMVi0Z/+9CctW7ZM+fn5iomJ0QUXXKDCwkKX1yUmJmrr1q169NFH1dzcrHHjxunhhx/WhRdeKEm6/vrrlZCQoF/96lf68Y9/rEGDBmnKlCm69dZbTf2sXbtWa9euVWlpqbKysvT6668rNTVVknTSSSfpjTfe0I9//GNNmzZNw4YN05IlS3T33Xeb+jj77LNVWlpqn901bNgwTZo0SYcPH9bEiRPt7aZOnaotW7borrvu0ty5c2UYhiZMmNBjB0ugN9jVEQAAAACAPs62q2NJSYmmT58e6nAAv6HGFwAAAAAAAKISiS8AAAAAAABEJZY6AgAAAAAAICox4wsAAAAAAABRicQXAAAAAAAAohKJLwAAAAAAAEQlEl8AAAAAAACISiS+AAAAAAAAEJVIfAEAAAAAACAqkfgCAAAAAABAVCLxBQAAAAAAgKj0/wPvWdVkGIxBEAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -7862,7 +11821,7 @@
    ],
    "source": [
     "fig, axes = subplots(ncols=3, figsize=(15, 5))\n",
-    "Auto.plot.scatter('horsepower', 'mpg', ax=axes[1]);\n"
+    "Auto.plot.scatter('horsepower', 'mpg', ax=axes[-1]);\n"
    ]
   },
   {
@@ -7886,7 +11845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 113,
    "id": "660482a9",
    "metadata": {
     "execution": {
@@ -7904,7 +11863,7 @@
        "CategoricalDtype(categories=[3, 4, 5, 6, 8], ordered=False, categories_dtype=int64)"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7925,7 +11884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 114,
    "id": "9eb5befc",
    "metadata": {
     "execution": {
@@ -7938,7 +11897,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -7962,7 +11921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 116,
    "id": "f529de12",
    "metadata": {
     "execution": {
@@ -7976,7 +11935,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -7986,8 +11945,8 @@
     }
    ],
    "source": [
-    "fig, ax = subplots(figsize=(8, 8))\n",
-    "Auto.hist('mpg', ax=ax);\n"
+    "fig, axx = subplots(figsize=(8, 8))\n",
+    "Auto.hist('mpg', ax=axx);\n"
    ]
   },
   {
@@ -8000,7 +11959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 117,
    "id": "9a897cee",
    "metadata": {
     "execution": {
@@ -8014,7 +11973,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -8042,7 +12001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 118,
    "id": "5e003b22",
    "metadata": {
     "execution": {
@@ -8056,7 +12015,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 49 Axes>"
       ]
@@ -8080,7 +12039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 120,
    "id": "87f2d391",
    "metadata": {
     "execution": {
@@ -8094,7 +12053,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 9 Axes>"
       ]
@@ -8106,7 +12065,7 @@
    "source": [
     "pd.plotting.scatter_matrix(Auto[['mpg',\n",
     "                                 'displacement',\n",
-    "                                 'weight']]);\n"
+    "                                 'year']]);\n"
    ]
   },
   {
@@ -8119,7 +12078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 121,
    "id": "33cc3b75",
    "metadata": {
     "execution": {
@@ -8133,6 +12092,73 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "mpg",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "weight",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "fe8466e3-65e8-4969-bedb-aeb41c1969c9",
+       "rows": [
+        [
+         "count",
+         "392.0",
+         "392.0"
+        ],
+        [
+         "mean",
+         "23.445918367346938",
+         "2977.5841836734694"
+        ],
+        [
+         "std",
+         "7.8050074865717995",
+         "849.4025600429492"
+        ],
+        [
+         "min",
+         "9.0",
+         "1613.0"
+        ],
+        [
+         "25%",
+         "17.0",
+         "2225.25"
+        ],
+        [
+         "50%",
+         "22.75",
+         "2803.5"
+        ],
+        [
+         "75%",
+         "29.0",
+         "3614.75"
+        ],
+        [
+         "max",
+         "46.6",
+         "5140.0"
+        ]
+       ],
+       "shape": {
+        "columns": 2,
+        "rows": 8
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -8213,7 +12239,7 @@
        "max     46.600000  5140.000000"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8232,7 +12258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 122,
    "id": "398d24d3",
    "metadata": {
     "execution": {
@@ -8258,7 +12284,7 @@
        "Name: mpg, dtype: float64"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8286,6 +12312,11 @@
    "main_language": "python",
    "notebook_metadata_filter": "-all"
   },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -8296,7 +12327,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/Ch03-linreg-lab.ipynb b/Ch03-linreg-lab.ipynb
index 2720fcde..8011ba21 100644
--- a/Ch03-linreg-lab.ipynb
+++ b/Ch03-linreg-lab.ipynb
@@ -185,6 +185,7 @@
        " '__name__',\n",
        " '__package__',\n",
        " '__spec__',\n",
+       " '__vsc_ipynb_file__',\n",
        " '_dh',\n",
        " '_i',\n",
        " '_i1',\n",
@@ -487,7 +488,7 @@
     "matrices (also called design matrices) using the `ModelSpec()`  transform from `ISLP.models`.\n",
     "\n",
     "We  will use the `Boston` housing data set, which is contained in the `ISLP` package.  The `Boston` dataset records  `medv`  (median house value) for $506$ neighborhoods\n",
-    "around Boston.  We will build a regression model to predict  `medv`  using $13$\n",
+    "around Boston.  We will build a regression model to predict  `medv`  using $13$ (should be $12$!)\n",
     "predictors such as  `rmvar`  (average number of rooms per house),\n",
     " `age`  (proportion of owner-occupied units built prior to 1940), and  `lstat`  (percent of\n",
     "households with low socioeconomic status).  We will use `statsmodels` for this\n",
@@ -500,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "d720e2c5",
    "metadata": {
     "execution": {
@@ -519,7 +520,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -529,6 +530,27 @@
     "Boston.columns\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c7636471",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(506, 13)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Boston.shape"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "27b9c8f5",
@@ -2965,6 +2987,11 @@
    "main_language": "python",
    "notebook_metadata_filter": "-all"
   },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -2975,7 +3002,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,
diff --git a/Ch05-resample-lab.ipynb b/Ch05-resample-lab.ipynb
index 9fd3ada3..1e2f36cc 100644
--- a/Ch05-resample-lab.ipynb
+++ b/Ch05-resample-lab.ipynb
@@ -1,1314 +1,1859 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "dc2d635a",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6dde3cef",
-   "metadata": {},
-   "source": [
-    "# Cross-Validation and the Bootstrap\n",
-    "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch05-resample-lab.ipynb\">\n",
-    "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-    "</a>\n",
-    "\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch05-resample-lab.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9fd4324",
-   "metadata": {},
-   "source": [
-    "In this lab, we explore the resampling techniques covered in this\n",
-    "chapter. Some of the commands in this lab may take a while to run on\n",
-    "your computer.\n",
-    "\n",
-    "We again begin by placing most of our imports at this top level."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f1deb5cc",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:13.493284Z",
-     "iopub.status.busy": "2024-06-04T23:19:13.492950Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.143174Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.142882Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import statsmodels.api as sm\n",
-    "from ISLP import load_data\n",
-    "from ISLP.models import (ModelSpec as MS,\n",
-    "                         summarize,\n",
-    "                         poly)\n",
-    "from sklearn.model_selection import train_test_split\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afa08b62",
-   "metadata": {},
-   "source": [
-    "There are several new imports needed for this lab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "268c41b3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.144884Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.144773Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.146541Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.146330Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "from functools import partial\n",
-    "from sklearn.model_selection import \\\n",
-    "     (cross_validate,\n",
-    "      KFold,\n",
-    "      ShuffleSplit)\n",
-    "from sklearn.base import clone\n",
-    "from ISLP.models import sklearn_sm\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c04f8e4",
-   "metadata": {},
-   "source": [
-    "## The Validation Set Approach\n",
-    "We explore the use of the validation set approach in order to estimate\n",
-    "the test error rates that result from fitting various linear models on\n",
-    "the  `Auto`  data set.\n",
-    "\n",
-    "We use the function `train_test_split()` to split\n",
-    "the data into training and validation sets. As there are 392 observations,\n",
-    "we split into two equal sets of size 196 using the\n",
-    "argument `test_size=196`. It is generally a good idea to set a random seed\n",
-    "when performing operations like this that contain an\n",
-    "element of randomness, so that the results obtained can be reproduced\n",
-    "precisely at a later time. We set the random seed of the splitter\n",
-    "with the argument `random_state=0`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "22f44ae0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.147809Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.147730Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.152606Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.152414Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "Auto = load_data('Auto')\n",
-    "Auto_train, Auto_valid = train_test_split(Auto,\n",
-    "                                         test_size=196,\n",
-    "                                         random_state=0)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "318fe69f",
-   "metadata": {},
-   "source": [
-    "Now we can fit a linear regression using only the observations corresponding to the training set `Auto_train`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "0c32e917",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.153847Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.153757Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.157537Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.157339Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "hp_mm = MS(['horsepower'])\n",
-    "X_train = hp_mm.fit_transform(Auto_train)\n",
-    "y_train = Auto_train['mpg']\n",
-    "model = sm.OLS(y_train, X_train)\n",
-    "results = model.fit()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e883b8f",
-   "metadata": {},
-   "source": [
-    "We now use the `predict()` method of `results` evaluated on the model matrix for this model\n",
-    "created using the validation data set. We also calculate the validation MSE of our model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "86ce4f85",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.158717Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.158637Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.162177Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.161910Z"
-    }
-   },
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "dc2d635a",
+      "metadata": {
+        "id": "dc2d635a"
+      },
+      "source": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "23.61661706966988"
+      "cell_type": "markdown",
+      "id": "6dde3cef",
+      "metadata": {
+        "id": "6dde3cef"
+      },
+      "source": [
+        "# Cross-Validation and the Bootstrap\n",
+        "\n",
+        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch05-resample-lab.ipynb\">\n",
+        "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+        "</a>\n",
+        "\n",
+        "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch05-resample-lab.ipynb)"
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_valid = hp_mm.transform(Auto_valid)\n",
-    "y_valid = Auto_valid['mpg']\n",
-    "valid_pred = results.predict(X_valid)\n",
-    "np.mean((y_valid - valid_pred)**2)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2ecdee6",
-   "metadata": {},
-   "source": [
-    "Hence our estimate for the validation MSE of  the linear regression\n",
-    "fit is $23.62$.\n",
-    "\n",
-    "We can also estimate the validation error for\n",
-    "higher-degree polynomial regressions. We first provide a function `evalMSE()` that takes a model string as well\n",
-    "as a training and test set and returns the MSE on the test set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "50a66a97",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.163466Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.163397Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.165323Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.165076Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def evalMSE(terms,\n",
-    "            response,\n",
-    "            train,\n",
-    "            test):\n",
-    "\n",
-    "   mm = MS(terms)\n",
-    "   X_train = mm.fit_transform(train)\n",
-    "   y_train = train[response]\n",
-    "\n",
-    "   X_test = mm.transform(test)\n",
-    "   y_test = test[response]\n",
-    "\n",
-    "   results = sm.OLS(y_train, X_train).fit()\n",
-    "   test_pred = results.predict(X_test)\n",
-    "\n",
-    "   return np.mean((y_test - test_pred)**2)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a255779c",
-   "metadata": {},
-   "source": [
-    "Let’s use this function to estimate the validation MSE\n",
-    "using linear, quadratic and cubic fits. We use the `enumerate()`  function\n",
-    "here, which gives both the values and indices of objects as one iterates\n",
-    "over a for loop."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "d49b6999",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.166563Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.166497Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.177198Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.176975Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([23.61661707, 18.76303135, 18.79694163])"
+      "cell_type": "markdown",
+      "id": "a9fd4324",
+      "metadata": {
+        "id": "a9fd4324"
+      },
+      "source": [
+        "In this lab, we explore the resampling techniques covered in this\n",
+        "chapter. Some of the commands in this lab may take a while to run on\n",
+        "your computer.\n",
+        "\n",
+        "We again begin by placing most of our imports at this top level."
       ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "MSE = np.zeros(3)\n",
-    "for idx, degree in enumerate(range(1, 4)):\n",
-    "    MSE[idx] = evalMSE([poly('horsepower', degree)],\n",
-    "                       'mpg',\n",
-    "                       Auto_train,\n",
-    "                       Auto_valid)\n",
-    "MSE\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d7b8fc1",
-   "metadata": {},
-   "source": [
-    "These error rates are $23.62, 18.76$, and $18.80$, respectively. If we\n",
-    "choose a different training/validation split instead, then we\n",
-    "can expect somewhat different errors on the validation set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "dac8bd54",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.178405Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.178321Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.188650Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.188432Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([20.75540796, 16.94510676, 16.97437833])"
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "9d98bbe0",
+        "outputId": "8e06e4f8-d5e6-4001-b6c2-0287941db6f6"
+      },
+      "source": [
+        "%pip install ISLP"
+      ],
+      "id": "9d98bbe0",
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: ISLP in /usr/local/lib/python3.12/dist-packages (0.4.0)\n",
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+            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=2022.5.0->pytorch-lightning->ISLP) (0.3.2)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=2022.5.0->pytorch-lightning->ISLP) (1.20.1)\n",
+            "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.12/dist-packages (from yarl<2.0,>=1.17.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>=2022.5.0->pytorch-lightning->ISLP) (3.10)\n"
+          ]
+        }
       ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Auto_train, Auto_valid = train_test_split(Auto,\n",
-    "                                          test_size=196,\n",
-    "                                          random_state=3)\n",
-    "MSE = np.zeros(3)\n",
-    "for idx, degree in enumerate(range(1, 4)):\n",
-    "    MSE[idx] = evalMSE([poly('horsepower', degree)],\n",
-    "                       'mpg',\n",
-    "                       Auto_train,\n",
-    "                       Auto_valid)\n",
-    "MSE"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61f2c12d",
-   "metadata": {},
-   "source": [
-    "Using this split of the observations into a training set and a validation set,\n",
-    "we find that the validation set error rates for the models with linear, quadratic, and cubic terms are $20.76$, $16.95$, and $16.97$, respectively.\n",
-    "\n",
-    "These results are consistent with our previous findings: a model that\n",
-    "predicts `mpg` using a quadratic function of `horsepower`\n",
-    "performs better than a model that involves only a linear function of\n",
-    "`horsepower`, and there is no evidence of an improvement in using a cubic function of `horsepower`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f22daa51",
-   "metadata": {},
-   "source": [
-    "## Cross-Validation\n",
-    "In theory, the cross-validation estimate can be computed for any generalized\n",
-    "linear model.  {}\n",
-    "In practice, however, the simplest way to cross-validate in\n",
-    "Python is to use `sklearn`, which has a different interface or API\n",
-    "than `statsmodels`, the code we have been using to fit GLMs.\n",
-    "\n",
-    "This is a problem which often confronts data scientists: \"I have a function to do task $A$, and need to feed it into something that performs task $B$, so that I can compute $B(A(D))$, where $D$ is my data.\" When $A$ and $B$ don’t naturally speak to each other, this\n",
-    "requires the use of a *wrapper*.\n",
-    "In the `ISLP` package,\n",
-    "we provide \n",
-    "a wrapper, `sklearn_sm()`, that enables us to easily use the cross-validation tools of `sklearn` with\n",
-    "models fit by `statsmodels`.\n",
-    "\n",
-    "The class `sklearn_sm()` \n",
-    "has  as its first argument\n",
-    "a model from `statsmodels`. It can take two additional\n",
-    "optional arguments: `model_str` which can be\n",
-    "used to specify a formula, and `model_args` which should\n",
-    "be a dictionary of additional arguments used when fitting\n",
-    "the model. For example, to fit a logistic regression model\n",
-    "we have to specify a `family` argument. This\n",
-    "is passed as `model_args={'family':sm.families.Binomial()}`.\n",
-    "\n",
-    "Here is our wrapper in action:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "601ae443",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.189993Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.189906Z",
-     "iopub.status.idle": "2024-06-04T23:19:14.876368Z",
-     "shell.execute_reply": "2024-06-04T23:19:14.876129Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "24.23151351792924"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "f1deb5cc",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:13.493284Z",
+          "iopub.status.busy": "2024-06-04T23:19:13.492950Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.143174Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.142882Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "f1deb5cc"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import statsmodels.api as sm\n",
+        "from ISLP import load_data\n",
+        "from ISLP.models import (ModelSpec as MS,\n",
+        "                         summarize,\n",
+        "                         poly)\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n"
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hp_model = sklearn_sm(sm.OLS,\n",
-    "                      MS(['horsepower']))\n",
-    "X, Y = Auto.drop(columns=['mpg']), Auto['mpg']\n",
-    "cv_results = cross_validate(hp_model,\n",
-    "                            X,\n",
-    "                            Y,\n",
-    "                            cv=Auto.shape[0])\n",
-    "cv_err = np.mean(cv_results['test_score'])\n",
-    "cv_err\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebadc35f",
-   "metadata": {},
-   "source": [
-    "The arguments to `cross_validate()` are as follows: an\n",
-    "object with the appropriate `fit()`, `predict()`,\n",
-    "and `score()` methods,  an\n",
-    "array of features `X` and a response `Y`. \n",
-    "We also included an additional argument `cv` to `cross_validate()`; specifying an integer\n",
-    "$K$ results in $K$-fold cross-validation. We have provided a value \n",
-    "corresponding to the total number of observations, which results in\n",
-    "leave-one-out cross-validation (LOOCV). The `cross_validate()`  function produces a dictionary with several components;\n",
-    "we simply want the cross-validated test score here (MSE), which is estimated to be 24.23."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "25f47b99",
-   "metadata": {},
-   "source": [
-    "We can repeat this procedure for increasingly complex polynomial fits.\n",
-    "To automate the process, we again\n",
-    "use a for loop which iteratively fits polynomial\n",
-    "regressions of degree 1 to 5, computes the\n",
-    "associated cross-validation error, and stores it in the $i$th element\n",
-    "of the vector `cv_error`. The variable `d` in the for loop\n",
-    "corresponds to the degree of the polynomial. We begin by initializing the\n",
-    "vector. This command may take a couple of seconds to run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "11226c85",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:14.877800Z",
-     "iopub.status.busy": "2024-06-04T23:19:14.877726Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.384419Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.384193Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([24.23151352, 19.24821312, 19.33498406, 19.42443033, 19.03323827])"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "afa08b62",
+      "metadata": {
+        "id": "afa08b62"
+      },
+      "source": [
+        "There are several new imports needed for this lab."
       ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cv_error = np.zeros(5)\n",
-    "H = np.array(Auto['horsepower'])\n",
-    "M = sklearn_sm(sm.OLS)\n",
-    "for i, d in enumerate(range(1,6)):\n",
-    "    X = np.power.outer(H, np.arange(d+1))\n",
-    "    M_CV = cross_validate(M,\n",
-    "                          X,\n",
-    "                          Y,\n",
-    "                          cv=Auto.shape[0])\n",
-    "    cv_error[i] = np.mean(M_CV['test_score'])\n",
-    "cv_error\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3a920ae",
-   "metadata": {},
-   "source": [
-    "As in Figure~\\ref{Ch5:cvplot}, we see a sharp drop in the estimated test MSE between the linear and\n",
-    "quadratic fits, but then no clear improvement from using higher-degree polynomials.\n",
-    "\n",
-    "Above we introduced the `outer()`  method of the `np.power()`\n",
-    "function.  The `outer()` method is applied to an operation\n",
-    "that has two arguments, such as `add()`, `min()`, or\n",
-    "`power()`.\n",
-    "It has two arrays as\n",
-    "arguments, and then forms a larger\n",
-    "array where the operation is applied to each pair of elements of the\n",
-    "two arrays. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "64b64d97",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.385768Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.385690Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.387686Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.387484Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 5,  7],\n",
-       "       [ 7,  9],\n",
-       "       [11, 13]])"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "268c41b3",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.144884Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.144773Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.146541Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.146330Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "268c41b3"
+      },
+      "outputs": [],
+      "source": [
+        "from functools import partial\n",
+        "from sklearn.model_selection import \\\n",
+        "     (cross_validate,\n",
+        "      KFold,\n",
+        "      ShuffleSplit)\n",
+        "from sklearn.base import clone\n",
+        "from ISLP.models import sklearn_sm\n"
       ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A = np.array([3, 5, 9])\n",
-    "B = np.array([2, 4])\n",
-    "np.add.outer(A, B)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71385c1b",
-   "metadata": {},
-   "source": [
-    "In the CV example above, we used $K=n$, but of course we can also use $K<n$. The code is very similar\n",
-    "to the above (and is significantly faster). Here we use `KFold()` to partition the data into $K=10$ random groups. We use `random_state` to set a random seed and initialize a vector `cv_error` in which we will store the CV errors corresponding to the\n",
-    "polynomial fits of degrees one to five."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "ca0f972f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.389014Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.388934Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.407438Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.407194Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([24.20766449, 19.18533142, 19.27626666, 19.47848403, 19.13720581])"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1c04f8e4",
+      "metadata": {
+        "id": "1c04f8e4"
+      },
+      "source": [
+        "## The Validation Set Approach\n",
+        "We explore the use of the validation set approach in order to estimate\n",
+        "the test error rates that result from fitting various linear models on\n",
+        "the  `Auto`  data set.\n",
+        "\n",
+        "We use the function `train_test_split()` to split\n",
+        "the data into training and validation sets. As there are 392 observations,\n",
+        "we split into two equal sets of size 196 using the\n",
+        "argument `test_size=196`. It is generally a good idea to set a random seed\n",
+        "when performing operations like this that contain an\n",
+        "element of randomness, so that the results obtained can be reproduced\n",
+        "precisely at a later time. We set the random seed of the splitter\n",
+        "with the argument `random_state=0`."
       ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cv_error = np.zeros(5)\n",
-    "cv = KFold(n_splits=10,\n",
-    "           shuffle=True,\n",
-    "           random_state=0) # use same splits for each degree\n",
-    "for i, d in enumerate(range(1,6)):\n",
-    "    X = np.power.outer(H, np.arange(d+1))\n",
-    "    M_CV = cross_validate(M,\n",
-    "                          X,\n",
-    "                          Y,\n",
-    "                          cv=cv)\n",
-    "    cv_error[i] = np.mean(M_CV['test_score'])\n",
-    "cv_error\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b234093",
-   "metadata": {},
-   "source": [
-    "Notice that the computation time is much shorter than that of LOOCV.\n",
-    "(In principle, the computation time for LOOCV for a least squares\n",
-    "linear model should be faster than for $K$-fold CV, due to the\n",
-    "availability of the formula~(\\ref{Ch5:eq:LOOCVform})  for LOOCV;\n",
-    "however, the generic `cross_validate()`  function does not make\n",
-    "use of this formula.)  We still see little evidence that using cubic\n",
-    "or higher-degree polynomial terms leads to a lower test error than simply\n",
-    "using a quadratic fit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb4487a4",
-   "metadata": {},
-   "source": [
-    "The `cross_validate()` function is flexible and can take\n",
-    "different splitting mechanisms as an argument. For instance, one can use the `ShuffleSplit()` funtion to implement\n",
-    "the validation set approach just as easily as K-fold cross-validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "080cdb29",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.408750Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.408677Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.413979Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.413762Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([23.61661707])"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "22f44ae0",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.147809Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.147730Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.152606Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.152414Z"
+        },
+        "id": "22f44ae0"
+      },
+      "outputs": [],
+      "source": [
+        "Auto = load_data('Auto')\n",
+        "Auto_train, Auto_valid = train_test_split(Auto,\n",
+        "                                         test_size=196,\n",
+        "                                         random_state=0)\n"
       ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation = ShuffleSplit(n_splits=1,\n",
-    "                          test_size=196,\n",
-    "                          random_state=0)\n",
-    "results = cross_validate(hp_model,\n",
-    "                         Auto.drop(['mpg'], axis=1),\n",
-    "                         Auto['mpg'],\n",
-    "                         cv=validation);\n",
-    "results['test_score']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b2f4b4cf",
-   "metadata": {},
-   "source": [
-    "One can estimate the variability in the test error by running the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "7c46de2b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.415225Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.415158Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.437526Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.437302Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(23.802232661034168, 1.421845094109185)"
+      "cell_type": "markdown",
+      "id": "318fe69f",
+      "metadata": {
+        "id": "318fe69f"
+      },
+      "source": [
+        "Now we can fit a linear regression using only the observations corresponding to the training set `Auto_train`."
       ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation = ShuffleSplit(n_splits=10,\n",
-    "                          test_size=196,\n",
-    "                          random_state=0)\n",
-    "results = cross_validate(hp_model,\n",
-    "                         Auto.drop(['mpg'], axis=1),\n",
-    "                         Auto['mpg'],\n",
-    "                         cv=validation)\n",
-    "results['test_score'].mean(), results['test_score'].std()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07165f0e",
-   "metadata": {},
-   "source": [
-    "Note that this standard deviation is not a valid estimate of the\n",
-    "sampling variability of the mean test score or the individual scores, since the randomly-selected training\n",
-    "samples overlap and hence introduce correlations. But it does give an\n",
-    "idea of the Monte Carlo variation\n",
-    "incurred by picking different random folds.\n",
-    "\n",
-    "## The Bootstrap\n",
-    "We illustrate the use of the bootstrap in the simple example\n",
-    " {of Section~\\ref{Ch5:sec:bootstrap},}  as well as on an example involving\n",
-    "estimating the accuracy of the linear regression model on the  `Auto`\n",
-    "data set.\n",
-    "### Estimating the Accuracy of a Statistic of Interest\n",
-    "One of the great advantages of the bootstrap approach is that it can\n",
-    "be applied in almost all situations. No complicated mathematical\n",
-    "calculations are required. While there are several implementations\n",
-    "of the bootstrap in Python, its use for estimating\n",
-    "standard error is simple enough that we write our own function\n",
-    "below for the case when our data is stored\n",
-    "in a dataframe.\n",
-    "\n",
-    "To illustrate the bootstrap, we\n",
-    "start with a simple example.\n",
-    "The  `Portfolio`  data set in the `ISLP` package is described\n",
-    "in Section~\\ref{Ch5:sec:bootstrap}. The goal is to estimate the\n",
-    "sampling variance of the parameter $\\alpha$ given in formula~(\\ref{Ch5:min.var}).  We will\n",
-    "create a function\n",
-    "`alpha_func()`, which takes as input a dataframe `D` assumed\n",
-    "to have columns `X` and `Y`, as well as a\n",
-    "vector `idx` indicating which observations should be used to\n",
-    "estimate \n",
-    "$\\alpha$. The function then outputs the estimate for $\\alpha$ based on\n",
-    "the selected observations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "a4b6d9b3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.438786Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.438714Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.441484Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.441268Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "Portfolio = load_data('Portfolio')\n",
-    "def alpha_func(D, idx):\n",
-    "   cov_ = np.cov(D[['X','Y']].loc[idx], rowvar=False)\n",
-    "   return ((cov_[1,1] - cov_[0,1]) /\n",
-    "           (cov_[0,0]+cov_[1,1]-2*cov_[0,1]))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d50058e",
-   "metadata": {},
-   "source": [
-    "This function returns an estimate for $\\alpha$\n",
-    "based on applying the minimum\n",
-    "    variance formula (\\ref{Ch5:min.var}) to the observations indexed by\n",
-    "the argument `idx`.  For instance, the following command\n",
-    "estimates $\\alpha$ using all 100 observations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "81498a11",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.442843Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.442765Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.445171Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.444944Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "0.57583207459283"
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "0c32e917",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.153847Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.153757Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.157537Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.157339Z"
+        },
+        "id": "0c32e917"
+      },
+      "outputs": [],
+      "source": [
+        "hp_mm = MS(['horsepower'])\n",
+        "X_train = hp_mm.fit_transform(Auto_train)\n",
+        "y_train = Auto_train['mpg']\n",
+        "model = sm.OLS(y_train, X_train)\n",
+        "results = model.fit()\n"
       ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alpha_func(Portfolio, range(100))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f5d0aab",
-   "metadata": {},
-   "source": [
-    "Next we randomly select\n",
-    "100 observations from `range(100)`, with replacement. This is equivalent\n",
-    "to constructing a new bootstrap data set and recomputing $\\hat{\\alpha}$\n",
-    "based on the new data set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "64fe1cb6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.446422Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.446354Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.448793Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.448579Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6074452469619004"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7e883b8f",
+      "metadata": {
+        "id": "7e883b8f"
+      },
+      "source": [
+        "We now use the `predict()` method of `results` evaluated on the model matrix for this model\n",
+        "created using the validation data set. We also calculate the validation MSE of our model."
       ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rng = np.random.default_rng(0)\n",
-    "alpha_func(Portfolio,\n",
-    "           rng.choice(100,\n",
-    "                      100,\n",
-    "                      replace=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91a635fe",
-   "metadata": {},
-   "source": [
-    "This process can be generalized to create a simple function `boot_SE()` for\n",
-    "computing the bootstrap standard error for arbitrary\n",
-    "functions that take only a data frame as an argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "dd16bbae",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.450062Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.449992Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.451958Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.451742Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "def boot_SE(func,\n",
-    "            D,\n",
-    "            n=None,\n",
-    "            B=1000,\n",
-    "            seed=0):\n",
-    "    rng = np.random.default_rng(seed)\n",
-    "    first_, second_ = 0, 0\n",
-    "    n = n or D.shape[0]\n",
-    "    for _ in range(B):\n",
-    "        idx = rng.choice(D.index,\n",
-    "                         n,\n",
-    "                         replace=True)\n",
-    "        value = func(D, idx)\n",
-    "        first_ += value\n",
-    "        second_ += value**2\n",
-    "    return np.sqrt(second_ / B - (first_ / B)**2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac4e17ed",
-   "metadata": {},
-   "source": [
-    "Notice the use of `_` as a loop variable in `for _ in range(B)`. This is often used if the value of the counter is\n",
-    "unimportant and simply makes sure  the loop is executed `B` times.\n",
-    "\n",
-    "Let’s use our function to evaluate the accuracy of our\n",
-    "estimate of $\\alpha$ using $B=1{,}000$ bootstrap replications. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "b42b4585",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.453190Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.453118Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.631597Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.631370Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "0.09118176521277699"
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "86ce4f85",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.158717Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.158637Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.162177Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.161910Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "86ce4f85",
+        "outputId": "a3836524-6514-4704-c570-1edf94e8e6fb"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(23.61661706966988)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ],
+      "source": [
+        "X_valid = hp_mm.transform(Auto_valid)\n",
+        "y_valid = Auto_valid['mpg']\n",
+        "valid_pred = results.predict(X_valid)\n",
+        "np.mean((y_valid - valid_pred)**2)\n"
       ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "alpha_SE = boot_SE(alpha_func,\n",
-    "                   Portfolio,\n",
-    "                   B=1000,\n",
-    "                   seed=0)\n",
-    "alpha_SE\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6c5464d7",
-   "metadata": {},
-   "source": [
-    "The final output shows that the bootstrap estimate for ${\\rm SE}(\\hat{\\alpha})$ is $0.0912$.\n",
-    "\n",
-    "### Estimating the Accuracy of a Linear Regression Model\n",
-    "The bootstrap approach can be used to assess the variability of the\n",
-    "coefficient estimates and predictions from a statistical learning\n",
-    "method. Here we use the bootstrap approach in order to assess the\n",
-    "variability of the estimates for $\\beta_0$ and $\\beta_1$, the\n",
-    "intercept and slope terms for the linear regression model that uses\n",
-    "`horsepower` to predict `mpg` in the  `Auto`  data set. We\n",
-    "will compare the estimates obtained using the bootstrap to those\n",
-    "obtained using the formulas for ${\\rm SE}(\\hat{\\beta}_0)$ and\n",
-    "${\\rm SE}(\\hat{\\beta}_1)$ described in Section~\\ref{Ch3:secoefsec}.\n",
-    "\n",
-    "To use our `boot_SE()` function, we must write a function (its\n",
-    "first argument)\n",
-    "that takes a data frame `D` and indices `idx`\n",
-    "as its only arguments. But here we want to bootstrap a specific\n",
-    "regression model, specified by a model formula and data. We show how\n",
-    "to do this in a few simple steps.\n",
-    "\n",
-    "We start by writing a generic\n",
-    "function `boot_OLS()` for bootstrapping a regression model that takes a formula to\n",
-    "define the corresponding regression. We use the `clone()` function to\n",
-    "make a copy of the formula that can be refit to the new dataframe. This means\n",
-    "that any derived features such as those defined by `poly()`\n",
-    "(which we will see shortly),\n",
-    "will be re-fit on the resampled data frame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "6bc11784",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.632802Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.632725Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.634450Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.634222Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "def boot_OLS(model_matrix, response, D, idx):\n",
-    "    D_ = D.loc[idx]\n",
-    "    Y_ = D_[response]\n",
-    "    X_ = clone(model_matrix).fit_transform(D_)\n",
-    "    return sm.OLS(Y_, X_).fit().params"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a6ea3ce",
-   "metadata": {},
-   "source": [
-    "This is not quite what is needed as the first argument to\n",
-    "`boot_SE()`. The first two arguments which specify the model will not change in the\n",
-    "bootstrap process, and we would like to *freeze* them.   The\n",
-    "function `partial()` from the `functools` module  does precisely this: it takes a function\n",
-    "as an argument, and freezes some of its arguments, starting from the\n",
-    "left. We use it to freeze the first two model-formula arguments of `boot_OLS()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "740cd50c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.635644Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.635575Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.637097Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.636867Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "hp_func = partial(boot_OLS, MS(['horsepower']), 'mpg')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed6d19e2",
-   "metadata": {},
-   "source": [
-    "Typing `hp_func?` will show that it has two arguments `D`\n",
-    "and `idx` --- it is a version of `boot_OLS()` with the first\n",
-    "two arguments frozen --- and hence is ideal as the first argument for `boot_SE()`.\n",
-    "\n",
-    "The `hp_func()` function can now be used in order to create\n",
-    "bootstrap estimates for the intercept and slope terms by randomly\n",
-    "sampling from among the observations with replacement. We first\n",
-    "demonstrate its utility on 10 bootstrap samples."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "ffb3ec50",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.638287Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.638220Z",
-     "iopub.status.idle": "2024-06-04T23:19:15.656475Z",
-     "shell.execute_reply": "2024-06-04T23:19:15.656261Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[39.12226577, -0.1555926 ],\n",
-       "       [37.18648613, -0.13915813],\n",
-       "       [37.46989244, -0.14112749],\n",
-       "       [38.56723252, -0.14830116],\n",
-       "       [38.95495707, -0.15315141],\n",
-       "       [39.12563927, -0.15261044],\n",
-       "       [38.45763251, -0.14767251],\n",
-       "       [38.43372587, -0.15019447],\n",
-       "       [37.87581142, -0.1409544 ],\n",
-       "       [37.95949036, -0.1451333 ]])"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f2ecdee6",
+      "metadata": {
+        "id": "f2ecdee6"
+      },
+      "source": [
+        "Hence our estimate for the validation MSE of  the linear regression\n",
+        "fit is $23.62$.\n",
+        "\n",
+        "We can also estimate the validation error for\n",
+        "higher-degree polynomial regressions. We first provide a function `evalMSE()` that takes a model string as well\n",
+        "as a training and test set and returns the MSE on the test set."
       ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rng = np.random.default_rng(0)\n",
-    "np.array([hp_func(Auto,\n",
-    "          rng.choice(Auto.index,\n",
-    "                     392,\n",
-    "                     replace=True)) for _ in range(10)])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6d09d96",
-   "metadata": {},
-   "source": [
-    "Next, we use the `boot_SE()` {}  function to compute the standard\n",
-    "errors of 1,000 bootstrap estimates for the intercept and slope terms."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "7d561f70",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:15.657733Z",
-     "iopub.status.busy": "2024-06-04T23:19:15.657659Z",
-     "iopub.status.idle": "2024-06-04T23:19:17.204871Z",
-     "shell.execute_reply": "2024-06-04T23:19:17.204614Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "intercept     0.731176\n",
-       "horsepower    0.006092\n",
-       "dtype: float64"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "50a66a97",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.163466Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.163397Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.165323Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.165076Z"
+        },
+        "id": "50a66a97"
+      },
+      "outputs": [],
+      "source": [
+        "def evalMSE(terms,\n",
+        "            response,\n",
+        "            train,\n",
+        "            test):\n",
+        "\n",
+        "   mm = MS(terms)\n",
+        "   X_train = mm.fit_transform(train)\n",
+        "   y_train = train[response]\n",
+        "\n",
+        "   X_test = mm.transform(test)\n",
+        "   y_test = test[response]\n",
+        "\n",
+        "   results = sm.OLS(y_train, X_train).fit()\n",
+        "   test_pred = results.predict(X_test)\n",
+        "\n",
+        "   return np.mean((y_test - test_pred)**2)\n"
       ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hp_se = boot_SE(hp_func,\n",
-    "                Auto,\n",
-    "                B=1000,\n",
-    "                seed=10)\n",
-    "hp_se\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a834f240",
-   "metadata": {},
-   "source": [
-    "This indicates that the bootstrap estimate for ${\\rm SE}(\\hat{\\beta}_0)$ is\n",
-    "0.85, and that the bootstrap\n",
-    "estimate for ${\\rm SE}(\\hat{\\beta}_1)$ is\n",
-    "0.0074.  As discussed in\n",
-    "Section~\\ref{Ch3:secoefsec}, standard formulas can be used to compute\n",
-    "the standard errors for the regression coefficients in a linear\n",
-    "model. These can be obtained using the `summarize()`  function\n",
-    "from `ISLP.sm`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "3888aa0a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:17.206302Z",
-     "iopub.status.busy": "2024-06-04T23:19:17.206223Z",
-     "iopub.status.idle": "2024-06-04T23:19:17.221631Z",
-     "shell.execute_reply": "2024-06-04T23:19:17.221444Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "intercept     0.717\n",
-       "horsepower    0.006\n",
-       "Name: std err, dtype: float64"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a255779c",
+      "metadata": {
+        "id": "a255779c"
+      },
+      "source": [
+        "Let’s use this function to estimate the validation MSE\n",
+        "using linear, quadratic and cubic fits. We use the `enumerate()`  function\n",
+        "here, which gives both the values and indices of objects as one iterates\n",
+        "over a for loop."
       ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hp_model.fit(Auto, Auto['mpg'])\n",
-    "model_se = summarize(hp_model.results_)['std err']\n",
-    "model_se\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aefc0575",
-   "metadata": {},
-   "source": [
-    "The standard error estimates for $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$\n",
-    "obtained using the formulas  from Section~\\ref{Ch3:secoefsec}  are\n",
-    "0.717 for the\n",
-    "intercept and\n",
-    "0.006 for the\n",
-    "slope. Interestingly, these are somewhat different from the estimates\n",
-    "obtained using the bootstrap.  Does this indicate a problem with the\n",
-    "bootstrap? In fact, it suggests the opposite.  Recall that the\n",
-    "standard formulas given in\n",
-    " {Equation~\\ref{Ch3:se.eqn} on page~\\pageref{Ch3:se.eqn}}\n",
-    "rely on certain assumptions. For example,\n",
-    "they depend on the unknown parameter $\\sigma^2$, the noise\n",
-    "variance. We then estimate $\\sigma^2$ using the RSS. Now although the\n",
-    "formula for the standard errors do not rely on the linear model being\n",
-    "correct, the estimate for $\\sigma^2$ does.  We see\n",
-    " {in Figure~\\ref{Ch3:polyplot} on page~\\pageref{Ch3:polyplot}}  that there is\n",
-    "a non-linear relationship in the data, and so the residuals from a\n",
-    "linear fit will be inflated, and so will $\\hat{\\sigma}^2$.  Secondly,\n",
-    "the standard formulas assume (somewhat unrealistically) that the $x_i$\n",
-    "are fixed, and all the variability comes from the variation in the\n",
-    "errors $\\epsilon_i$.  The bootstrap approach does not rely on any of\n",
-    "these assumptions, and so it is likely giving a more accurate estimate\n",
-    "of the standard errors of $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$ than\n",
-    "the results from `sm.OLS`.\n",
-    "\n",
-    "Below we compute the bootstrap standard error estimates and the\n",
-    "standard linear regression estimates that result from fitting the\n",
-    "quadratic model to the data. Since this model provides a good fit to\n",
-    "the data (Figure~\\ref{Ch3:polyplot}), there is now a better\n",
-    "correspondence between the bootstrap estimates and the standard\n",
-    "estimates of ${\\rm SE}(\\hat{\\beta}_0)$, ${\\rm SE}(\\hat{\\beta}_1)$ and\n",
-    "${\\rm SE}(\\hat{\\beta}_2)$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "acc3e32c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:17.222887Z",
-     "iopub.status.busy": "2024-06-04T23:19:17.222785Z",
-     "iopub.status.idle": "2024-06-04T23:19:19.351574Z",
-     "shell.execute_reply": "2024-06-04T23:19:19.351317Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "intercept                                  1.538641\n",
-       "poly(horsepower, degree=2, raw=True)[0]    0.024696\n",
-       "poly(horsepower, degree=2, raw=True)[1]    0.000090\n",
-       "dtype: float64"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "id": "d49b6999",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.166563Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.166497Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.177198Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.176975Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "d49b6999",
+        "outputId": "2ca4750b-f5ba-46e6-f8f5-ec36dde48552"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([23.61661707, 18.76303135, 18.79694163])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ],
+      "source": [
+        "MSE = np.zeros(3)\n",
+        "for idx, degree in enumerate(range(1, 4)):\n",
+        "    MSE[idx] = evalMSE([poly('horsepower', degree)],\n",
+        "                       'mpg',\n",
+        "                       Auto_train,\n",
+        "                       Auto_valid)\n",
+        "MSE\n"
       ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "quad_model = MS([poly('horsepower', 2, raw=True)])\n",
-    "quad_func = partial(boot_OLS,\n",
-    "                    quad_model,\n",
-    "                    'mpg')\n",
-    "boot_SE(quad_func, Auto, B=1000)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e8a2fd2b",
-   "metadata": {},
-   "source": [
-    "We  compare the results to the standard errors computed using `sm.OLS()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "dca5340c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:19.352904Z",
-     "iopub.status.busy": "2024-06-04T23:19:19.352827Z",
-     "iopub.status.idle": "2024-06-04T23:19:19.360147Z",
-     "shell.execute_reply": "2024-06-04T23:19:19.359948Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "intercept                                  1.800\n",
-       "poly(horsepower, degree=2, raw=True)[0]    0.031\n",
-       "poly(horsepower, degree=2, raw=True)[1]    0.000\n",
-       "Name: std err, dtype: float64"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9d7b8fc1",
+      "metadata": {
+        "id": "9d7b8fc1"
+      },
+      "source": [
+        "These error rates are $23.62, 18.76$, and $18.80$, respectively. If we\n",
+        "choose a different training/validation split instead, then we\n",
+        "can expect somewhat different errors on the validation set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "dac8bd54",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.178405Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.178321Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.188650Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.188432Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dac8bd54",
+        "outputId": "ed13c196-a490-4e99-dd84-52295eca6e9e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([20.75540796, 16.94510676, 16.97437833])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
+      "source": [
+        "Auto_train, Auto_valid = train_test_split(Auto,\n",
+        "                                          test_size=196,\n",
+        "                                          random_state=3)\n",
+        "MSE = np.zeros(3)\n",
+        "for idx, degree in enumerate(range(1, 4)):\n",
+        "    MSE[idx] = evalMSE([poly('horsepower', degree)],\n",
+        "                       'mpg',\n",
+        "                       Auto_train,\n",
+        "                       Auto_valid)\n",
+        "MSE"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "61f2c12d",
+      "metadata": {
+        "id": "61f2c12d"
+      },
+      "source": [
+        "Using this split of the observations into a training set and a validation set,\n",
+        "we find that the validation set error rates for the models with linear, quadratic, and cubic terms are $20.76$, $16.95$, and $16.97$, respectively.\n",
+        "\n",
+        "These results are consistent with our previous findings: a model that\n",
+        "predicts `mpg` using a quadratic function of `horsepower`\n",
+        "performs better than a model that involves only a linear function of\n",
+        "`horsepower`, and there is no evidence of an improvement in using a cubic function of `horsepower`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f22daa51",
+      "metadata": {
+        "id": "f22daa51"
+      },
+      "source": [
+        "## Cross-Validation\n",
+        "In theory, the cross-validation estimate can be computed for any generalized\n",
+        "linear model.  {}\n",
+        "In practice, however, the simplest way to cross-validate in\n",
+        "Python is to use `sklearn`, which has a different interface or API\n",
+        "than `statsmodels`, the code we have been using to fit GLMs.\n",
+        "\n",
+        "This is a problem which often confronts data scientists: \"I have a function to do task $A$, and need to feed it into something that performs task $B$, so that I can compute $B(A(D))$, where $D$ is my data.\" When $A$ and $B$ don’t naturally speak to each other, this\n",
+        "requires the use of a *wrapper*.\n",
+        "In the `ISLP` package,\n",
+        "we provide\n",
+        "a wrapper, `sklearn_sm()`, that enables us to easily use the cross-validation tools of `sklearn` with\n",
+        "models fit by `statsmodels`.\n",
+        "\n",
+        "The class `sklearn_sm()`\n",
+        "has  as its first argument\n",
+        "a model from `statsmodels`. It can take two additional\n",
+        "optional arguments: `model_str` which can be\n",
+        "used to specify a formula, and `model_args` which should\n",
+        "be a dictionary of additional arguments used when fitting\n",
+        "the model. For example, to fit a logistic regression model\n",
+        "we have to specify a `family` argument. This\n",
+        "is passed as `model_args={'family':sm.families.Binomial()}`.\n",
+        "\n",
+        "Here is our wrapper in action:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "id": "601ae443",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.189993Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.189906Z",
+          "iopub.status.idle": "2024-06-04T23:19:14.876368Z",
+          "shell.execute_reply": "2024-06-04T23:19:14.876129Z"
+        },
+        "lines_to_next_cell": 0,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "601ae443",
+        "outputId": "970d021e-04c2-454d-9672-6f1d554c5395"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(24.23151351792922)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
+      "source": [
+        "hp_model = sklearn_sm(sm.OLS,\n",
+        "                      MS(['horsepower']))\n",
+        "X, Y = Auto.drop(columns=['mpg']), Auto['mpg']\n",
+        "cv_results = cross_validate(hp_model,\n",
+        "                            X,\n",
+        "                            Y,\n",
+        "                            cv=Auto.shape[0])\n",
+        "cv_err = np.mean(cv_results['test_score'])\n",
+        "cv_err\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(cv_results.keys())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pOpPEckGFmw7",
+        "outputId": "b6fc592b-ef26-47c0-ee26-bb092f69e9fa"
+      },
+      "id": "pOpPEckGFmw7",
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "dict_keys(['fit_time', 'score_time', 'test_score'])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ebadc35f",
+      "metadata": {
+        "id": "ebadc35f"
+      },
+      "source": [
+        "The arguments to `cross_validate()` are as follows: an\n",
+        "object with the appropriate `fit()`, `predict()`,\n",
+        "and `score()` methods,  an\n",
+        "array of features `X` and a response `Y`.\n",
+        "We also included an additional argument `cv` to `cross_validate()`; specifying an integer\n",
+        "$K$ results in $K$-fold cross-validation. We have provided a value\n",
+        "corresponding to the total number of observations, which results in\n",
+        "leave-one-out cross-validation (LOOCV). The `cross_validate()`  function produces a dictionary with several components;\n",
+        "we simply want the cross-validated test score here (MSE), which is estimated to be 24.23."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "25f47b99",
+      "metadata": {
+        "id": "25f47b99"
+      },
+      "source": [
+        "We can repeat this procedure for increasingly complex polynomial fits.\n",
+        "To automate the process, we again\n",
+        "use a for loop which iteratively fits polynomial\n",
+        "regressions of degree 1 to 5, computes the\n",
+        "associated cross-validation error, and stores it in the $i$th element\n",
+        "of the vector `cv_error`. The variable `d` in the for loop\n",
+        "corresponds to the degree of the polynomial. We begin by initializing the\n",
+        "vector. This command may take a couple of seconds to run."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "11226c85",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:14.877800Z",
+          "iopub.status.busy": "2024-06-04T23:19:14.877726Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.384419Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.384193Z"
+        },
+        "lines_to_next_cell": 0,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "11226c85",
+        "outputId": "524948b7-9546-4dca-d81c-1abae32d36bc"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "max_degree = 5\n",
+        "cv_error = np.zeros(max_degree)\n",
+        "H = np.array(Auto['horsepower'])\n",
+        "M = sklearn_sm(sm.OLS)\n",
+        "for i, d in enumerate(range(1,max_degree+1)):\n",
+        "    X = np.power.outer(H, np.arange(d+1))# creates a polynomial input with\n",
+        "    #powers of horsepower extending to d incl constant 1 too!\n",
+        "    M_CV = cross_validate(M,\n",
+        "                          X,\n",
+        "                          Y,\n",
+        "                          cv=Auto.shape[0])\n",
+        "    cv_error[i] = np.mean(M_CV['test_score'])\n",
+        "cv_error\n",
+        "from matplotlib import pyplot as plt\n",
+        "plt.plot(range(1, max_degree + 1), cv_error, 'o-')\n",
+        "plt.xlabel('degree'); plt.ylabel('CV_error')\n",
+        "plt.xticks(range(1, max_degree + 1))\n",
+        "plt.grid()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cv_error"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IWj_rOzIK_4i",
+        "outputId": "7a2d696b-4705-451f-afb7-3e0ce96ddab4"
+      },
+      "id": "IWj_rOzIK_4i",
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([24.23151352, 19.24821312, 19.33498406, 19.42443029, 19.03320648])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a3a920ae",
+      "metadata": {
+        "id": "a3a920ae"
+      },
+      "source": [
+        "As in Figure~\\ref{Ch5:cvplot}, we see a sharp drop in the estimated test MSE between the linear and\n",
+        "quadratic fits, but then no clear improvement from using higher-degree polynomials.\n",
+        "\n",
+        "Above we introduced the `outer()`  method of the `np.power()`\n",
+        "function.  The `outer()` method is applied to an operation\n",
+        "that has two arguments, such as `add()`, `min()`, or\n",
+        "`power()`.\n",
+        "It has two arrays as\n",
+        "arguments, and then forms a larger\n",
+        "array where the operation is applied to each pair of elements of the\n",
+        "two arrays."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "id": "64b64d97",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.385768Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.385690Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.387686Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.387484Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "64b64d97",
+        "outputId": "bc99d234-b96a-405e-908e-395d228e6be6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(392, 6)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ],
+      "source": [
+        "A = np.array([3, 5, 9])\n",
+        "B = np.array([2, 4])\n",
+        "np.power.outer(A, B)\n",
+        "np.zeros(H.shape)\n",
+        "X.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "71385c1b",
+      "metadata": {
+        "id": "71385c1b"
+      },
+      "source": [
+        "In the CV example above, we used $K=n$, but of course we can also use $K<n$. The code is very similar\n",
+        "to the above (and is significantly faster). Here we use `KFold()` to partition the data into $K=10$ random groups. We use `random_state` to set a random seed and initialize a vector `cv_error` in which we will store the CV errors corresponding to the\n",
+        "polynomial fits of degrees one to five."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "id": "ca0f972f",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.389014Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.388934Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.407438Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.407194Z"
+        },
+        "lines_to_next_cell": 0,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "ca0f972f",
+        "outputId": "21148ee0-c8b5-4510-9a16-ac279fb7803c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "cv = KFold(n_splits=10,\n",
+        "           shuffle=True,\n",
+        "           random_state=0) # use same splits for each degree\n",
+        "cv_error = np.zeros(max_degree)\n",
+        "H = np.array(Auto['horsepower'])\n",
+        "M = sklearn_sm(sm.OLS)\n",
+        "for i, d in enumerate(range(1,max_degree+1)):\n",
+        "    X = np.power.outer(H, np.arange(d+1))# creates a polynomial input with\n",
+        "    #powers of horsepower extending to d incl constant 1 too!\n",
+        "    M_CV = cross_validate(M,\n",
+        "                          X,\n",
+        "                          Y,\n",
+        "                          cv=Auto.shape[0])\n",
+        "    cv_error[i] = np.mean(M_CV['test_score'])\n",
+        "cv_error\n",
+        "from matplotlib import pyplot as plt\n",
+        "plt.plot(range(1, max_degree + 1), cv_error, 'o-')\n",
+        "plt.xlabel('degree'); plt.ylabel('CV_error')\n",
+        "plt.xticks(range(1, max_degree + 1))\n",
+        "plt.grid()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8b234093",
+      "metadata": {
+        "id": "8b234093"
+      },
+      "source": [
+        "Notice that the computation time is much shorter than that of LOOCV.\n",
+        "(In principle, the computation time for LOOCV for a least squares\n",
+        "linear model should be faster than for $K$-fold CV, due to the\n",
+        "availability of the formula~(\\ref{Ch5:eq:LOOCVform})  for LOOCV;\n",
+        "however, the generic `cross_validate()`  function does not make\n",
+        "use of this formula.)  We still see little evidence that using cubic\n",
+        "or higher-degree polynomial terms leads to a lower test error than simply\n",
+        "using a quadratic fit."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fb4487a4",
+      "metadata": {
+        "id": "fb4487a4"
+      },
+      "source": [
+        "The `cross_validate()` function is flexible and can take\n",
+        "different splitting mechanisms as an argument. For instance, one can use the `ShuffleSplit()` funtion to implement\n",
+        "the validation set approach just as easily as K-fold cross-validation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "080cdb29",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.408750Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.408677Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.413979Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.413762Z"
+        },
+        "lines_to_next_cell": 2,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "080cdb29",
+        "outputId": "b10724e1-4d9b-4973-fa80-2eb2f726e74e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([23.61661707])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ],
+      "source": [
+        "validation = ShuffleSplit(n_splits=1,\n",
+        "                          test_size=196,\n",
+        "                          random_state=0)\n",
+        "results = cross_validate(hp_model,\n",
+        "                         Auto.drop(['mpg'], axis=1),\n",
+        "                         Auto['mpg'],\n",
+        "                         cv=validation);\n",
+        "results['test_score']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b2f4b4cf",
+      "metadata": {
+        "id": "b2f4b4cf"
+      },
+      "source": [
+        "One can estimate the variability in the test error by running the following:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "id": "7c46de2b",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.415225Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.415158Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.437526Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.437302Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7c46de2b",
+        "outputId": "4a4b0f0c-a859-468e-e8dc-1ac1e0293475"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(np.float64(23.802232661034168), np.float64(1.4218450941091842))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ],
+      "source": [
+        "validation = ShuffleSplit(n_splits=10,\n",
+        "                          test_size=196,\n",
+        "                          random_state=0)\n",
+        "results = cross_validate(hp_model,\n",
+        "                         Auto.drop(['mpg'], axis=1),\n",
+        "                         Auto['mpg'],\n",
+        "                         cv=validation)\n",
+        "results['test_score'].mean(), results['test_score'].std()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results['test_score']"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DbgSLDjbMmQQ",
+        "outputId": "16093914-0735-49f1-c1c3-4b312002e2e4"
+      },
+      "id": "DbgSLDjbMmQQ",
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([23.61661707, 22.96552529, 23.43853845, 21.72781699, 22.79416823,\n",
+              "       23.09191932, 23.69196999, 23.90184611, 26.53545818, 26.258467  ])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "07165f0e",
+      "metadata": {
+        "id": "07165f0e"
+      },
+      "source": [
+        "Note that this standard deviation is not a valid estimate of the\n",
+        "sampling variability of the mean test score or the individual scores, since the randomly-selected training\n",
+        "samples overlap and hence introduce correlations. But it does give an\n",
+        "idea of the Monte Carlo variation\n",
+        "incurred by picking different random folds.\n",
+        "\n",
+        "## The Bootstrap\n",
+        "We illustrate the use of the bootstrap in the simple example\n",
+        " {of Section~\\ref{Ch5:sec:bootstrap},}  as well as on an example involving\n",
+        "estimating the accuracy of the linear regression model on the  `Auto`\n",
+        "data set.\n",
+        "### Estimating the Accuracy of a Statistic of Interest\n",
+        "One of the great advantages of the bootstrap approach is that it can\n",
+        "be applied in almost all situations. No complicated mathematical\n",
+        "calculations are required. While there are several implementations\n",
+        "of the bootstrap in Python, its use for estimating\n",
+        "standard error is simple enough that we write our own function\n",
+        "below for the case when our data is stored\n",
+        "in a dataframe.\n",
+        "\n",
+        "To illustrate the bootstrap, we\n",
+        "start with a simple example.\n",
+        "The  `Portfolio`  data set in the `ISLP` package is described\n",
+        "in Section~\\ref{Ch5:sec:bootstrap}. The goal is to estimate the\n",
+        "sampling variance of the parameter $\\alpha$ given in formula~(\\ref{Ch5:min.var}).  We will\n",
+        "create a function\n",
+        "`alpha_func()`, which takes as input a dataframe `D` assumed\n",
+        "to have columns `X` and `Y`, as well as a\n",
+        "vector `idx` indicating which observations should be used to\n",
+        "estimate\n",
+        "$\\alpha$. The function then outputs the estimate for $\\alpha$ based on\n",
+        "the selected observations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "a4b6d9b3",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.438786Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.438714Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.441484Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.441268Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "a4b6d9b3"
+      },
+      "outputs": [],
+      "source": [
+        "Portfolio = load_data('Portfolio')\n",
+        "def alpha_func(D, idx):\n",
+        "   cov_ = np.cov(D[['X','Y']].loc[idx], rowvar=False)\n",
+        "   return ((cov_[1,1] - cov_[0,1]) /\n",
+        "           (cov_[0,0]+cov_[1,1]-2*cov_[0,1]))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Portfolio.shape\n",
+        "Portfolio.index"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "phfGMU7ImR8l",
+        "outputId": "b4a9b81d-008a-4922-98ea-59f18065ca80"
+      },
+      "id": "phfGMU7ImR8l",
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=100, step=1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9d50058e",
+      "metadata": {
+        "id": "9d50058e"
+      },
+      "source": [
+        "This function returns an estimate for $\\alpha$\n",
+        "based on applying the minimum\n",
+        "    variance formula (\\ref{Ch5:min.var}) to the observations indexed by\n",
+        "the argument `idx`.  For instance, the following command\n",
+        "estimates $\\alpha$ using all 100 observations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "81498a11",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.442843Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.442765Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.445171Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.444944Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "81498a11",
+        "outputId": "c5c8c84c-7819-490e-d913-12f4d9175680"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(0.57583207459283)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
+      "source": [
+        "alpha_func(Portfolio, range(100))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4f5d0aab",
+      "metadata": {
+        "id": "4f5d0aab"
+      },
+      "source": [
+        "Next we randomly select\n",
+        "100 observations from `range(100)`, with replacement. This is equivalent\n",
+        "to constructing a new bootstrap data set and recomputing $\\hat{\\alpha}$\n",
+        "based on the new data set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "64fe1cb6",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.446422Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.446354Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.448793Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.448579Z"
+        },
+        "lines_to_next_cell": 2,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "64fe1cb6",
+        "outputId": "3fb7207a-f76b-4a6a-8900-ec4a19a882ae"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(0.6074452469619004)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "rng = np.random.default_rng(0)\n",
+        "alpha_func(Portfolio,\n",
+        "           rng.choice(a = 100,\n",
+        "                      size = 100,\n",
+        "                      replace = True))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rng.choice?"
+      ],
+      "metadata": {
+        "id": "DSLHu7A8mprs"
+      },
+      "id": "DSLHu7A8mprs",
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "id": "91a635fe",
+      "metadata": {
+        "id": "91a635fe"
+      },
+      "source": [
+        "This process can be generalized to create a simple function `boot_SE()` for\n",
+        "computing the bootstrap standard error for arbitrary\n",
+        "functions that take only a data frame as an argument."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "dd16bbae",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.450062Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.449992Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.451958Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.451742Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "dd16bbae"
+      },
+      "outputs": [],
+      "source": [
+        "def boot_SE(func,\n",
+        "            D,\n",
+        "            n=None,\n",
+        "            B=1000,\n",
+        "            seed=0):\n",
+        "    rng = np.random.default_rng(seed)\n",
+        "    first_, second_ = 0, 0\n",
+        "    n = n or D.shape[0]\n",
+        "    for _ in range(B):\n",
+        "        idx = rng.choice(a = D.index,\n",
+        "                         size = n,\n",
+        "                         replace=True)\n",
+        "        value = func(D, idx)\n",
+        "        first_ += value\n",
+        "        second_ += value**2\n",
+        "    return np.sqrt(second_ / B - (first_ / B)**2)# E[X^2] - (E[X])^2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ac4e17ed",
+      "metadata": {
+        "id": "ac4e17ed"
+      },
+      "source": [
+        "Notice the use of `_` as a loop variable in `for _ in range(B)`. This is often used if the value of the counter is\n",
+        "unimportant and simply makes sure  the loop is executed `B` times.\n",
+        "\n",
+        "Let’s use our function to evaluate the accuracy of our\n",
+        "estimate of $\\alpha$ using $B=1{,}000$ bootstrap replications."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "b42b4585",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.453190Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.453118Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.631597Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.631370Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b42b4585",
+        "outputId": "7f9f9640-8fc1-4b60-d987-a98ef08f7824"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(0.09118176521277699)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "alpha_SE = boot_SE(alpha_func,\n",
+        "                   Portfolio,\n",
+        "                   B=1000,\n",
+        "                   seed=0)\n",
+        "alpha_SE\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6c5464d7",
+      "metadata": {
+        "id": "6c5464d7"
+      },
+      "source": [
+        "The final output shows that the bootstrap estimate for ${\\rm SE}(\\hat{\\alpha})$ is $0.0912$.\n",
+        "\n",
+        "### Estimating the Accuracy of a Linear Regression Model\n",
+        "The bootstrap approach can be used to assess the variability of the\n",
+        "coefficient estimates and predictions from a statistical learning\n",
+        "method. Here we use the bootstrap approach in order to assess the\n",
+        "variability of the estimates for $\\beta_0$ and $\\beta_1$, the\n",
+        "intercept and slope terms for the linear regression model that uses\n",
+        "`horsepower` to predict `mpg` in the  `Auto`  data set. We\n",
+        "will compare the estimates obtained using the bootstrap to those\n",
+        "obtained using the formulas for ${\\rm SE}(\\hat{\\beta}_0)$ and\n",
+        "${\\rm SE}(\\hat{\\beta}_1)$ described in Section~\\ref{Ch3:secoefsec}.\n",
+        "\n",
+        "To use our `boot_SE()` function, we must write a function (its\n",
+        "first argument)\n",
+        "that takes a data frame `D` and indices `idx`\n",
+        "as its only arguments. But here we want to bootstrap a specific\n",
+        "regression model, specified by a model formula and data. We show how\n",
+        "to do this in a few simple steps.\n",
+        "\n",
+        "We start by writing a generic\n",
+        "function `boot_OLS()` for bootstrapping a regression model that takes a formula to\n",
+        "define the corresponding regression. We use the `clone()` function to\n",
+        "make a copy of the formula that can be refit to the new dataframe. This means\n",
+        "that any derived features such as those defined by `poly()`\n",
+        "(which we will see shortly),\n",
+        "will be re-fit on the resampled data frame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "6bc11784",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.632802Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.632725Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.634450Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.634222Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "6bc11784"
+      },
+      "outputs": [],
+      "source": [
+        "def boot_OLS(model_matrix, response, D, idx):\n",
+        "    D_ = D.loc[idx]\n",
+        "    Y_ = D_[response]\n",
+        "    X_ = clone(model_matrix).fit_transform(D_)\n",
+        "    return sm.OLS(Y_, X_).fit().params"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2a6ea3ce",
+      "metadata": {
+        "id": "2a6ea3ce"
+      },
+      "source": [
+        "This is not quite what is needed as the first argument to\n",
+        "`boot_SE()`. The first two arguments which specify the model will not change in the\n",
+        "bootstrap process, and we would like to *freeze* them.   The\n",
+        "function `partial()` from the `functools` module  does precisely this: it takes a function\n",
+        "as an argument, and freezes some of its arguments, starting from the\n",
+        "left. We use it to freeze the first two model-formula arguments of `boot_OLS()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "740cd50c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.635644Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.635575Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.637097Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.636867Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "740cd50c"
+      },
+      "outputs": [],
+      "source": [
+        "hp_func = partial(boot_OLS, MS(['horsepower']), 'mpg')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ed6d19e2",
+      "metadata": {
+        "id": "ed6d19e2"
+      },
+      "source": [
+        "Typing `hp_func?` will show that it has two arguments `D`\n",
+        "and `idx` --- it is a version of `boot_OLS()` with the first\n",
+        "two arguments frozen --- and hence is ideal as the first argument for `boot_SE()`.\n",
+        "\n",
+        "The `hp_func()` function can now be used in order to create\n",
+        "bootstrap estimates for the intercept and slope terms by randomly\n",
+        "sampling from among the observations with replacement. We first\n",
+        "demonstrate its utility on 10 bootstrap samples."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "ffb3ec50",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.638287Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.638220Z",
+          "iopub.status.idle": "2024-06-04T23:19:15.656475Z",
+          "shell.execute_reply": "2024-06-04T23:19:15.656261Z"
+        },
+        "lines_to_next_cell": 0,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ffb3ec50",
+        "outputId": "fdf48a72-69ab-4885-94cd-5234fc87534f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[39.12226577, -0.1555926 ],\n",
+              "       [37.18648613, -0.13915813],\n",
+              "       [37.46989244, -0.14112749],\n",
+              "       [38.56723252, -0.14830116],\n",
+              "       [38.95495707, -0.15315141],\n",
+              "       [39.12563927, -0.15261044],\n",
+              "       [38.45763251, -0.14767251],\n",
+              "       [38.43372587, -0.15019447],\n",
+              "       [37.87581142, -0.1409544 ],\n",
+              "       [37.95949036, -0.1451333 ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
+      "source": [
+        "rng = np.random.default_rng(0)\n",
+        "np.array([hp_func(Auto,\n",
+        "          rng.choice(Auto.index,\n",
+        "                     392,\n",
+        "                     replace=True)) for _ in range(10)])\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c6d09d96",
+      "metadata": {
+        "id": "c6d09d96"
+      },
+      "source": [
+        "Next, we use the `boot_SE()` {}  function to compute the standard\n",
+        "errors of 1,000 bootstrap estimates for the intercept and slope terms."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "7d561f70",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:15.657733Z",
+          "iopub.status.busy": "2024-06-04T23:19:15.657659Z",
+          "iopub.status.idle": "2024-06-04T23:19:17.204871Z",
+          "shell.execute_reply": "2024-06-04T23:19:17.204614Z"
+        },
+        "lines_to_next_cell": 2,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 147
+        },
+        "id": "7d561f70",
+        "outputId": "5b458383-be94-4ffe-8e5b-09d49a96e6d5"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "intercept     0.731176\n",
+              "horsepower    0.006092\n",
+              "dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>0.731176</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>horsepower</th>\n",
+              "      <td>0.006092</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "hp_se = boot_SE(hp_func,\n",
+        "                Auto,\n",
+        "                B=1000,\n",
+        "                seed=10)\n",
+        "hp_se\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a834f240",
+      "metadata": {
+        "id": "a834f240"
+      },
+      "source": [
+        "This indicates that the bootstrap estimate for ${\\rm SE}(\\hat{\\beta}_0)$ is\n",
+        "0.85, and that the bootstrap\n",
+        "estimate for ${\\rm SE}(\\hat{\\beta}_1)$ is\n",
+        "0.0074.  As discussed in\n",
+        "Section~\\ref{Ch3:secoefsec}, standard formulas can be used to compute\n",
+        "the standard errors for the regression coefficients in a linear\n",
+        "model. These can be obtained using the `summarize()`  function\n",
+        "from `ISLP.sm`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "id": "3888aa0a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:17.206302Z",
+          "iopub.status.busy": "2024-06-04T23:19:17.206223Z",
+          "iopub.status.idle": "2024-06-04T23:19:17.221631Z",
+          "shell.execute_reply": "2024-06-04T23:19:17.221444Z"
+        },
+        "lines_to_next_cell": 2,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 147
+        },
+        "id": "3888aa0a",
+        "outputId": "36c5f66b-6474-4146-b329-2cb7f318ea21"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "intercept     0.717\n",
+              "horsepower    0.006\n",
+              "Name: std err, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>std err</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>0.717</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>horsepower</th>\n",
+              "      <td>0.006</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ],
+      "source": [
+        "hp_model.fit(Auto, Auto['mpg'])\n",
+        "model_se = summarize(hp_model.results_)['std err']\n",
+        "model_se\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "aefc0575",
+      "metadata": {
+        "id": "aefc0575"
+      },
+      "source": [
+        "The standard error estimates for $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$\n",
+        "obtained using the formulas  from Section~\\ref{Ch3:secoefsec}  are\n",
+        "0.717 for the\n",
+        "intercept and\n",
+        "0.006 for the\n",
+        "slope. Interestingly, these are somewhat different from the estimates\n",
+        "obtained using the bootstrap.  Does this indicate a problem with the\n",
+        "bootstrap? In fact, it suggests the opposite.  Recall that the\n",
+        "standard formulas given in\n",
+        " {Equation~\\ref{Ch3:se.eqn} on page~\\pageref{Ch3:se.eqn}}\n",
+        "rely on certain assumptions. For example,\n",
+        "they depend on the unknown parameter $\\sigma^2$, the noise\n",
+        "variance. We then estimate $\\sigma^2$ using the RSS. Now although the\n",
+        "formula for the standard errors do not rely on the linear model being\n",
+        "correct, the estimate for $\\sigma^2$ does.  We see\n",
+        " {in Figure~\\ref{Ch3:polyplot} on page~\\pageref{Ch3:polyplot}}  that there is\n",
+        "a non-linear relationship in the data, and so the residuals from a\n",
+        "linear fit will be inflated, and so will $\\hat{\\sigma}^2$.  Secondly,\n",
+        "the standard formulas assume (somewhat unrealistically) that the $x_i$\n",
+        "are fixed, and all the variability comes from the variation in the\n",
+        "errors $\\epsilon_i$.  The bootstrap approach does not rely on any of\n",
+        "these assumptions, and so it is likely giving a more accurate estimate\n",
+        "of the standard errors of $\\hat{\\beta}_0$ and $\\hat{\\beta}_1$ than\n",
+        "the results from `sm.OLS`.\n",
+        "\n",
+        "Below we compute the bootstrap standard error estimates and the\n",
+        "standard linear regression estimates that result from fitting the\n",
+        "quadratic model to the data. Since this model provides a good fit to\n",
+        "the data (Figure~\\ref{Ch3:polyplot}), there is now a better\n",
+        "correspondence between the bootstrap estimates and the standard\n",
+        "estimates of ${\\rm SE}(\\hat{\\beta}_0)$, ${\\rm SE}(\\hat{\\beta}_1)$ and\n",
+        "${\\rm SE}(\\hat{\\beta}_2)$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "id": "acc3e32c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:17.222887Z",
+          "iopub.status.busy": "2024-06-04T23:19:17.222785Z",
+          "iopub.status.idle": "2024-06-04T23:19:19.351574Z",
+          "shell.execute_reply": "2024-06-04T23:19:19.351317Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "acc3e32c",
+        "outputId": "447a8b9f-5b79-4968-c3cd-5349df859ad2"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "intercept                                  1.538641\n",
+              "poly(horsepower, degree=2, raw=True)[0]    0.024696\n",
+              "poly(horsepower, degree=2, raw=True)[1]    0.000090\n",
+              "dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>1.538641</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(horsepower, degree=2, raw=True)[0]</th>\n",
+              "      <td>0.024696</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(horsepower, degree=2, raw=True)[1]</th>\n",
+              "      <td>0.000090</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 39
+        }
+      ],
+      "source": [
+        "quad_model = MS([poly('horsepower', 2, raw=True)])\n",
+        "quad_func = partial(boot_OLS,\n",
+        "                    quad_model,\n",
+        "                    'mpg')\n",
+        "boot_SE(quad_func, Auto, B=1000)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e8a2fd2b",
+      "metadata": {
+        "id": "e8a2fd2b"
+      },
+      "source": [
+        "We  compare the results to the standard errors computed using `sm.OLS()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "id": "dca5340c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:19.352904Z",
+          "iopub.status.busy": "2024-06-04T23:19:19.352827Z",
+          "iopub.status.idle": "2024-06-04T23:19:19.360147Z",
+          "shell.execute_reply": "2024-06-04T23:19:19.359948Z"
+        },
+        "lines_to_next_cell": 0,
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "dca5340c",
+        "outputId": "626e2e1e-48b2-45c6-cb1e-0614ecba7bde"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "intercept                                  1.800\n",
+              "poly(horsepower, degree=2, raw=True)[0]    0.031\n",
+              "poly(horsepower, degree=2, raw=True)[1]    0.000\n",
+              "Name: std err, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>std err</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>1.800</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(horsepower, degree=2, raw=True)[0]</th>\n",
+              "      <td>0.031</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(horsepower, degree=2, raw=True)[1]</th>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ],
+      "source": [
+        "M = sm.OLS(Auto['mpg'],\n",
+        "           quad_model.fit_transform(Auto))\n",
+        "summarize(M.fit())['std err']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e98297be",
+      "metadata": {
+        "id": "e98297be"
+      },
+      "source": [
+        "\n",
+        "\n"
       ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "M = sm.OLS(Auto['mpg'],\n",
-    "           quad_model.fit_transform(Auto))\n",
-    "summarize(M.fit())['std err']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e98297be",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
+  ],
+  "metadata": {
+    "jupytext": {
+      "cell_metadata_filter": "-all",
+      "main_language": "python",
+      "notebook_metadata_filter": "-all"
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.3"
+    },
+    "colab": {
+      "provenance": []
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/Ch06-varselect-lab.ipynb b/Ch06-varselect-lab.ipynb
index 765a7fa8..171b42c6 100644
--- a/Ch06-varselect-lab.ipynb
+++ b/Ch06-varselect-lab.ipynb
@@ -1,6412 +1,6830 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e8a7df77",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Linear Models and Regularization Methods\n",
-    "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch06-varselect-lab.ipynb\">\n",
-    "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-    "</a>\n",
-    "\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch06-varselect-lab.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d12a766",
-   "metadata": {},
-   "source": [
-    "In this lab we implement many of the techniques discussed in this chapter.\n",
-    "We import some of our libraries at this top\n",
-    "level. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b59f6bc4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:20.427195Z",
-     "iopub.status.busy": "2024-06-04T23:19:20.426849Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.241188Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.240924Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from matplotlib.pyplot import subplots\n",
-    "from statsmodels.api import OLS\n",
-    "import sklearn.model_selection as skm\n",
-    "import sklearn.linear_model as skl\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from ISLP import load_data\n",
-    "from ISLP.models import ModelSpec as MS\n",
-    "from functools import partial\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c16c47d1",
-   "metadata": {},
-   "source": [
-    "We again collect the new imports\n",
-    "needed for this lab. Readers will also have to have installed `l0bnb` using `pip install l0bnb`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8440e64a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.242868Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.242739Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.482878Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.482642Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.decomposition import PCA\n",
-    "from sklearn.cross_decomposition import PLSRegression\n",
-    "from ISLP.models import \\\n",
-    "     (Stepwise,\n",
-    "      sklearn_selected,\n",
-    "      sklearn_selection_path)\n",
-    "from l0bnb import fit_path\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bfbf09cb",
-   "metadata": {},
-   "source": [
-    "## Subset Selection Methods\n",
-    "Here we implement methods that reduce the number of parameters in a\n",
-    "model by restricting the model to a subset of the input variables.\n",
-    "\n",
-    "### Forward Selection\n",
-    " \n",
-    "We will  apply the forward-selection approach to the  `Hitters` \n",
-    "data.  We wish to predict a baseball player’s `Salary` on the\n",
-    "basis of various statistics associated with performance in the\n",
-    "previous year.\n",
-    "\n",
-    "First of all, we note that the `Salary` variable is missing for\n",
-    "some of the players.  The `np.isnan()`  function can be used to\n",
-    "identify the missing observations. It returns an array\n",
-    "of the same shape as the input vector, with a `True` for any elements that\n",
-    "are missing, and a `False` for non-missing elements.  The\n",
-    "`sum()`  method can then be used to count all of the\n",
-    "missing elements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "63d45749",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.484407Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.484321Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.489374Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.489194Z"
-    }
-   },
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "59"
+      "cell_type": "markdown",
+      "id": "e8a7df77",
+      "metadata": {
+        "id": "e8a7df77"
+      },
+      "source": [
+        "\n",
+        "# Linear Models and Regularization Methods\n",
+        "\n",
+        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch06-varselect-lab.ipynb\">\n",
+        "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+        "</a>\n",
+        "\n",
+        "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch06-varselect-lab.ipynb)"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Hitters = load_data('Hitters')\n",
-    "np.isnan(Hitters['Salary']).sum()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "25acaae1",
-   "metadata": {},
-   "source": [
-    " We see that `Salary` is missing for 59 players. The\n",
-    "`dropna()`  method of data frames removes all of the rows that have missing\n",
-    "values in any variable (by default --- see  `Hitters.dropna?`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "47262cbe",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.490561Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.490484Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.493093Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.492903Z"
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(263, 20)"
+      "cell_type": "markdown",
+      "id": "0d12a766",
+      "metadata": {
+        "id": "0d12a766"
+      },
+      "source": [
+        "In this lab we implement many of the techniques discussed in this chapter.\n",
+        "We import some of our libraries at this top\n",
+        "level."
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Hitters = Hitters.dropna();\n",
-    "Hitters.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5199f18e",
-   "metadata": {},
-   "source": [
-    "We first choose the best model using forward selection based on $C_p$ (\\ref{Ch6:eq:cp}). This score\n",
-    "is not built in as a metric to `sklearn`. We therefore define a function to compute it ourselves, and use\n",
-    "it as a scorer. By default, `sklearn` tries to maximize a score, hence\n",
-    "  our scoring function  computes the negative $C_p$ statistic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "096fe127",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.494225Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.494154Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.495738Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.495580Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "def nCp(sigma2, estimator, X, Y):\n",
-    "    \"Negative Cp statistic\"\n",
-    "    n, p = X.shape\n",
-    "    Yhat = estimator.predict(X)\n",
-    "    RSS = np.sum((Y - Yhat)**2)\n",
-    "    return -(RSS + 2 * p * sigma2) / n \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf9e3ca3",
-   "metadata": {},
-   "source": [
-    "We need to estimate the residual variance $\\sigma^2$, which is the first argument in our scoring function above.\n",
-    "We will fit the biggest model, using all the variables, and estimate $\\sigma^2$ based on its MSE."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b697d287",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.496810Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.496736Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.510614Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.510406Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "design = MS(Hitters.columns.drop('Salary')).fit(Hitters)\n",
-    "Y = np.array(Hitters['Salary'])\n",
-    "X = design.transform(Hitters)\n",
-    "sigma2 = OLS(Y,X).fit().scale\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bc283589",
-   "metadata": {},
-   "source": [
-    "The function `sklearn_selected()` expects a scorer with just three arguments --- the last three in the definition of `nCp()` above. We use the function `partial()` first seen in Section~\\ref{Ch5-resample-lab:the-bootstrap} to freeze the first argument with our estimate of $\\sigma^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2151b133",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.511833Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.511764Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.513251Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.513008Z"
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "b59f6bc4",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:20.427195Z",
+          "iopub.status.busy": "2024-06-04T23:19:20.426849Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.241188Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.240924Z"
+        },
+        "collapsed": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b59f6bc4",
+        "outputId": "40086c54-1aad-4ad5-9149-098969bae8b7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting ISLP\n",
+            "  Downloading ISLP-0.4.0-py3-none-any.whl.metadata (7.0 kB)\n",
+            "Requirement already satisfied: numpy>=1.7.1 in /usr/local/lib/python3.12/dist-packages (from ISLP) (2.0.2)\n",
+            "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.12/dist-packages (from ISLP) (1.16.2)\n",
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+            "Collecting lifelines (from ISLP)\n",
+            "  Downloading lifelines-0.30.0-py3-none-any.whl.metadata (3.2 kB)\n",
+            "Collecting pygam (from ISLP)\n",
+            "  Downloading pygam-0.10.1-py3-none-any.whl.metadata (9.7 kB)\n",
+            "Requirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (from ISLP) (2.8.0+cu126)\n",
+            "Collecting pytorch-lightning (from ISLP)\n",
+            "  Downloading pytorch_lightning-2.5.5-py3-none-any.whl.metadata (20 kB)\n",
+            "Collecting torchmetrics (from ISLP)\n",
+            "  Downloading torchmetrics-1.8.2-py3-none-any.whl.metadata (22 kB)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas>=0.20->ISLP) (2.9.0.post0)\n",
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+            "Collecting autograd-gamma>=0.3 (from lifelines->ISLP)\n",
+            "  Downloading autograd-gamma-0.5.0.tar.gz (4.0 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting formulaic>=0.2.2 (from lifelines->ISLP)\n",
+            "  Downloading formulaic-1.2.1-py3-none-any.whl.metadata (7.0 kB)\n",
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+            "Requirement already satisfied: typing-extensions>4.5.0 in /usr/local/lib/python3.12/dist-packages (from pytorch-lightning->ISLP) (4.15.0)\n",
+            "Collecting lightning-utilities>=0.10.0 (from pytorch-lightning->ISLP)\n",
+            "  Downloading lightning_utilities-0.15.2-py3-none-any.whl.metadata (5.7 kB)\n",
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+            "Collecting interface-meta>=1.2.0 (from formulaic>=0.2.2->lifelines->ISLP)\n",
+            "  Downloading interface_meta-1.3.0-py3-none-any.whl.metadata (6.7 kB)\n",
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+            "Downloading ISLP-0.4.0-py3-none-any.whl (3.6 MB)\n",
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+            "\u001b[?25hDownloading lightning_utilities-0.15.2-py3-none-any.whl (29 kB)\n",
+            "Downloading interface_meta-1.3.0-py3-none-any.whl (14 kB)\n",
+            "Building wheels for collected packages: autograd-gamma\n",
+            "  Building wheel for autograd-gamma (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for autograd-gamma: filename=autograd_gamma-0.5.0-py3-none-any.whl size=4030 sha256=8b1cdd86ff072ff0bf2d180779e58325a480a311f417ff1a32f066d18be3ce1c\n",
+            "  Stored in directory: /root/.cache/pip/wheels/50/37/21/0a719b9d89c635e89ff24bd93b862882ad675279552013b2fb\n",
+            "Successfully built autograd-gamma\n",
+            "Installing collected packages: lightning-utilities, interface-meta, autograd-gamma, pygam, formulaic, torchmetrics, lifelines, pytorch-lightning, ISLP\n",
+            "Successfully installed ISLP-0.4.0 autograd-gamma-0.5.0 formulaic-1.2.1 interface-meta-1.3.0 lifelines-0.30.0 lightning-utilities-0.15.2 pygam-0.10.1 pytorch-lightning-2.5.5 torchmetrics-1.8.2\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install ISLP\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from matplotlib.pyplot import subplots\n",
+        "from statsmodels.api import OLS\n",
+        "import sklearn.model_selection as skm\n",
+        "import sklearn.linear_model as skl\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from ISLP import load_data\n",
+        "from ISLP.models import ModelSpec as MS\n",
+        "from functools import partial\n"
+      ]
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "neg_Cp = partial(nCp, sigma2)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6993ae8",
-   "metadata": {},
-   "source": [
-    "We can now use `neg_Cp()` as a scorer for model selection.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "436a744d",
-   "metadata": {},
-   "source": [
-    "Along with a score we need to specify the search strategy. This is done through the object\n",
-    "`Stepwise()`  in the `ISLP.models` package. The method `Stepwise.first_peak()`\n",
-    "runs forward stepwise until any further additions to the model do not result\n",
-    "in an improvement in the evaluation score. Similarly, the method `Stepwise.fixed_steps()`\n",
-    "runs a fixed number of steps of stepwise search."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "ab28d6fd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.514438Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.514363Z",
-     "iopub.status.idle": "2024-06-04T23:19:21.515875Z",
-     "shell.execute_reply": "2024-06-04T23:19:21.515674Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "strategy = Stepwise.first_peak(design,\n",
-    "                               direction='forward',\n",
-    "                               max_terms=len(design.terms))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4bf946d2",
-   "metadata": {},
-   "source": [
-    "We now fit a linear regression model with `Salary` as outcome using forward\n",
-    "selection. To do so, we use the function `sklearn_selected()`  from the `ISLP.models` package. This takes\n",
-    "a model from `statsmodels` along with a search strategy and selects a model with its\n",
-    "`fit` method. Without specifying a `scoring` argument, the score defaults to MSE, and so all 19 variables will be\n",
-    "selected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "d8b345ec",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:21.517091Z",
-     "iopub.status.busy": "2024-06-04T23:19:21.517008Z",
-     "iopub.status.idle": "2024-06-04T23:19:22.178273Z",
-     "shell.execute_reply": "2024-06-04T23:19:22.178018Z"
-    }
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "('Assists',\n",
-       " 'AtBat',\n",
-       " 'CAtBat',\n",
-       " 'CHits',\n",
-       " 'CHmRun',\n",
-       " 'CRBI',\n",
-       " 'CRuns',\n",
-       " 'CWalks',\n",
-       " 'Division',\n",
-       " 'Errors',\n",
-       " 'Hits',\n",
-       " 'HmRun',\n",
-       " 'League',\n",
-       " 'NewLeague',\n",
-       " 'PutOuts',\n",
-       " 'RBI',\n",
-       " 'Runs',\n",
-       " 'Walks',\n",
-       " 'Years')"
+      "cell_type": "markdown",
+      "id": "c16c47d1",
+      "metadata": {
+        "id": "c16c47d1"
+      },
+      "source": [
+        "We again collect the new imports\n",
+        "needed for this lab. Readers will also have to have installed `l0bnb` using `pip install l0bnb`."
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hitters_MSE = sklearn_selected(OLS,\n",
-    "                               strategy)\n",
-    "hitters_MSE.fit(Hitters, Y)\n",
-    "hitters_MSE.selected_state_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1a78607",
-   "metadata": {},
-   "source": [
-    "Using `neg_Cp` results in a smaller model, as expected, with just 10 variables selected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "915cdff6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:22.179650Z",
-     "iopub.status.busy": "2024-06-04T23:19:22.179571Z",
-     "iopub.status.idle": "2024-06-04T23:19:22.573219Z",
-     "shell.execute_reply": "2024-06-04T23:19:22.572980Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "('Assists',\n",
-       " 'AtBat',\n",
-       " 'CAtBat',\n",
-       " 'CRBI',\n",
-       " 'CRuns',\n",
-       " 'CWalks',\n",
-       " 'Division',\n",
-       " 'Hits',\n",
-       " 'PutOuts',\n",
-       " 'Walks')"
+      "cell_type": "code",
+      "source": [
+        "!pip install l0bnb"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PSFjEqzB8Pzk",
+        "outputId": "3000aaef-0b19-4402-df2f-8b18c6cc65fc"
+      },
+      "id": "PSFjEqzB8Pzk",
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting l0bnb\n",
+            "  Downloading l0bnb-1.0.0.tar.gz (79 kB)\n",
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/79.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.8/79.8 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.12/dist-packages (from l0bnb) (2.0.2)\n",
+            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.12/dist-packages (from l0bnb) (1.16.2)\n",
+            "Requirement already satisfied: numba>=0.53.1 in /usr/local/lib/python3.12/dist-packages (from l0bnb) (0.60.0)\n",
+            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.12/dist-packages (from numba>=0.53.1->l0bnb) (0.43.0)\n",
+            "Building wheels for collected packages: l0bnb\n",
+            "  Building wheel for l0bnb (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for l0bnb: filename=l0bnb-1.0.0-py3-none-any.whl size=22296 sha256=50a27114795f3c0e78e206f0c9e02ec12e1a1e7519dbffd6d5ece21d80e5497b\n",
+            "  Stored in directory: /root/.cache/pip/wheels/0e/74/8b/8a39b657554150b98e1520971211118b5137e6a1873a85f66a\n",
+            "Successfully built l0bnb\n",
+            "Installing collected packages: l0bnb\n",
+            "Successfully installed l0bnb-1.0.0\n"
+          ]
+        }
       ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hitters_Cp = sklearn_selected(OLS,\n",
-    "                               strategy,\n",
-    "                               scoring=neg_Cp)\n",
-    "hitters_Cp.fit(Hitters, Y)\n",
-    "hitters_Cp.selected_state_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f5d37f87",
-   "metadata": {},
-   "source": [
-    "### Choosing Among Models Using the Validation Set Approach and Cross-Validation\n",
-    " \n",
-    "As an  alternative to using $C_p$, we might try cross-validation to select a model in forward selection. For this, we need a\n",
-    "method that stores the full path of models found in forward selection, and allows predictions for each of these. This can be done with the `sklearn_selection_path()` \n",
-    "estimator from `ISLP.models`. The function `cross_val_predict()` from `ISLP.models`\n",
-    "computes the cross-validated predictions for each of the models\n",
-    "along the path, which we can use to evaluate the cross-validated MSE\n",
-    "along the path."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6769115d",
-   "metadata": {},
-   "source": [
-    "Here we define a strategy that fits the full forward selection path.\n",
-    "While there are various parameter choices for `sklearn_selection_path()`,\n",
-    "we use the defaults here, which selects the model at each step based on the biggest reduction  in RSS."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "33ae4aa4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:22.574545Z",
-     "iopub.status.busy": "2024-06-04T23:19:22.574468Z",
-     "iopub.status.idle": "2024-06-04T23:19:22.576122Z",
-     "shell.execute_reply": "2024-06-04T23:19:22.575907Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "strategy = Stepwise.fixed_steps(design,\n",
-    "                                len(design.terms),\n",
-    "                                direction='forward')\n",
-    "full_path = sklearn_selection_path(OLS, strategy)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d397415c",
-   "metadata": {},
-   "source": [
-    "We now fit the full forward-selection path on the `Hitters` data and compute the fitted values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "f05e1a81",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:22.577302Z",
-     "iopub.status.busy": "2024-06-04T23:19:22.577233Z",
-     "iopub.status.idle": "2024-06-04T23:19:22.976283Z",
-     "shell.execute_reply": "2024-06-04T23:19:22.976065Z"
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(263, 20)"
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "8440e64a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.242868Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.242739Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.482878Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.482642Z"
+        },
+        "id": "8440e64a"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.decomposition import PCA\n",
+        "from sklearn.cross_decomposition import PLSRegression\n",
+        "from ISLP.models import \\\n",
+        "     (Stepwise,\n",
+        "      sklearn_selected,\n",
+        "      sklearn_selection_path)\n",
+        "from l0bnb import fit_path\n"
       ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_path.fit(Hitters, Y)\n",
-    "Yhat_in = full_path.predict(Hitters)\n",
-    "Yhat_in.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "767af527",
-   "metadata": {},
-   "source": [
-    "This gives us an array of fitted values --- 20 steps in all, including the fitted mean for the null model --- which we can use to evaluate\n",
-    "in-sample MSE. As expected, the in-sample MSE improves each step we take,\n",
-    "indicating we must use either the validation or cross-validation\n",
-    "approach to select the number of steps. We fix the y-axis to range from\n",
-    "50,000 to 250,000 to compare to the cross-validation and validation\n",
-    "set MSE below, as well as other methods such as ridge regression, lasso and\n",
-    "principal components regression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "0b07ae72",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:22.977573Z",
-     "iopub.status.busy": "2024-06-04T23:19:22.977503Z",
-     "iopub.status.idle": "2024-06-04T23:19:23.094004Z",
-     "shell.execute_reply": "2024-06-04T23:19:23.091687Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "bfbf09cb",
+      "metadata": {
+        "id": "bfbf09cb"
+      },
+      "source": [
+        "## Subset Selection Methods\n",
+        "Here we implement methods that reduce the number of parameters in a\n",
+        "model by restricting the model to a subset of the input variables.\n",
+        "\n",
+        "### Forward Selection\n",
+        "\n",
+        "We will  apply the forward-selection approach to the  `Hitters`\n",
+        "data.  We wish to predict a baseball player’s `Salary` on the\n",
+        "basis of various statistics associated with performance in the\n",
+        "previous year.\n",
+        "\n",
+        "First of all, we note that the `Salary` variable is missing for\n",
+        "some of the players.  The `np.isnan()`  function can be used to\n",
+        "identify the missing observations. It returns an array\n",
+        "of the same shape as the input vector, with a `True` for any elements that\n",
+        "are missing, and a `False` for non-missing elements.  The\n",
+        "`sum()`  method can then be used to count all of the\n",
+        "missing elements."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mse_fig, ax = subplots(figsize=(8,8))\n",
-    "insample_mse = ((Yhat_in - Y[:,None])**2).mean(0)\n",
-    "n_steps = insample_mse.shape[0]\n",
-    "ax.plot(np.arange(n_steps),\n",
-    "        insample_mse,\n",
-    "        'k', # color black\n",
-    "        label='In-sample')\n",
-    "ax.set_ylabel('MSE',\n",
-    "              fontsize=20)\n",
-    "ax.set_xlabel('# steps of forward stepwise',\n",
-    "              fontsize=20)\n",
-    "ax.set_xticks(np.arange(n_steps)[::2])\n",
-    "ax.legend()\n",
-    "ax.set_ylim([50000,250000]);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5f7508e0",
-   "metadata": {},
-   "source": [
-    "Notice the expression `None` in `Y[:,None]` above.\n",
-    "This adds an axis (dimension) to the one-dimensional array `Y`,\n",
-    "which allows it to be recycled when subtracted from the two-dimensional `Yhat_in`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f6c9531a",
-   "metadata": {},
-   "source": [
-    "We are now ready to use cross-validation to estimate test error along\n",
-    "the model path. We must use *only the training observations* to perform all aspects of model-fitting --- including\n",
-    "variable selection.  Therefore, the determination of which model of a\n",
-    "given size is best must be made using \\emph{only the training\n",
-    "  observations} in each training fold. This point is subtle but important.  If the full data\n",
-    "set is used to select the best subset at each step, then the validation\n",
-    "set errors and cross-validation errors that we obtain will not be\n",
-    "accurate estimates of the test error.\n",
-    "\n",
-    "We now compute the cross-validated predicted values using 5-fold cross-validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "a1654ad8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:23.097519Z",
-     "iopub.status.busy": "2024-06-04T23:19:23.097271Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.139415Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.139180Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(263, 20)"
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "63d45749",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.484407Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.484321Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.489374Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.489194Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "63d45749",
+        "outputId": "b6eaf6d7-9390-41d5-912d-9e3806f32811"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.int64(59)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
+      "source": [
+        "Hitters = load_data('Hitters')\n",
+        "np.isnan(Hitters['Salary']).sum()\n"
       ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "K = 5\n",
-    "kfold = skm.KFold(K,\n",
-    "                  random_state=0,\n",
-    "                  shuffle=True)\n",
-    "Yhat_cv = skm.cross_val_predict(full_path,\n",
-    "                                Hitters,\n",
-    "                                Y,\n",
-    "                                cv=kfold)\n",
-    "Yhat_cv.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "98dfabdc",
-   "metadata": {},
-   "source": [
-    "`skm.cross_val_predict()`\n",
-    "The prediction matrix `Yhat_cv` is the same shape as `Yhat_in`; the difference is that the predictions in each row, corresponding to a particular sample index, were made from models fit on a training fold that did not include that row.\n",
-    "\n",
-    "At each model along the path, we compute the MSE in each of the cross-validation folds.\n",
-    "These we will average to get the  mean MSE, and can also use the individual values to compute a crude estimate of the standard error of the mean. {The estimate is crude because the five error estimates  are based on overlapping training sets, and hence are not independent.}\n",
-    "Hence we must know the test indices for each cross-validation\n",
-    "split. This can be found by using the `split()` method of `kfold`. Because\n",
-    "we  fixed the random state above, whenever we split any array with the same\n",
-    "number of rows as $Y$ we recover the same training and test indices, though we simply\n",
-    "ignore the training indices below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "ce919bd3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.140773Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.140698Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.143330Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.143113Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(20, 5)"
+      "cell_type": "code",
+      "source": [
+        "# Does the same thing!\n",
+        "Hitters[np.isnan(Hitters['Salary'])].shape[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "i3qTR0Lc878_",
+        "outputId": "1f0ab34f-599d-4876-a717-b63cd61e942e"
+      },
+      "id": "i3qTR0Lc878_",
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "59"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
       ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cv_mse = []\n",
-    "for train_idx, test_idx in kfold.split(Y):\n",
-    "    errors = (Yhat_cv[test_idx] - Y[test_idx,None])**2\n",
-    "    cv_mse.append(errors.mean(0)) # column means\n",
-    "cv_mse = np.array(cv_mse).T\n",
-    "cv_mse.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a27004be",
-   "metadata": {},
-   "source": [
-    "We now add the cross-validation error estimates to our MSE plot.\n",
-    "We include the mean error across the five folds, and the estimate of the standard error of the mean. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "26520d22",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.144503Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.144435Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.201431Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.201214Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "25acaae1",
+      "metadata": {
+        "id": "25acaae1"
+      },
+      "source": [
+        " We see that `Salary` is missing for 59 players. The\n",
+        "`dropna()`  method of data frames removes all of the rows that have missing\n",
+        "values in any variable (by default --- see  `Hitters.dropna?`)."
       ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ax.errorbar(np.arange(n_steps), \n",
-    "            cv_mse.mean(1),\n",
-    "            cv_mse.std(1) / np.sqrt(K),\n",
-    "            label='Cross-validated',\n",
-    "            c='r') # color red\n",
-    "ax.set_ylim([50000,250000])\n",
-    "ax.legend()\n",
-    "mse_fig\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9881566e",
-   "metadata": {},
-   "source": [
-    "To repeat the above using the validation set approach, we simply change our\n",
-    "`cv` argument to a validation set: one random split of the data into a test and training. We choose a test size\n",
-    "of 20%, similar to the size of each test set in 5-fold cross-validation.`skm.ShuffleSplit()`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "f19c9091",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.202701Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.202634Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.603532Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.603301Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "validation = skm.ShuffleSplit(n_splits=1, \n",
-    "                              test_size=0.2,\n",
-    "                              random_state=0)\n",
-    "for train_idx, test_idx in validation.split(Y):\n",
-    "    full_path.fit(Hitters.iloc[train_idx],\n",
-    "                  Y[train_idx])\n",
-    "    Yhat_val = full_path.predict(Hitters.iloc[test_idx])\n",
-    "    errors = (Yhat_val - Y[test_idx,None])**2\n",
-    "    validation_mse = errors.mean(0)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7421fc11",
-   "metadata": {},
-   "source": [
-    " As for the in-sample MSE case, the validation set approach does not provide standard errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "1fca6b08",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.604861Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.604789Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.664722Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.664495Z"
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "47262cbe",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.490561Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.490484Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.493093Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.492903Z"
+        },
+        "lines_to_next_cell": 2,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "47262cbe",
+        "outputId": "8a65724a-6077-4272-ba94-18b4414639aa"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(263, 20)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ],
+      "source": [
+        "Hitters = Hitters.dropna();\n",
+        "Hitters.shape\n"
+      ]
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "5199f18e",
+      "metadata": {
+        "id": "5199f18e"
+      },
+      "source": [
+        "We first choose the best model using forward selection based on $C_p$ (\\ref{Ch6:eq:cp}). This score\n",
+        "is not built in as a metric to `sklearn`. We therefore define a function to compute it ourselves, and use\n",
+        "it as a scorer. By default, `sklearn` tries to maximize a score, hence\n",
+        "  our scoring function  computes the negative $C_p$ statistic."
       ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ax.plot(np.arange(n_steps), \n",
-    "        validation_mse,\n",
-    "        'b--', # color blue, broken line\n",
-    "        label='Validation')\n",
-    "ax.set_xticks(np.arange(n_steps)[::2])\n",
-    "ax.set_ylim([50000,250000])\n",
-    "ax.legend()\n",
-    "mse_fig\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b4df366a",
-   "metadata": {},
-   "source": [
-    "### Best Subset Selection\n",
-    "Forward stepwise is a *greedy* selection procedure; at each step it augments the current set by including one additional variable.  We now apply best subset selection  to the  `Hitters` \n",
-    "data, which for every subset size, searches for the best set of predictors.  \n",
-    "\n",
-    "We will use a package called `l0bnb` to perform\n",
-    "best subset selection.\n",
-    "Instead of constraining the subset to be a given size,\n",
-    "this package produces a path of solutions using the subset size as a\n",
-    "penalty rather than a constraint. Although the distinction is subtle, the difference comes when we cross-validate. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "6e9dd7d4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.665992Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.665901Z",
-     "iopub.status.idle": "2024-06-04T23:19:25.679390Z",
-     "shell.execute_reply": "2024-06-04T23:19:25.679171Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "D = design.fit_transform(Hitters)\n",
-    "D = D.drop('intercept', axis=1)\n",
-    "X = np.asarray(D)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b87bd74",
-   "metadata": {},
-   "source": [
-    "Here we excluded the first column corresponding to the intercept, as\n",
-    "`l0bnb` will fit the intercept separately. We can find a path using the `fit_path()` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "caed84ae",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:25.680677Z",
-     "iopub.status.busy": "2024-06-04T23:19:25.680609Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.246130Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.245877Z"
-    }
-   },
-   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Preprocessing Data.\n",
-      "BnB Started.\n"
-     ]
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "096fe127",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.494225Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.494154Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.495738Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.495580Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "096fe127"
+      },
+      "outputs": [],
+      "source": [
+        "def nCp(sigma2, estimator, X, Y):\n",
+        "    \"Negative Cp statistic\"\n",
+        "    n, p = X.shape\n",
+        "    Yhat = estimator.predict(X)\n",
+        "    RSS = np.sum((Y - Yhat)**2)\n",
+        "    return -(RSS + 2 * p * sigma2) / n\n"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 1. Number of non-zeros:  1\n",
-      "Iteration: 2. Number of non-zeros:  2\n",
-      "Iteration: 3. Number of non-zeros:  2\n"
-     ]
+      "cell_type": "markdown",
+      "id": "bf9e3ca3",
+      "metadata": {
+        "id": "bf9e3ca3"
+      },
+      "source": [
+        "We need to estimate the residual variance $\\sigma^2$, which is the first argument in our scoring function above.\n",
+        "We will fit the biggest model, using all the variables, and estimate $\\sigma^2$ based on its MSE."
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 4. Number of non-zeros:  2\n",
-      "Iteration: 5. Number of non-zeros:  3\n"
-     ]
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b697d287",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.496810Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.496736Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.510614Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.510406Z"
+        },
+        "id": "b697d287"
+      },
+      "outputs": [],
+      "source": [
+        "design = MS(Hitters.columns.drop('Salary')).fit(Hitters)\n",
+        "Y = np.array(Hitters['Salary'])\n",
+        "X = design.transform(Hitters)\n",
+        "sigma2 = OLS(Y,X).fit().scale\n"
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 6. Number of non-zeros:  3\n",
-      "Iteration: 7. Number of non-zeros:  4\n"
-     ]
+      "cell_type": "markdown",
+      "id": "bc283589",
+      "metadata": {
+        "id": "bc283589"
+      },
+      "source": [
+        "The function `sklearn_selected()` expects a scorer with just three arguments --- the last three in the definition of `nCp()` above. We use the function `partial()` first seen in Section~\\ref{Ch5-resample-lab:the-bootstrap} to freeze the first argument with our estimate of $\\sigma^2$."
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 8. Number of non-zeros:  9\n",
-      "Iteration: 9. Number of non-zeros:  9\n",
-      "Iteration: 10. Number of non-zeros:  9\n",
-      "Iteration: 11. Number of non-zeros:  9\n",
-      "Iteration: 12. Number of non-zeros:  9\n",
-      "Iteration: 13. Number of non-zeros:  9\n",
-      "Iteration: 14. Number of non-zeros:  9\n",
-      "Iteration: 15. Number of non-zeros:  9\n",
-      "Iteration: 16. Number of non-zeros:  9\n",
-      "Iteration: 17. Number of non-zeros:  9\n",
-      "Iteration: 18. Number of non-zeros:  17\n",
-      "Iteration: 19. Number of non-zeros:  19\n"
-     ]
-    }
-   ],
-   "source": [
-    "path = fit_path(X, \n",
-    "                Y,\n",
-    "                max_nonzeros=X.shape[1])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4e9ca6a",
-   "metadata": {},
-   "source": [
-    "The function `fit_path()` returns a list whose values include the fitted coefficients as `B`, an intercept as `B0`, as well as a few other attributes related to the particular path algorithm used. Such details are beyond the scope of this book."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "f2fc2ef8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.247604Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.247525Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.249696Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.249497Z"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2151b133",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.511833Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.511764Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.513251Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.513008Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "2151b133"
+      },
+      "outputs": [],
+      "source": [
+        "neg_Cp = partial(nCp, sigma2)\n"
+      ]
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "{'B': array([0.        , 3.25484367, 0.        , 0.        , 0.        ,\n",
-       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
-       "        0.        , 0.67775265, 0.        , 0.        , 0.        ,\n",
-       "        0.        , 0.        , 0.        , 0.        ]),\n",
-       " 'B0': -38.98216739555551,\n",
-       " 'lambda_0': 0.011416248027450178,\n",
-       " 'M': 0.5829861733382012,\n",
-       " 'Time_exceeded': False}"
+      "cell_type": "markdown",
+      "id": "f6993ae8",
+      "metadata": {
+        "id": "f6993ae8"
+      },
+      "source": [
+        "We can now use `neg_Cp()` as a scorer for model selection.\n"
       ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "path[3]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c4a96126",
-   "metadata": {},
-   "source": [
-    "In the example above, we see that at the fourth step in the path, we have two nonzero coefficients in `'B'`, corresponding to the value $0.114$ for the penalty parameter `lambda_0`.\n",
-    "We could make predictions using this sequence of fits on a validation set as a function of `lambda_0`, or with more work using cross-validation.\n",
-    "\n",
-    "## Ridge Regression and the Lasso\n",
-    "We will use the `sklearn.linear_model` package (for which\n",
-    "we use `skl` as shorthand below)\n",
-    "to fit ridge and  lasso regularized linear models on the `Hitters` data.\n",
-    "We start with the model matrix `X` (without an intercept) that we computed in the previous section on best subset regression.\n",
-    "\n",
-    "### Ridge Regression\n",
-    "We will use the function `skl.ElasticNet()` to fit both  ridge and the lasso.\n",
-    "To fit a *path* of ridge regressions models, we use\n",
-    "`skl.ElasticNet.path()`, which can fit both ridge and lasso, as well as a hybrid mixture;  ridge regression\n",
-    "corresponds to `l1_ratio=0`.\n",
-    "It is good practice to standardize the columns of `X` in these applications, if the variables are measured in different units. Since `skl.ElasticNet()` does no normalization, we have to take care of that ourselves.\n",
-    "Since we\n",
-    "standardize first, in order to find coefficient\n",
-    "estimates on the original scale, we must *unstandardize*\n",
-    "the coefficient estimates. The parameter\n",
-    "$\\lambda$ in (\\ref{Ch6:ridge}) and (\\ref{Ch6:LASSO}) is called `alphas` in `sklearn`. In order to\n",
-    "be consistent with the rest of this chapter, we use `lambdas`\n",
-    "rather than `alphas` in what follows.  {At the time of publication, ridge fits like the one in code chunk [22] issue unwarranted convergence warning messages; we expect these to disappear as this package matures.}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "4a5d6fc8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.250946Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.250878Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.303537Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.303273Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(19, 100)"
+      "cell_type": "markdown",
+      "id": "436a744d",
+      "metadata": {
+        "id": "436a744d"
+      },
+      "source": [
+        "Along with a score we need to specify the search strategy. This is done through the object\n",
+        "`Stepwise()`  in the `ISLP.models` package. The method `Stepwise.first_peak()`\n",
+        "runs forward stepwise until any further additions to the model do not result\n",
+        "in an improvement in the evaluation score. Similarly, the method `Stepwise.fixed_steps()`\n",
+        "runs a fixed number of steps of stepwise search."
       ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Xs = X - X.mean(0)[None,:]\n",
-    "X_scale = X.std(0)\n",
-    "Xs = Xs / X_scale[None,:]\n",
-    "lambdas = 10**np.linspace(8, -2, 100) / Y.std()\n",
-    "soln_array = skl.ElasticNet.path(Xs,\n",
-    "                                 Y,\n",
-    "                                 l1_ratio=0.,\n",
-    "                                 alphas=lambdas)[1]\n",
-    "soln_array.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e7770f5",
-   "metadata": {},
-   "source": [
-    "Here we extract the array of coefficients corresponding to the solutions along the regularization path.\n",
-    "By default the `skl.ElasticNet.path` method fits a path along\n",
-    "an automatically selected range of $\\lambda$ values, except for the case when\n",
-    "`l1_ratio=0`, which results in ridge regression (as is the case here). {The reason is rather technical; for all models except ridge, we can find the smallest value of $\\lambda$ for which all coefficients are zero. For ridge this value is $\\infty$.}  So here\n",
-    "we have chosen to implement the function over a grid of values ranging\n",
-    "from $\\lambda=10^{8}$ to $\\lambda=10^{-2}$ scaled by the standard\n",
-    "deviation of $y$, essentially covering the full range of scenarios\n",
-    "from the null model containing only the intercept, to the least\n",
-    "squares fit.\n",
-    "\n",
-    "Associated with each value of $\\lambda$ is a vector of ridge\n",
-    "regression coefficients,   that can be accessed by\n",
-    "a column of `soln_array`. In this case, `soln_array` is a $19 \\times 100$ matrix, with\n",
-    "19 rows (one for each predictor) and 100\n",
-    "columns (one for each value of $\\lambda$).\n",
-    "\n",
-    "We transpose this matrix and turn it into a data frame to facilitate viewing and plotting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "1bca28f6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.304830Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.304756Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.311709Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.311502Z"
-    }
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ab28d6fd",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.514438Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.514363Z",
+          "iopub.status.idle": "2024-06-04T23:19:21.515875Z",
+          "shell.execute_reply": "2024-06-04T23:19:21.515674Z"
+        },
+        "id": "ab28d6fd"
+      },
+      "outputs": [],
+      "source": [
+        "strategy = Stepwise.first_peak(design,\n",
+        "                               direction='forward',\n",
+        "                               max_terms=len(design.terms))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4bf946d2",
+      "metadata": {
+        "id": "4bf946d2"
+      },
+      "source": [
+        "We now fit a linear regression model with `Salary` as outcome using forward\n",
+        "selection. To do so, we use the function `sklearn_selected()`  from the `ISLP.models` package. This takes\n",
+        "a model from `statsmodels` along with a search strategy and selects a model with its\n",
+        "`fit` method. Without specifying a `scoring` argument, the score defaults to MSE, and so all 19 variables will be\n",
+        "selected."
+      ]
+    },
     {
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-       "      <td>0.001404</td>\n",
-       "      <td>0.001343</td>\n",
-       "      <td>0.001439</td>\n",
-       "      <td>0.001450</td>\n",
-       "      <td>0.001253</td>\n",
-       "      <td>-0.000037</td>\n",
-       "      <td>-0.000492</td>\n",
-       "      <td>0.000769</td>\n",
-       "      <td>0.000065</td>\n",
-       "      <td>-0.000014</td>\n",
-       "      <td>-0.000007</td>\n",
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-       "      <td>0.001355</td>\n",
-       "      <td>0.001451</td>\n",
-       "      <td>0.001433</td>\n",
-       "      <td>0.001293</td>\n",
-       "      <td>0.001698</td>\n",
-       "      <td>0.001772</td>\n",
-       "      <td>0.001694</td>\n",
-       "      <td>0.001816</td>\n",
-       "      <td>0.001830</td>\n",
-       "      <td>0.001581</td>\n",
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-       "      <td>-0.000621</td>\n",
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-       "      <td>-0.000017</td>\n",
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-       "      <th>-11.613102</th>\n",
-       "      <td>0.001608</td>\n",
-       "      <td>0.001787</td>\n",
-       "      <td>0.001397</td>\n",
-       "      <td>0.001710</td>\n",
-       "      <td>0.001831</td>\n",
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-       "      <td>0.000104</td>\n",
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-       "      <th>9.784658</th>\n",
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-       "      <td>336.929968</td>\n",
-       "      <td>37.322686</td>\n",
-       "      <td>-59.748520</td>\n",
-       "      <td>-26.507086</td>\n",
-       "      <td>134.855915</td>\n",
-       "      <td>-17.216195</td>\n",
-       "      <td>-387.775826</td>\n",
-       "      <td>89.573601</td>\n",
-       "      <td>-12.273926</td>\n",
-       "      <td>476.079273</td>\n",
-       "      <td>257.271255</td>\n",
-       "      <td>-213.124780</td>\n",
-       "      <td>31.258215</td>\n",
-       "      <td>-58.457857</td>\n",
-       "      <td>78.761266</td>\n",
-       "      <td>53.622113</td>\n",
-       "      <td>-22.208456</td>\n",
-       "      <td>-12.402891</td>\n",
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-       "    <tr>\n",
-       "      <th>10.017243</th>\n",
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-       "      <td>-26.606957</td>\n",
-       "      <td>134.900549</td>\n",
-       "      <td>-17.108041</td>\n",
-       "      <td>-388.458404</td>\n",
-       "      <td>89.000707</td>\n",
-       "      <td>-12.661459</td>\n",
-       "      <td>477.031349</td>\n",
-       "      <td>257.966790</td>\n",
-       "      <td>-213.280891</td>\n",
-       "      <td>31.256434</td>\n",
-       "      <td>-58.448850</td>\n",
-       "      <td>78.761240</td>\n",
-       "      <td>53.645147</td>\n",
-       "      <td>-22.198802</td>\n",
-       "      <td>-12.391969</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10.249827</th>\n",
-       "      <td>-290.923382</td>\n",
-       "      <td>337.260446</td>\n",
-       "      <td>37.518064</td>\n",
-       "      <td>-60.051166</td>\n",
-       "      <td>-26.686604</td>\n",
-       "      <td>134.936136</td>\n",
-       "      <td>-17.022194</td>\n",
-       "      <td>-388.997470</td>\n",
-       "      <td>88.537380</td>\n",
-       "      <td>-12.971603</td>\n",
-       "      <td>477.791860</td>\n",
-       "      <td>258.523025</td>\n",
-       "      <td>-213.405740</td>\n",
-       "      <td>31.254958</td>\n",
-       "      <td>-58.441682</td>\n",
-       "      <td>78.761230</td>\n",
-       "      <td>53.663357</td>\n",
-       "      <td>-22.191071</td>\n",
-       "      <td>-12.383205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10.482412</th>\n",
-       "      <td>-290.958537</td>\n",
-       "      <td>337.377455</td>\n",
-       "      <td>37.587122</td>\n",
-       "      <td>-60.158256</td>\n",
-       "      <td>-26.750044</td>\n",
-       "      <td>134.964477</td>\n",
-       "      <td>-16.954081</td>\n",
-       "      <td>-389.423414</td>\n",
-       "      <td>88.164178</td>\n",
-       "      <td>-13.219329</td>\n",
-       "      <td>478.398404</td>\n",
-       "      <td>258.967059</td>\n",
-       "      <td>-213.505412</td>\n",
-       "      <td>31.253747</td>\n",
-       "      <td>-58.435983</td>\n",
-       "      <td>78.761230</td>\n",
-       "      <td>53.677759</td>\n",
-       "      <td>-22.184893</td>\n",
-       "      <td>-12.376191</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10.714996</th>\n",
-       "      <td>-290.986528</td>\n",
-       "      <td>337.470648</td>\n",
-       "      <td>37.642077</td>\n",
-       "      <td>-60.243522</td>\n",
-       "      <td>-26.800522</td>\n",
-       "      <td>134.987027</td>\n",
-       "      <td>-16.900054</td>\n",
-       "      <td>-389.760135</td>\n",
-       "      <td>87.864551</td>\n",
-       "      <td>-13.416889</td>\n",
-       "      <td>478.881540</td>\n",
-       "      <td>259.321007</td>\n",
-       "      <td>-213.584869</td>\n",
-       "      <td>31.252760</td>\n",
-       "      <td>-58.431454</td>\n",
-       "      <td>78.761235</td>\n",
-       "      <td>53.689152</td>\n",
-       "      <td>-22.179964</td>\n",
-       "      <td>-12.370587</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 19 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d8b345ec",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:21.517091Z",
+          "iopub.status.busy": "2024-06-04T23:19:21.517008Z",
+          "iopub.status.idle": "2024-06-04T23:19:22.178273Z",
+          "shell.execute_reply": "2024-06-04T23:19:22.178018Z"
+        },
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+        "outputId": "6165f494-510f-408a-e4ea-2fd2ec0a57aa"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "('Assists',\n",
+              " 'AtBat',\n",
+              " 'CAtBat',\n",
+              " 'CHits',\n",
+              " 'CHmRun',\n",
+              " 'CRBI',\n",
+              " 'CRuns',\n",
+              " 'CWalks',\n",
+              " 'Division',\n",
+              " 'Errors',\n",
+              " 'Hits',\n",
+              " 'HmRun',\n",
+              " 'League',\n",
+              " 'NewLeague',\n",
+              " 'PutOuts',\n",
+              " 'RBI',\n",
+              " 'Runs',\n",
+              " 'Walks',\n",
+              " 'Years')"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "                           AtBat        Hits      HmRun       Runs        RBI  \\\n",
-       "negative log(lambda)                                                            \n",
-       "-12.310855              0.000800    0.000889   0.000695   0.000851   0.000911   \n",
-       "-12.078271              0.001010    0.001122   0.000878   0.001074   0.001150   \n",
-       "-11.845686              0.001274    0.001416   0.001107   0.001355   0.001451   \n",
-       "-11.613102              0.001608    0.001787   0.001397   0.001710   0.001831   \n",
-       "-11.380518              0.002029    0.002255   0.001763   0.002158   0.002310   \n",
-       "...                          ...         ...        ...        ...        ...   \n",
-       " 9.784658            -290.823989  336.929968  37.322686 -59.748520 -26.507086   \n",
-       " 10.017243           -290.879272  337.113713  37.431373 -59.916820 -26.606957   \n",
-       " 10.249827           -290.923382  337.260446  37.518064 -60.051166 -26.686604   \n",
-       " 10.482412           -290.958537  337.377455  37.587122 -60.158256 -26.750044   \n",
-       " 10.714996           -290.986528  337.470648  37.642077 -60.243522 -26.800522   \n",
-       "\n",
-       "                           Walks      Years      CAtBat      CHits     CHmRun  \\\n",
-       "negative log(lambda)                                                            \n",
-       "-12.310855              0.000900   0.000812    0.001067   0.001113   0.001064   \n",
-       "-12.078271              0.001135   0.001025    0.001346   0.001404   0.001343   \n",
-       "-11.845686              0.001433   0.001293    0.001698   0.001772   0.001694   \n",
-       "-11.613102              0.001808   0.001632    0.002143   0.002236   0.002138   \n",
-       "-11.380518              0.002281   0.002059    0.002704   0.002821   0.002698   \n",
-       "...                          ...        ...         ...        ...        ...   \n",
-       " 9.784658             134.855915 -17.216195 -387.775826  89.573601 -12.273926   \n",
-       " 10.017243            134.900549 -17.108041 -388.458404  89.000707 -12.661459   \n",
-       " 10.249827            134.936136 -17.022194 -388.997470  88.537380 -12.971603   \n",
-       " 10.482412            134.964477 -16.954081 -389.423414  88.164178 -13.219329   \n",
-       " 10.714996            134.987027 -16.900054 -389.760135  87.864551 -13.416889   \n",
-       "\n",
-       "                           CRuns        CRBI      CWalks  League[N]  \\\n",
-       "negative log(lambda)                                                  \n",
-       "-12.310855              0.001141    0.001149    0.000993  -0.000029   \n",
-       "-12.078271              0.001439    0.001450    0.001253  -0.000037   \n",
-       "-11.845686              0.001816    0.001830    0.001581  -0.000046   \n",
-       "-11.613102              0.002292    0.002309    0.001995  -0.000058   \n",
-       "-11.380518              0.002892    0.002914    0.002517  -0.000073   \n",
-       "...                          ...         ...         ...        ...   \n",
-       " 9.784658             476.079273  257.271255 -213.124780  31.258215   \n",
-       " 10.017243            477.031349  257.966790 -213.280891  31.256434   \n",
-       " 10.249827            477.791860  258.523025 -213.405740  31.254958   \n",
-       " 10.482412            478.398404  258.967059 -213.505412  31.253747   \n",
-       " 10.714996            478.881540  259.321007 -213.584869  31.252760   \n",
-       "\n",
-       "                      Division[W]    PutOuts    Assists     Errors  \\\n",
-       "negative log(lambda)                                                 \n",
-       "-12.310855              -0.000390   0.000609   0.000052  -0.000011   \n",
-       "-12.078271              -0.000492   0.000769   0.000065  -0.000014   \n",
-       "-11.845686              -0.000621   0.000970   0.000082  -0.000017   \n",
-       "-11.613102              -0.000784   0.001224   0.000104  -0.000022   \n",
-       "-11.380518              -0.000990   0.001544   0.000131  -0.000028   \n",
-       "...                           ...        ...        ...        ...   \n",
-       " 9.784658              -58.457857  78.761266  53.622113 -22.208456   \n",
-       " 10.017243             -58.448850  78.761240  53.645147 -22.198802   \n",
-       " 10.249827             -58.441682  78.761230  53.663357 -22.191071   \n",
-       " 10.482412             -58.435983  78.761230  53.677759 -22.184893   \n",
-       " 10.714996             -58.431454  78.761235  53.689152 -22.179964   \n",
-       "\n",
-       "                      NewLeague[N]  \n",
-       "negative log(lambda)                \n",
-       "-12.310855               -0.000006  \n",
-       "-12.078271               -0.000007  \n",
-       "-11.845686               -0.000009  \n",
-       "-11.613102               -0.000012  \n",
-       "-11.380518               -0.000015  \n",
-       "...                            ...  \n",
-       " 9.784658               -12.402891  \n",
-       " 10.017243              -12.391969  \n",
-       " 10.249827              -12.383205  \n",
-       " 10.482412              -12.376191  \n",
-       " 10.714996              -12.370587  \n",
-       "\n",
-       "[100 rows x 19 columns]"
+      "source": [
+        "hitters_MSE = sklearn_selected(OLS,\n",
+        "                               strategy)\n",
+        "hitters_MSE.fit(Hitters, Y)\n",
+        "hitters_MSE.selected_state_\n"
       ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "soln_path = pd.DataFrame(soln_array.T,\n",
-    "                         columns=D.columns,\n",
-    "                         index=-np.log(lambdas))\n",
-    "soln_path.index.name = 'negative log(lambda)'\n",
-    "soln_path\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2246bb2b",
-   "metadata": {},
-   "source": [
-    "We plot the paths to get a sense of how the coefficients vary with $\\lambda$.\n",
-    "To control the location of the legend we first set `legend` to `False` in the\n",
-    "plot method, adding it afterward with the `legend()` method of `ax`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "08bc997b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.312975Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.312908Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.473880Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.473585Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "c1a78607",
+      "metadata": {
+        "id": "c1a78607"
+      },
+      "source": [
+        "Using `neg_Cp` results in a smaller model, as expected, with just 10 variables selected."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "path_fig, ax = subplots(figsize=(8,8))\n",
-    "soln_path.plot(ax=ax, legend=False)\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Standardized coefficients', fontsize=20)\n",
-    "ax.legend(loc='upper left');\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae73ca09",
-   "metadata": {},
-   "source": [
-    "(We have used `latex` formatting in the horizontal label, in order to format the Greek $\\lambda$ appropriately.) \n",
-    "We expect the coefficient estimates to be much smaller, in terms of\n",
-    "$\\ell_2$ norm, when a large value of $\\lambda$ is used, as compared to\n",
-    "when a small value of $\\lambda$ is used. (Recall that the  $\\ell_2$ norm is the square root of the sum of squared coefficient values.) We display  the coefficients at the $40$th step,\n",
-    "where $\\lambda$ is 25.535."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "0f7359c7",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.475363Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.475242Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.477806Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.477534Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(25.53538897200662,\n",
-       " AtBat           5.433750\n",
-       " Hits            6.223582\n",
-       " HmRun           4.585498\n",
-       " Runs            5.880855\n",
-       " RBI             6.195921\n",
-       " Walks           6.277975\n",
-       " Years           5.299767\n",
-       " CAtBat          7.147501\n",
-       " CHits           7.539495\n",
-       " CHmRun          7.182344\n",
-       " CRuns           7.728649\n",
-       " CRBI            7.790702\n",
-       " CWalks          6.592901\n",
-       " League[N]       0.042445\n",
-       " Division[W]    -3.107159\n",
-       " PutOuts         4.605263\n",
-       " Assists         0.378371\n",
-       " Errors         -0.135196\n",
-       " NewLeague[N]    0.150323\n",
-       " Name: -3.240065292879872, dtype: float64)"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "915cdff6",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:22.179650Z",
+          "iopub.status.busy": "2024-06-04T23:19:22.179571Z",
+          "iopub.status.idle": "2024-06-04T23:19:22.573219Z",
+          "shell.execute_reply": "2024-06-04T23:19:22.572980Z"
+        },
+        "id": "915cdff6",
+        "outputId": "b5e9d80d-e824-4895-926e-edc8e23f7a9d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "('Assists',\n",
+              " 'AtBat',\n",
+              " 'CAtBat',\n",
+              " 'CRBI',\n",
+              " 'CRuns',\n",
+              " 'CWalks',\n",
+              " 'Division',\n",
+              " 'Hits',\n",
+              " 'PutOuts',\n",
+              " 'Walks')"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "hitters_Cp = sklearn_selected(OLS,\n",
+        "                               strategy,\n",
+        "                               scoring=neg_Cp)\n",
+        "hitters_Cp.fit(Hitters, Y)\n",
+        "hitters_Cp.selected_state_\n"
       ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "beta_hat = soln_path.loc[soln_path.index[39]]\n",
-    "lambdas[39], beta_hat\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9baa8b00",
-   "metadata": {},
-   "source": [
-    "Let’s compute the $\\ell_2$ norm of the standardized coefficients."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "d2984305",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.479160Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.479064Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.481153Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.480921Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "24.17061720144378"
+      "cell_type": "markdown",
+      "id": "f5d37f87",
+      "metadata": {
+        "id": "f5d37f87"
+      },
+      "source": [
+        "### Choosing Among Models Using the Validation Set Approach and Cross-Validation\n",
+        "\n",
+        "As an  alternative to using $C_p$, we might try cross-validation to select a model in forward selection. For this, we need a\n",
+        "method that stores the full path of models found in forward selection, and allows predictions for each of these. This can be done with the `sklearn_selection_path()`\n",
+        "estimator from `ISLP.models`. The function `cross_val_predict()` from `ISLP.models`\n",
+        "computes the cross-validated predictions for each of the models\n",
+        "along the path, which we can use to evaluate the cross-validated MSE\n",
+        "along the path."
       ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.linalg.norm(beta_hat)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5111fc6f",
-   "metadata": {},
-   "source": [
-    "In contrast, here is the $\\ell_2$ norm when $\\lambda$ is 2.44e-01.\n",
-    "Note the much larger $\\ell_2$ norm of the\n",
-    "coefficients associated with this smaller value of $\\lambda$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "9a3424a4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.482508Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.482402Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.484554Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.484320Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(0.24374766133488554, 160.42371017725839)"
+      "cell_type": "markdown",
+      "id": "6769115d",
+      "metadata": {
+        "id": "6769115d"
+      },
+      "source": [
+        "Here we define a strategy that fits the full forward selection path.\n",
+        "While there are various parameter choices for `sklearn_selection_path()`,\n",
+        "we use the defaults here, which selects the model at each step based on the biggest reduction  in RSS."
       ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "beta_hat = soln_path.loc[soln_path.index[59]]\n",
-    "lambdas[59], np.linalg.norm(beta_hat)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50239927",
-   "metadata": {},
-   "source": [
-    "Above we normalized `X` upfront, and fit the ridge model using `Xs`.\n",
-    "The `Pipeline()`  object\n",
-    "in `sklearn` provides a clear way to separate feature\n",
-    "normalization from the fitting of the ridge model itself."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "c864d25f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.485813Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.485727Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.495072Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.494829Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.24374766133488554, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "33ae4aa4",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:22.574545Z",
+          "iopub.status.busy": "2024-06-04T23:19:22.574468Z",
+          "iopub.status.idle": "2024-06-04T23:19:22.576122Z",
+          "shell.execute_reply": "2024-06-04T23:19:22.575907Z"
+        },
+        "id": "33ae4aa4"
+      },
+      "outputs": [],
+      "source": [
+        "strategy = Stepwise.fixed_steps(design,\n",
+        "                                len(design.terms),\n",
+        "                                direction='forward')\n",
+        "full_path = sklearn_selection_path(OLS, strategy)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d397415c",
+      "metadata": {
+        "id": "d397415c"
+      },
+      "source": [
+        "We now fit the full forward-selection path on the `Hitters` data and compute the fitted values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f05e1a81",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:22.577302Z",
+          "iopub.status.busy": "2024-06-04T23:19:22.577233Z",
+          "iopub.status.idle": "2024-06-04T23:19:22.976283Z",
+          "shell.execute_reply": "2024-06-04T23:19:22.976065Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "f05e1a81",
+        "outputId": "c0ad0566-b3f6-40c9-bbcc-40ac21b3a1d3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(263, 20)"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('ridge', ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])"
+      "source": [
+        "full_path.fit(Hitters, Y)\n",
+        "Yhat_in = full_path.predict(Hitters)\n",
+        "Yhat_in.shape\n"
       ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ridge = skl.ElasticNet(alpha=lambdas[59], l1_ratio=0)\n",
-    "scaler = StandardScaler(with_mean=True,  with_std=True)\n",
-    "pipe = Pipeline(steps=[('scaler', scaler), ('ridge', ridge)])\n",
-    "pipe.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71c8c514",
-   "metadata": {},
-   "source": [
-    "We show that it gives the same $\\ell_2$ norm as in our previous fit on the standardized data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "8a07da6e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.496384Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.496277Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.498333Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.498078Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "160.4237101772591"
+      "cell_type": "markdown",
+      "id": "767af527",
+      "metadata": {
+        "id": "767af527"
+      },
+      "source": [
+        "This gives us an array of fitted values --- 20 steps in all, including the fitted mean for the null model --- which we can use to evaluate\n",
+        "in-sample MSE. As expected, the in-sample MSE improves each step we take,\n",
+        "indicating we must use either the validation or cross-validation\n",
+        "approach to select the number of steps. We fix the y-axis to range from\n",
+        "50,000 to 250,000 to compare to the cross-validation and validation\n",
+        "set MSE below, as well as other methods such as ridge regression, lasso and\n",
+        "principal components regression."
       ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.linalg.norm(ridge.coef_)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6a20ea7e",
-   "metadata": {},
-   "source": [
-    " Notice that the operation `pipe.fit(X, Y)` above has changed the `ridge` object, and in particular has added attributes such as `coef_` that were not there before. \n",
-    "### Estimating Test Error of Ridge Regression\n",
-    "Choosing an *a priori* value of $\\lambda$ for ridge regression is\n",
-    "difficult if not impossible. We will want to use the validation method\n",
-    "or cross-validation to select the tuning parameter. The reader may not\n",
-    "be surprised that the  `Pipeline()` approach can be used in\n",
-    "`skm.cross_validate()` with either a validation method\n",
-    "(i.e. `validation`) or $k$-fold cross-validation.\n",
-    "\n",
-    "We fix the random state of the splitter\n",
-    "so that the results obtained will be reproducible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "c10e3ae5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.499658Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.499553Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.505630Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.505369Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([134214.00419204])"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0b07ae72",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:22.977573Z",
+          "iopub.status.busy": "2024-06-04T23:19:22.977503Z",
+          "iopub.status.idle": "2024-06-04T23:19:23.094004Z",
+          "shell.execute_reply": "2024-06-04T23:19:23.091687Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "0b07ae72",
+        "outputId": "d1316a68-e99a-49f5-f101-c23a032ceb63"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "mse_fig, ax = subplots(figsize=(8,8))\n",
+        "insample_mse = ((Yhat_in - Y[:,None])**2).mean(0)\n",
+        "n_steps = insample_mse.shape[0]\n",
+        "ax.plot(np.arange(n_steps),\n",
+        "        insample_mse,\n",
+        "        'k', # color black\n",
+        "        label='In-sample')\n",
+        "ax.set_ylabel('MSE',\n",
+        "              fontsize=20)\n",
+        "ax.set_xlabel('# steps of forward stepwise',\n",
+        "              fontsize=20)\n",
+        "ax.set_xticks(np.arange(n_steps)[::2])\n",
+        "ax.legend()\n",
+        "ax.set_ylim([50000,250000]);\n"
       ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "validation = skm.ShuffleSplit(n_splits=1,\n",
-    "                              test_size=0.5,\n",
-    "                              random_state=0)\n",
-    "ridge.alpha = 0.01\n",
-    "results = skm.cross_validate(ridge,\n",
-    "                             X,\n",
-    "                             Y,\n",
-    "                             scoring='neg_mean_squared_error',\n",
-    "                             cv=validation)\n",
-    "-results['test_score']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c5f962ee",
-   "metadata": {},
-   "source": [
-    "The test MSE is 1.342e+05.  Note\n",
-    "that if we had instead simply fit a model with just an intercept, we\n",
-    "would have predicted each test observation using the mean of the\n",
-    "training observations. We can get the same result by fitting a ridge regression model\n",
-    "with a *very* large value of $\\lambda$. Note that `1e10`\n",
-    "means $10^{10}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "0df2b24d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.507058Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.506964Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.512846Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.512640Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([231788.32155285])"
+      "cell_type": "markdown",
+      "id": "5f7508e0",
+      "metadata": {
+        "id": "5f7508e0"
+      },
+      "source": [
+        "Notice the expression `None` in `Y[:,None]` above.\n",
+        "This adds an axis (dimension) to the one-dimensional array `Y`,\n",
+        "which allows it to be recycled when subtracted from the two-dimensional `Yhat_in`."
       ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ridge.alpha = 1e10\n",
-    "results = skm.cross_validate(ridge,\n",
-    "                             X,\n",
-    "                             Y,\n",
-    "                             scoring='neg_mean_squared_error',\n",
-    "                             cv=validation)\n",
-    "-results['test_score']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e78a776c",
-   "metadata": {},
-   "source": [
-    "Obviously choosing $\\lambda=0.01$ is arbitrary,  so we will  use cross-validation or the validation-set\n",
-    "approach to choose the tuning parameter $\\lambda$.\n",
-    "The object `GridSearchCV()`  allows exhaustive\n",
-    "grid search to choose such a parameter.\n",
-    "\n",
-    "We first use the validation set method\n",
-    "to choose $\\lambda$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "9b76df85",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.514070Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.514003Z",
-     "iopub.status.idle": "2024-06-04T23:19:27.851311Z",
-     "shell.execute_reply": "2024-06-04T23:19:27.851092Z"
-    }
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f6c9531a",
+      "metadata": {
+        "id": "f6c9531a"
+      },
+      "source": [
+        "We are now ready to use cross-validation to estimate test error along\n",
+        "the model path. We must use *only the training observations* to perform all aspects of model-fitting --- including\n",
+        "variable selection.  Therefore, the determination of which model of a\n",
+        "given size is best must be made using \\emph{only the training\n",
+        "  observations} in each training fold. This point is subtle but important.  If the full data\n",
+        "set is used to select the best subset at each step, then the validation\n",
+        "set errors and cross-validation errors that we obtain will not be\n",
+        "accurate estimates of the test error.\n",
+        "\n",
+        "We now compute the cross-validated predicted values using 5-fold cross-validation."
+      ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.005899006046740856, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a1654ad8",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:23.097519Z",
+          "iopub.status.busy": "2024-06-04T23:19:23.097271Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.139415Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.139180Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "a1654ad8",
+        "outputId": "15893cc5-1d2a-4714-f310-91e8385fef21"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(263, 20)"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('ridge', ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])"
+      "source": [
+        "K = 5\n",
+        "kfold = skm.KFold(K,\n",
+        "                  random_state=0,\n",
+        "                  shuffle=True)\n",
+        "Yhat_cv = skm.cross_val_predict(full_path,\n",
+        "                                Hitters,\n",
+        "                                Y,\n",
+        "                                cv=kfold)\n",
+        "Yhat_cv.shape\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "98dfabdc",
+      "metadata": {
+        "id": "98dfabdc"
+      },
+      "source": [
+        "`skm.cross_val_predict()`\n",
+        "The prediction matrix `Yhat_cv` is the same shape as `Yhat_in`; the difference is that the predictions in each row, corresponding to a particular sample index, were made from models fit on a training fold that did not include that row.\n",
+        "\n",
+        "At each model along the path, we compute the MSE in each of the cross-validation folds.\n",
+        "These we will average to get the  mean MSE, and can also use the individual values to compute a crude estimate of the standard error of the mean. {The estimate is crude because the five error estimates  are based on overlapping training sets, and hence are not independent.}\n",
+        "Hence we must know the test indices for each cross-validation\n",
+        "split. This can be found by using the `split()` method of `kfold`. Because\n",
+        "we  fixed the random state above, whenever we split any array with the same\n",
+        "number of rows as $Y$ we recover the same training and test indices, though we simply\n",
+        "ignore the training indices below."
       ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param_grid = {'ridge__alpha': lambdas}\n",
-    "grid = skm.GridSearchCV(pipe,\n",
-    "                        param_grid,\n",
-    "                        cv=validation,\n",
-    "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)\n",
-    "grid.best_params_['ridge__alpha']\n",
-    "grid.best_estimator_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9befd23e",
-   "metadata": {},
-   "source": [
-    "Alternatively, we can use 5-fold cross-validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "3e4c1b53",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:27.852563Z",
-     "iopub.status.busy": "2024-06-04T23:19:27.852495Z",
-     "iopub.status.idle": "2024-06-04T23:19:30.080670Z",
-     "shell.execute_reply": "2024-06-04T23:19:30.080412Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-3 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.01185247763144249, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ce919bd3",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.140773Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.140698Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.143330Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.143113Z"
+        },
+        "id": "ce919bd3",
+        "outputId": "5de1a2ad-412c-4217-d8eb-cd26524c3741"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(20, 5)"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('ridge', ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])"
+      "source": [
+        "cv_mse = []\n",
+        "for train_idx, test_idx in kfold.split(Y):\n",
+        "    errors = (Yhat_cv[test_idx] - Y[test_idx,None])**2\n",
+        "    cv_mse.append(errors.mean(0)) # column means\n",
+        "cv_mse = np.array(cv_mse).T\n",
+        "cv_mse.shape\n"
       ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grid = skm.GridSearchCV(pipe, \n",
-    "                        param_grid,\n",
-    "                        cv=kfold,\n",
-    "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)\n",
-    "grid.best_params_['ridge__alpha']\n",
-    "grid.best_estimator_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cae429da",
-   "metadata": {},
-   "source": [
-    "Recall we set up the `kfold` object for 5-fold cross-validation on page~\\pageref{line:choos-among-models}. We now plot the cross-validated MSE as a function of $-\\log(\\lambda)$, which has shrinkage decreasing from left\n",
-    "to right."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "39ff3251",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:30.082088Z",
-     "iopub.status.busy": "2024-06-04T23:19:30.082012Z",
-     "iopub.status.idle": "2024-06-04T23:19:30.194197Z",
-     "shell.execute_reply": "2024-06-04T23:19:30.193921Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "a27004be",
+      "metadata": {
+        "id": "a27004be"
+      },
+      "source": [
+        "We now add the cross-validation error estimates to our MSE plot.\n",
+        "We include the mean error across the five folds, and the estimate of the standard error of the mean."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ridge_fig, ax = subplots(figsize=(8,8))\n",
-    "ax.errorbar(-np.log(lambdas),\n",
-    "            -grid.cv_results_['mean_test_score'],\n",
-    "            yerr=grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
-    "ax.set_ylim([50000,250000])\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "854e0c21",
-   "metadata": {},
-   "source": [
-    "One can cross-validate different metrics to choose a parameter. The default\n",
-    "metric for `skl.ElasticNet()` is test $R^2$.\n",
-    "Let’s compare $R^2$ to MSE for cross-validation here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "766ab0b0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:30.195678Z",
-     "iopub.status.busy": "2024-06-04T23:19:30.195576Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.425386Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.425155Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-4 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-4 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-4 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-4 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-4 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                                       (&#x27;ridge&#x27;,\n",
-       "                                        ElasticNet(alpha=10000000000.0,\n",
-       "                                                   l1_ratio=0))]),\n",
-       "             param_grid={&#x27;ridge__alpha&#x27;: array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606...\n",
-       "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                                       (&#x27;ridge&#x27;,\n",
-       "                                        ElasticNet(alpha=10000000000.0,\n",
-       "                                                   l1_ratio=0))]),\n",
-       "             param_grid={&#x27;ridge__alpha&#x27;: array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606...\n",
-       "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.01185247763144249, l1_ratio=0)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "26520d22",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.144503Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.144435Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.201431Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.201214Z"
+        },
+        "id": "26520d22",
+        "outputId": "723e53b0-e36b-42e6-8f40-c51d45fc08fa"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                                       ('ridge',\n",
-       "                                        ElasticNet(alpha=10000000000.0,\n",
-       "                                                   l1_ratio=0))]),\n",
-       "             param_grid={'ridge__alpha': array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606...\n",
-       "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})"
+      "source": [
+        "ax.errorbar(np.arange(n_steps),\n",
+        "            cv_mse.mean(1),\n",
+        "            cv_mse.std(1) / np.sqrt(K),\n",
+        "            label='Cross-validated',\n",
+        "            c='r') # color red\n",
+        "ax.set_ylim([50000,250000])\n",
+        "ax.legend()\n",
+        "mse_fig\n"
       ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grid_r2 = skm.GridSearchCV(pipe, \n",
-    "                           param_grid,\n",
-    "                           cv=kfold)\n",
-    "grid_r2.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f3887a9c",
-   "metadata": {},
-   "source": [
-    "Finally, let’s plot the results for cross-validated $R^2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "642f1de3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.426644Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.426570Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.492503Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.492264Z"
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "9881566e",
+      "metadata": {
+        "id": "9881566e"
+      },
+      "source": [
+        "To repeat the above using the validation set approach, we simply change our\n",
+        "`cv` argument to a validation set: one random split of the data into a test and training. We choose a test size\n",
+        "of 20%, similar to the size of each test set in 5-fold cross-validation.`skm.ShuffleSplit()`"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "r2_fig, ax = subplots(figsize=(8,8))\n",
-    "ax.errorbar(-np.log(lambdas),\n",
-    "            grid_r2.cv_results_['mean_test_score'],\n",
-    "            yerr=grid_r2.cv_results_['std_test_score'] / np.sqrt(K))\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated $R^2$', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0cb6f97b",
-   "metadata": {},
-   "source": [
-    "### Fast Cross-Validation for Solution Paths\n",
-    "The ridge, lasso, and elastic net can be efficiently fit along a sequence of $\\lambda$ values, creating what is known as a *solution path* or *regularization path*. Hence there is specialized code to fit\n",
-    "such paths, and to choose a suitable value of $\\lambda$ using cross-validation. Even with\n",
-    "identical splits the results will not agree *exactly* with our `grid`\n",
-    "above because the standardization of each feature  in `grid` is carried out on each fold,\n",
-    "while in `pipeCV` below it is carried out only once.\n",
-    "Nevertheless, the results are similar as the normalization\n",
-    "is relatively stable across folds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "016ffe7c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.493853Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.493772Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.763066Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.762828Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-5 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-5 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-5 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-5 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-5 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;,\n",
-       "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
-       "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
-       "       5.37495461e+03, 4.25955961e+03,...\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
-       "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "                              l1_ratio=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                (&#x27;ridge&#x27;,\n",
-       "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
-       "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
-       "       5.37495461e+03, 4.25955961e+03,...\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
-       "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "                              l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNetCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNetCV.html\">?<span>Documentation for ElasticNetCV</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
-       "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
-       "       5.37495461e+03, 4.25955961e+03, 3.37562814e+03, 2.67512757e+03,\n",
-       "       2.11999285e+03, 1.680058...\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
-       "             cv=KFold(n_splits=5, random_state=0, shuffle=True), l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f19c9091",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.202701Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.202634Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.603532Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.603301Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "f19c9091"
+      },
+      "outputs": [],
+      "source": [
+        "validation = skm.ShuffleSplit(n_splits=1,\n",
+        "                              test_size=0.2,\n",
+        "                              random_state=0)\n",
+        "for train_idx, test_idx in validation.split(Y):\n",
+        "    full_path.fit(Hitters.iloc[train_idx],\n",
+        "                  Y[train_idx])\n",
+        "    Yhat_val = full_path.predict(Hitters.iloc[test_idx])\n",
+        "    errors = (Yhat_val - Y[test_idx,None])**2\n",
+        "    validation_mse = errors.mean(0)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7421fc11",
+      "metadata": {
+        "id": "7421fc11"
+      },
+      "source": [
+        " As for the in-sample MSE case, the validation set approach does not provide standard errors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1fca6b08",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.604861Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.604789Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.664722Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.664495Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "1fca6b08",
+        "outputId": "442fbcef-a7cc-4e3a-fc84-f37a70c41034"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                ('ridge',\n",
-       "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
-       "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
-       "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
-       "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
-       "       5.37495461e+03, 4.25955961e+03,...\n",
-       "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
-       "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
-       "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
-       "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
-       "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
-       "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "                              l1_ratio=0))])"
+      "source": [
+        "ax.plot(np.arange(n_steps),\n",
+        "        validation_mse,\n",
+        "        'b--', # color blue, broken line\n",
+        "        label='Validation')\n",
+        "ax.set_xticks(np.arange(n_steps)[::2])\n",
+        "ax.set_ylim([50000,250000])\n",
+        "ax.legend()\n",
+        "mse_fig\n"
       ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ridgeCV = skl.ElasticNetCV(alphas=lambdas, \n",
-    "                           l1_ratio=0,\n",
-    "                           cv=kfold)\n",
-    "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
-    "                         ('ridge', ridgeCV)])\n",
-    "pipeCV.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6845efe",
-   "metadata": {},
-   "source": [
-    "Let’s produce a plot again of the cross-validation error to see that\n",
-    "it is similar to using `skm.GridSearchCV`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "52f6cf29",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.764408Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.764316Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.836546Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.836313Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "b4df366a",
+      "metadata": {
+        "id": "b4df366a"
+      },
+      "source": [
+        "### Best Subset Selection\n",
+        "Forward stepwise is a *greedy* selection procedure; at each step it augments the current set by including one additional variable.  We now apply best subset selection  to the  `Hitters`\n",
+        "data, which for every subset size, searches for the best set of predictors.  \n",
+        "\n",
+        "We will use a package called `l0bnb` to perform\n",
+        "best subset selection.\n",
+        "Instead of constraining the subset to be a given size,\n",
+        "this package produces a path of solutions using the subset size as a\n",
+        "penalty rather than a constraint. Although the distinction is subtle, the difference comes when we cross-validate.\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tuned_ridge = pipeCV.named_steps['ridge']\n",
-    "ridgeCV_fig, ax = subplots(figsize=(8,8))\n",
-    "ax.errorbar(-np.log(lambdas),\n",
-    "            tuned_ridge.mse_path_.mean(1),\n",
-    "            yerr=tuned_ridge.mse_path_.std(1) / np.sqrt(K))\n",
-    "ax.axvline(-np.log(tuned_ridge.alpha_), c='k', ls='--')\n",
-    "ax.set_ylim([50000,250000])\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80e07995",
-   "metadata": {},
-   "source": [
-    "We see that the value of $\\lambda$ that results in the\n",
-    "smallest cross-validation error is 1.19e-02, available\n",
-    "as the value `tuned_ridge.alpha_`. What is the test MSE\n",
-    "associated with this value of $\\lambda$?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "2b49c3d7",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.838011Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.837899Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.840000Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.839740Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "115526.70630987917"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6e9dd7d4",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.665992Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.665901Z",
+          "iopub.status.idle": "2024-06-04T23:19:25.679390Z",
+          "shell.execute_reply": "2024-06-04T23:19:25.679171Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "6e9dd7d4"
+      },
+      "outputs": [],
+      "source": [
+        "D = design.fit_transform(Hitters)\n",
+        "D = D.drop('intercept', axis=1)\n",
+        "X = np.asarray(D)\n"
       ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.min(tuned_ridge.mse_path_.mean(1))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0961957c",
-   "metadata": {},
-   "source": [
-    "This represents a further improvement over the test MSE that we got\n",
-    "using $\\lambda=4$.  Finally, `tuned_ridge.coef_`\n",
-    "has the coefficients fit on the entire data set\n",
-    "at this value of  $\\lambda$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "973e7e79",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.841440Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.841345Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.843427Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.843185Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([-222.80877051,  238.77246614,    3.21103754,   -2.93050845,\n",
-       "          3.64888723,  108.90953869,  -50.81896152, -105.15731984,\n",
-       "        122.00714801,   57.1859509 ,  210.35170348,  118.05683748,\n",
-       "       -150.21959435,   30.36634231,  -61.62459095,   77.73832472,\n",
-       "         40.07350744,  -25.02151514,  -13.68429544])"
+      "cell_type": "markdown",
+      "id": "4b87bd74",
+      "metadata": {
+        "id": "4b87bd74"
+      },
+      "source": [
+        "Here we excluded the first column corresponding to the intercept, as\n",
+        "`l0bnb` will fit the intercept separately. We can find a path using the `fit_path()` function."
       ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tuned_ridge.coef_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b19ffa3f",
-   "metadata": {},
-   "source": [
-    "As expected, none of the coefficients are zero—ridge regression does\n",
-    "not perform variable selection!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bfb6c72e",
-   "metadata": {},
-   "source": [
-    "### Evaluating Test Error of Cross-Validated Ridge\n",
-    "Choosing $\\lambda$ using cross-validation provides a single regression\n",
-    "estimator, similar to fitting a linear regression model as we saw in\n",
-    "Chapter~\\ref{Ch3:linreg}. It is therefore reasonable to estimate what its test error\n",
-    "is. We run into a problem here in that cross-validation will have\n",
-    "*touched* all of its data in choosing $\\lambda$, hence we have no\n",
-    "further data to estimate test error. A compromise is to do an initial\n",
-    "split of the data into two disjoint sets: a training set and a test set.\n",
-    "We then fit a cross-validation\n",
-    "tuned ridge regression on the training set, and evaluate its performance on the test set.\n",
-    "We might call this cross-validation nested\n",
-    "within the validation set approach. A priori there is no reason to use\n",
-    "half of the data for each of the two sets in validation. Below, we use\n",
-    "75% for training and 25% for test, with the estimator being ridge\n",
-    "regression tuned using 5-fold cross-validation.  This can be achieved\n",
-    "in code as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "a9140b3a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.844819Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.844727Z",
-     "iopub.status.idle": "2024-06-04T23:19:32.846512Z",
-     "shell.execute_reply": "2024-06-04T23:19:32.846267Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "outer_valid = skm.ShuffleSplit(n_splits=1, \n",
-    "                               test_size=0.25,\n",
-    "                               random_state=1)\n",
-    "inner_cv = skm.KFold(n_splits=5,\n",
-    "                     shuffle=True,\n",
-    "                     random_state=2)\n",
-    "ridgeCV = skl.ElasticNetCV(alphas=lambdas,\n",
-    "                           l1_ratio=0,\n",
-    "                           cv=inner_cv)\n",
-    "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
-    "                         ('ridge', ridgeCV)]);\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "8fc4aea0",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:32.847712Z",
-     "iopub.status.busy": "2024-06-04T23:19:32.847645Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.096196Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.095971Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([132393.84003227])"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "caed84ae",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:25.680677Z",
+          "iopub.status.busy": "2024-06-04T23:19:25.680609Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.246130Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.245877Z"
+        },
+        "id": "caed84ae",
+        "outputId": "62e09d79-a6c0-44f7-a261-e2064a356279"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Preprocessing Data.\n",
+            "BnB Started.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Iteration: 1. Number of non-zeros:  1\n",
+            "Iteration: 2. Number of non-zeros:  2\n",
+            "Iteration: 3. Number of non-zeros:  2\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Iteration: 4. Number of non-zeros:  2\n",
+            "Iteration: 5. Number of non-zeros:  3\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Iteration: 6. Number of non-zeros:  3\n",
+            "Iteration: 7. Number of non-zeros:  4\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Iteration: 8. Number of non-zeros:  9\n",
+            "Iteration: 9. Number of non-zeros:  9\n",
+            "Iteration: 10. Number of non-zeros:  9\n",
+            "Iteration: 11. Number of non-zeros:  9\n",
+            "Iteration: 12. Number of non-zeros:  9\n",
+            "Iteration: 13. Number of non-zeros:  9\n",
+            "Iteration: 14. Number of non-zeros:  9\n",
+            "Iteration: 15. Number of non-zeros:  9\n",
+            "Iteration: 16. Number of non-zeros:  9\n",
+            "Iteration: 17. Number of non-zeros:  9\n",
+            "Iteration: 18. Number of non-zeros:  17\n",
+            "Iteration: 19. Number of non-zeros:  19\n"
+          ]
+        }
+      ],
+      "source": [
+        "path = fit_path(X,\n",
+        "                Y,\n",
+        "                max_nonzeros=X.shape[1])\n"
       ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = skm.cross_validate(pipeCV, \n",
-    "                             X,\n",
-    "                             Y,\n",
-    "                             cv=outer_valid,\n",
-    "                             scoring='neg_mean_squared_error')\n",
-    "-results['test_score']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e27147ec",
-   "metadata": {},
-   "source": [
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4f126e9",
-   "metadata": {},
-   "source": [
-    "### The Lasso\n",
-    "We saw that ridge regression with a wise choice of $\\lambda$ can\n",
-    "outperform least squares as well as the null model on the\n",
-    " `Hitters`  data set. We now ask whether the lasso can yield\n",
-    "either a more accurate or a more interpretable model than ridge\n",
-    "regression. In order to fit a lasso model, we once again use the\n",
-    "`ElasticNetCV()`  function; however, this time we use the argument\n",
-    "`l1_ratio=1`. Other than that change, we proceed just as we did in\n",
-    "fitting a ridge model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "008ecc7c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.097496Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.097429Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.139203Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.138992Z"
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "3.1472370031649866"
+      "cell_type": "markdown",
+      "id": "d4e9ca6a",
+      "metadata": {
+        "id": "d4e9ca6a"
+      },
+      "source": [
+        "The function `fit_path()` returns a list whose values include the fitted coefficients as `B`, an intercept as `B0`, as well as a few other attributes related to the particular path algorithm used. Such details are beyond the scope of this book."
       ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lassoCV = skl.ElasticNetCV(n_alphas=100, \n",
-    "                           l1_ratio=1,\n",
-    "                           cv=kfold)\n",
-    "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
-    "                         ('lasso', lassoCV)])\n",
-    "pipeCV.fit(X, Y)\n",
-    "tuned_lasso = pipeCV.named_steps['lasso']\n",
-    "tuned_lasso.alpha_\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "1b909861",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.140345Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.140279Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.148367Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.148166Z"
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "lambdas, soln_array = skl.Lasso.path(Xs, \n",
-    "                                    Y,\n",
-    "                                    l1_ratio=1,\n",
-    "                                    n_alphas=100)[:2]\n",
-    "soln_path = pd.DataFrame(soln_array.T,\n",
-    "                         columns=D.columns,\n",
-    "                         index=-np.log(lambdas))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "abec44b3",
-   "metadata": {},
-   "source": [
-    "We can see from the coefficient plot of the standardized coefficients that depending on the choice of\n",
-    "tuning parameter, some of the coefficients will be exactly equal to\n",
-    "zero."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "ff719798",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.149542Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.149477Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.268439Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.268204Z"
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f2fc2ef8",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.247604Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.247525Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.249696Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.249497Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "f2fc2ef8",
+        "outputId": "162c7db2-c218-49cd-820b-7ee600069214"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'B': array([0.        , 3.25484367, 0.        , 0.        , 0.        ,\n",
+              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+              "        0.        , 0.67775265, 0.        , 0.        , 0.        ,\n",
+              "        0.        , 0.        , 0.        , 0.        ]),\n",
+              " 'B0': -38.98216739555551,\n",
+              " 'lambda_0': 0.011416248027450178,\n",
+              " 'M': 0.5829861733382012,\n",
+              " 'Time_exceeded': False}"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "path[3]\n"
+      ]
     },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "c4a96126",
+      "metadata": {
+        "id": "c4a96126"
+      },
+      "source": [
+        "In the example above, we see that at the fourth step in the path, we have two nonzero coefficients in `'B'`, corresponding to the value $0.114$ for the penalty parameter `lambda_0`.\n",
+        "We could make predictions using this sequence of fits on a validation set as a function of `lambda_0`, or with more work using cross-validation.\n",
+        "\n",
+        "## Ridge Regression and the Lasso\n",
+        "We will use the `sklearn.linear_model` package (for which\n",
+        "we use `skl` as shorthand below)\n",
+        "to fit ridge and  lasso regularized linear models on the `Hitters` data.\n",
+        "We start with the model matrix `X` (without an intercept) that we computed in the previous section on best subset regression.\n",
+        "\n",
+        "### Ridge Regression\n",
+        "We will use the function `skl.ElasticNet()` to fit both  ridge and the lasso.\n",
+        "To fit a *path* of ridge regressions models, we use\n",
+        "`skl.ElasticNet.path()`, which can fit both ridge and lasso, as well as a hybrid mixture;  ridge regression\n",
+        "corresponds to `l1_ratio=0`.\n",
+        "It is good practice to standardize the columns of `X` in these applications, if the variables are measured in different units. Since `skl.ElasticNet()` does no normalization, we have to take care of that ourselves.\n",
+        "Since we\n",
+        "standardize first, in order to find coefficient\n",
+        "estimates on the original scale, we must *unstandardize*\n",
+        "the coefficient estimates. The parameter\n",
+        "$\\lambda$ in (\\ref{Ch6:ridge}) and (\\ref{Ch6:LASSO}) is called `alphas` in `sklearn`. In order to\n",
+        "be consistent with the rest of this chapter, we use `lambdas`\n",
+        "rather than `alphas` in what follows.  {At the time of publication, ridge fits like the one in code chunk [22] issue unwarranted convergence warning messages; we expect these to disappear as this package matures.}"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "path_fig, ax = subplots(figsize=(8,8))\n",
-    "soln_path.plot(ax=ax, legend=False)\n",
-    "ax.legend(loc='upper left')\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Standardized coefficiients', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "201ccdfb",
-   "metadata": {},
-   "source": [
-    "The smallest cross-validated error is lower than the test set MSE of the null model\n",
-    "and of least squares, and very similar to the test MSE of 115526.71 of ridge\n",
-    "regression (page~\\pageref{page:MSECVRidge}) with $\\lambda$ chosen by cross-validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "f21ad8dc",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.269744Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.269654Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.271587Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.271375Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "114690.73118253727"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "4a5d6fc8",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.250946Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.250878Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.303537Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.303273Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "4a5d6fc8",
+        "outputId": "b4e908d9-abb9-4c7a-ec06-d4cfad725a1d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(19, 100)"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "Xs = X - X.mean(0)[None,:]\n",
+        "X_scale = X.std(0)\n",
+        "Xs = Xs / X_scale[None,:]\n",
+        "lambdas = 10**np.linspace(8, -2, 100) / Y.std()\n",
+        "soln_array = skl.ElasticNet.path(Xs,\n",
+        "                                 Y,\n",
+        "                                 l1_ratio=0.,\n",
+        "                                 alphas=lambdas)[1]\n",
+        "soln_array.shape\n"
       ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.min(tuned_lasso.mse_path_.mean(1))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "136f171f",
-   "metadata": {},
-   "source": [
-    "Let’s again produce a plot of the cross-validation error.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "7624e483",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.272793Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.272706Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.345703Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.345426Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "1e7770f5",
+      "metadata": {
+        "id": "1e7770f5"
+      },
+      "source": [
+        "Here we extract the array of coefficients corresponding to the solutions along the regularization path.\n",
+        "By default the `skl.ElasticNet.path` method fits a path along\n",
+        "an automatically selected range of $\\lambda$ values, except for the case when\n",
+        "`l1_ratio=0`, which results in ridge regression (as is the case here). {The reason is rather technical; for all models except ridge, we can find the smallest value of $\\lambda$ for which all coefficients are zero. For ridge this value is $\\infty$.}  So here\n",
+        "we have chosen to implement the function over a grid of values ranging\n",
+        "from $\\lambda=10^{8}$ to $\\lambda=10^{-2}$ scaled by the standard\n",
+        "deviation of $y$, essentially covering the full range of scenarios\n",
+        "from the null model containing only the intercept, to the least\n",
+        "squares fit.\n",
+        "\n",
+        "Associated with each value of $\\lambda$ is a vector of ridge\n",
+        "regression coefficients,   that can be accessed by\n",
+        "a column of `soln_array`. In this case, `soln_array` is a $19 \\times 100$ matrix, with\n",
+        "19 rows (one for each predictor) and 100\n",
+        "columns (one for each value of $\\lambda$).\n",
+        "\n",
+        "We transpose this matrix and turn it into a data frame to facilitate viewing and plotting."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lassoCV_fig, ax = subplots(figsize=(8,8))\n",
-    "ax.errorbar(-np.log(tuned_lasso.alphas_),\n",
-    "            tuned_lasso.mse_path_.mean(1),\n",
-    "            yerr=tuned_lasso.mse_path_.std(1) / np.sqrt(K))\n",
-    "ax.axvline(-np.log(tuned_lasso.alpha_), c='k', ls='--')\n",
-    "ax.set_ylim([50000,250000])\n",
-    "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2b85ea4",
-   "metadata": {},
-   "source": [
-    "However, the lasso has a substantial advantage over ridge regression\n",
-    "in that the resulting coefficient estimates are sparse. Here we see\n",
-    "that 6 of the 19 coefficient estimates are exactly zero. So the lasso\n",
-    "model with $\\lambda$ chosen by cross-validation contains only 13\n",
-    "variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "b389c7d4",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.347189Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.347081Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.349101Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.348883Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([-210.01008773,  243.4550306 ,    0.        ,    0.        ,\n",
-       "          0.        ,   97.69397357,  -41.52283116,   -0.        ,\n",
-       "          0.        ,   39.62298193,  205.75273856,  124.55456561,\n",
-       "       -126.29986768,   15.70262427,  -59.50157967,   75.24590036,\n",
-       "         21.62698014,  -12.04423675,   -0.        ])"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1bca28f6",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.304830Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.304756Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.311709Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.311502Z"
+        },
+        "id": "1bca28f6",
+        "outputId": "47d9be67-4f56-4487-cc57-93cafa8b1a7c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>AtBat</th>\n",
+              "      <th>Hits</th>\n",
+              "      <th>HmRun</th>\n",
+              "      <th>Runs</th>\n",
+              "      <th>RBI</th>\n",
+              "      <th>Walks</th>\n",
+              "      <th>Years</th>\n",
+              "      <th>CAtBat</th>\n",
+              "      <th>CHits</th>\n",
+              "      <th>CHmRun</th>\n",
+              "      <th>CRuns</th>\n",
+              "      <th>CRBI</th>\n",
+              "      <th>CWalks</th>\n",
+              "      <th>League[N]</th>\n",
+              "      <th>Division[W]</th>\n",
+              "      <th>PutOuts</th>\n",
+              "      <th>Assists</th>\n",
+              "      <th>Errors</th>\n",
+              "      <th>NewLeague[N]</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>negative log(lambda)</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>-12.310855</th>\n",
+              "      <td>0.000800</td>\n",
+              "      <td>0.000889</td>\n",
+              "      <td>0.000695</td>\n",
+              "      <td>0.000851</td>\n",
+              "      <td>0.000911</td>\n",
+              "      <td>0.000900</td>\n",
+              "      <td>0.000812</td>\n",
+              "      <td>0.001067</td>\n",
+              "      <td>0.001113</td>\n",
+              "      <td>0.001064</td>\n",
+              "      <td>0.001141</td>\n",
+              "      <td>0.001149</td>\n",
+              "      <td>0.000993</td>\n",
+              "      <td>-0.000029</td>\n",
+              "      <td>-0.000390</td>\n",
+              "      <td>0.000609</td>\n",
+              "      <td>0.000052</td>\n",
+              "      <td>-0.000011</td>\n",
+              "      <td>-0.000006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>-12.078271</th>\n",
+              "      <td>0.001010</td>\n",
+              "      <td>0.001122</td>\n",
+              "      <td>0.000878</td>\n",
+              "      <td>0.001074</td>\n",
+              "      <td>0.001150</td>\n",
+              "      <td>0.001135</td>\n",
+              "      <td>0.001025</td>\n",
+              "      <td>0.001346</td>\n",
+              "      <td>0.001404</td>\n",
+              "      <td>0.001343</td>\n",
+              "      <td>0.001439</td>\n",
+              "      <td>0.001450</td>\n",
+              "      <td>0.001253</td>\n",
+              "      <td>-0.000037</td>\n",
+              "      <td>-0.000492</td>\n",
+              "      <td>0.000769</td>\n",
+              "      <td>0.000065</td>\n",
+              "      <td>-0.000014</td>\n",
+              "      <td>-0.000007</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>-11.845686</th>\n",
+              "      <td>0.001274</td>\n",
+              "      <td>0.001416</td>\n",
+              "      <td>0.001107</td>\n",
+              "      <td>0.001355</td>\n",
+              "      <td>0.001451</td>\n",
+              "      <td>0.001433</td>\n",
+              "      <td>0.001293</td>\n",
+              "      <td>0.001698</td>\n",
+              "      <td>0.001772</td>\n",
+              "      <td>0.001694</td>\n",
+              "      <td>0.001816</td>\n",
+              "      <td>0.001830</td>\n",
+              "      <td>0.001581</td>\n",
+              "      <td>-0.000046</td>\n",
+              "      <td>-0.000621</td>\n",
+              "      <td>0.000970</td>\n",
+              "      <td>0.000082</td>\n",
+              "      <td>-0.000017</td>\n",
+              "      <td>-0.000009</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>-11.613102</th>\n",
+              "      <td>0.001608</td>\n",
+              "      <td>0.001787</td>\n",
+              "      <td>0.001397</td>\n",
+              "      <td>0.001710</td>\n",
+              "      <td>0.001831</td>\n",
+              "      <td>0.001808</td>\n",
+              "      <td>0.001632</td>\n",
+              "      <td>0.002143</td>\n",
+              "      <td>0.002236</td>\n",
+              "      <td>0.002138</td>\n",
+              "      <td>0.002292</td>\n",
+              "      <td>0.002309</td>\n",
+              "      <td>0.001995</td>\n",
+              "      <td>-0.000058</td>\n",
+              "      <td>-0.000784</td>\n",
+              "      <td>0.001224</td>\n",
+              "      <td>0.000104</td>\n",
+              "      <td>-0.000022</td>\n",
+              "      <td>-0.000012</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>-11.380518</th>\n",
+              "      <td>0.002029</td>\n",
+              "      <td>0.002255</td>\n",
+              "      <td>0.001763</td>\n",
+              "      <td>0.002158</td>\n",
+              "      <td>0.002310</td>\n",
+              "      <td>0.002281</td>\n",
+              "      <td>0.002059</td>\n",
+              "      <td>0.002704</td>\n",
+              "      <td>0.002821</td>\n",
+              "      <td>0.002698</td>\n",
+              "      <td>0.002892</td>\n",
+              "      <td>0.002914</td>\n",
+              "      <td>0.002517</td>\n",
+              "      <td>-0.000073</td>\n",
+              "      <td>-0.000990</td>\n",
+              "      <td>0.001544</td>\n",
+              "      <td>0.000131</td>\n",
+              "      <td>-0.000028</td>\n",
+              "      <td>-0.000015</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9.784658</th>\n",
+              "      <td>-290.823989</td>\n",
+              "      <td>336.929968</td>\n",
+              "      <td>37.322686</td>\n",
+              "      <td>-59.748520</td>\n",
+              "      <td>-26.507086</td>\n",
+              "      <td>134.855915</td>\n",
+              "      <td>-17.216195</td>\n",
+              "      <td>-387.775826</td>\n",
+              "      <td>89.573601</td>\n",
+              "      <td>-12.273926</td>\n",
+              "      <td>476.079273</td>\n",
+              "      <td>257.271255</td>\n",
+              "      <td>-213.124780</td>\n",
+              "      <td>31.258215</td>\n",
+              "      <td>-58.457857</td>\n",
+              "      <td>78.761266</td>\n",
+              "      <td>53.622113</td>\n",
+              "      <td>-22.208456</td>\n",
+              "      <td>-12.402891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10.017243</th>\n",
+              "      <td>-290.879272</td>\n",
+              "      <td>337.113713</td>\n",
+              "      <td>37.431373</td>\n",
+              "      <td>-59.916820</td>\n",
+              "      <td>-26.606957</td>\n",
+              "      <td>134.900549</td>\n",
+              "      <td>-17.108041</td>\n",
+              "      <td>-388.458404</td>\n",
+              "      <td>89.000707</td>\n",
+              "      <td>-12.661459</td>\n",
+              "      <td>477.031349</td>\n",
+              "      <td>257.966790</td>\n",
+              "      <td>-213.280891</td>\n",
+              "      <td>31.256434</td>\n",
+              "      <td>-58.448850</td>\n",
+              "      <td>78.761240</td>\n",
+              "      <td>53.645147</td>\n",
+              "      <td>-22.198802</td>\n",
+              "      <td>-12.391969</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10.249827</th>\n",
+              "      <td>-290.923382</td>\n",
+              "      <td>337.260446</td>\n",
+              "      <td>37.518064</td>\n",
+              "      <td>-60.051166</td>\n",
+              "      <td>-26.686604</td>\n",
+              "      <td>134.936136</td>\n",
+              "      <td>-17.022194</td>\n",
+              "      <td>-388.997470</td>\n",
+              "      <td>88.537380</td>\n",
+              "      <td>-12.971603</td>\n",
+              "      <td>477.791860</td>\n",
+              "      <td>258.523025</td>\n",
+              "      <td>-213.405740</td>\n",
+              "      <td>31.254958</td>\n",
+              "      <td>-58.441682</td>\n",
+              "      <td>78.761230</td>\n",
+              "      <td>53.663357</td>\n",
+              "      <td>-22.191071</td>\n",
+              "      <td>-12.383205</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10.482412</th>\n",
+              "      <td>-290.958537</td>\n",
+              "      <td>337.377455</td>\n",
+              "      <td>37.587122</td>\n",
+              "      <td>-60.158256</td>\n",
+              "      <td>-26.750044</td>\n",
+              "      <td>134.964477</td>\n",
+              "      <td>-16.954081</td>\n",
+              "      <td>-389.423414</td>\n",
+              "      <td>88.164178</td>\n",
+              "      <td>-13.219329</td>\n",
+              "      <td>478.398404</td>\n",
+              "      <td>258.967059</td>\n",
+              "      <td>-213.505412</td>\n",
+              "      <td>31.253747</td>\n",
+              "      <td>-58.435983</td>\n",
+              "      <td>78.761230</td>\n",
+              "      <td>53.677759</td>\n",
+              "      <td>-22.184893</td>\n",
+              "      <td>-12.376191</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10.714996</th>\n",
+              "      <td>-290.986528</td>\n",
+              "      <td>337.470648</td>\n",
+              "      <td>37.642077</td>\n",
+              "      <td>-60.243522</td>\n",
+              "      <td>-26.800522</td>\n",
+              "      <td>134.987027</td>\n",
+              "      <td>-16.900054</td>\n",
+              "      <td>-389.760135</td>\n",
+              "      <td>87.864551</td>\n",
+              "      <td>-13.416889</td>\n",
+              "      <td>478.881540</td>\n",
+              "      <td>259.321007</td>\n",
+              "      <td>-213.584869</td>\n",
+              "      <td>31.252760</td>\n",
+              "      <td>-58.431454</td>\n",
+              "      <td>78.761235</td>\n",
+              "      <td>53.689152</td>\n",
+              "      <td>-22.179964</td>\n",
+              "      <td>-12.370587</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>100 rows × 19 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                           AtBat        Hits      HmRun       Runs        RBI  \\\n",
+              "negative log(lambda)                                                            \n",
+              "-12.310855              0.000800    0.000889   0.000695   0.000851   0.000911   \n",
+              "-12.078271              0.001010    0.001122   0.000878   0.001074   0.001150   \n",
+              "-11.845686              0.001274    0.001416   0.001107   0.001355   0.001451   \n",
+              "-11.613102              0.001608    0.001787   0.001397   0.001710   0.001831   \n",
+              "-11.380518              0.002029    0.002255   0.001763   0.002158   0.002310   \n",
+              "...                          ...         ...        ...        ...        ...   \n",
+              " 9.784658            -290.823989  336.929968  37.322686 -59.748520 -26.507086   \n",
+              " 10.017243           -290.879272  337.113713  37.431373 -59.916820 -26.606957   \n",
+              " 10.249827           -290.923382  337.260446  37.518064 -60.051166 -26.686604   \n",
+              " 10.482412           -290.958537  337.377455  37.587122 -60.158256 -26.750044   \n",
+              " 10.714996           -290.986528  337.470648  37.642077 -60.243522 -26.800522   \n",
+              "\n",
+              "                           Walks      Years      CAtBat      CHits     CHmRun  \\\n",
+              "negative log(lambda)                                                            \n",
+              "-12.310855              0.000900   0.000812    0.001067   0.001113   0.001064   \n",
+              "-12.078271              0.001135   0.001025    0.001346   0.001404   0.001343   \n",
+              "-11.845686              0.001433   0.001293    0.001698   0.001772   0.001694   \n",
+              "-11.613102              0.001808   0.001632    0.002143   0.002236   0.002138   \n",
+              "-11.380518              0.002281   0.002059    0.002704   0.002821   0.002698   \n",
+              "...                          ...        ...         ...        ...        ...   \n",
+              " 9.784658             134.855915 -17.216195 -387.775826  89.573601 -12.273926   \n",
+              " 10.017243            134.900549 -17.108041 -388.458404  89.000707 -12.661459   \n",
+              " 10.249827            134.936136 -17.022194 -388.997470  88.537380 -12.971603   \n",
+              " 10.482412            134.964477 -16.954081 -389.423414  88.164178 -13.219329   \n",
+              " 10.714996            134.987027 -16.900054 -389.760135  87.864551 -13.416889   \n",
+              "\n",
+              "                           CRuns        CRBI      CWalks  League[N]  \\\n",
+              "negative log(lambda)                                                  \n",
+              "-12.310855              0.001141    0.001149    0.000993  -0.000029   \n",
+              "-12.078271              0.001439    0.001450    0.001253  -0.000037   \n",
+              "-11.845686              0.001816    0.001830    0.001581  -0.000046   \n",
+              "-11.613102              0.002292    0.002309    0.001995  -0.000058   \n",
+              "-11.380518              0.002892    0.002914    0.002517  -0.000073   \n",
+              "...                          ...         ...         ...        ...   \n",
+              " 9.784658             476.079273  257.271255 -213.124780  31.258215   \n",
+              " 10.017243            477.031349  257.966790 -213.280891  31.256434   \n",
+              " 10.249827            477.791860  258.523025 -213.405740  31.254958   \n",
+              " 10.482412            478.398404  258.967059 -213.505412  31.253747   \n",
+              " 10.714996            478.881540  259.321007 -213.584869  31.252760   \n",
+              "\n",
+              "                      Division[W]    PutOuts    Assists     Errors  \\\n",
+              "negative log(lambda)                                                 \n",
+              "-12.310855              -0.000390   0.000609   0.000052  -0.000011   \n",
+              "-12.078271              -0.000492   0.000769   0.000065  -0.000014   \n",
+              "-11.845686              -0.000621   0.000970   0.000082  -0.000017   \n",
+              "-11.613102              -0.000784   0.001224   0.000104  -0.000022   \n",
+              "-11.380518              -0.000990   0.001544   0.000131  -0.000028   \n",
+              "...                           ...        ...        ...        ...   \n",
+              " 9.784658              -58.457857  78.761266  53.622113 -22.208456   \n",
+              " 10.017243             -58.448850  78.761240  53.645147 -22.198802   \n",
+              " 10.249827             -58.441682  78.761230  53.663357 -22.191071   \n",
+              " 10.482412             -58.435983  78.761230  53.677759 -22.184893   \n",
+              " 10.714996             -58.431454  78.761235  53.689152 -22.179964   \n",
+              "\n",
+              "                      NewLeague[N]  \n",
+              "negative log(lambda)                \n",
+              "-12.310855               -0.000006  \n",
+              "-12.078271               -0.000007  \n",
+              "-11.845686               -0.000009  \n",
+              "-11.613102               -0.000012  \n",
+              "-11.380518               -0.000015  \n",
+              "...                            ...  \n",
+              " 9.784658               -12.402891  \n",
+              " 10.017243              -12.391969  \n",
+              " 10.249827              -12.383205  \n",
+              " 10.482412              -12.376191  \n",
+              " 10.714996              -12.370587  \n",
+              "\n",
+              "[100 rows x 19 columns]"
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "soln_path = pd.DataFrame(soln_array.T,\n",
+        "                         columns=D.columns,\n",
+        "                         index=-np.log(lambdas))\n",
+        "soln_path.index.name = 'negative log(lambda)'\n",
+        "soln_path\n"
       ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tuned_lasso.coef_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "acc5ed38",
-   "metadata": {},
-   "source": [
-    "As in ridge regression, we could evaluate the test error\n",
-    "of cross-validated lasso by first splitting into\n",
-    "test and training sets and internally running\n",
-    "cross-validation on the training set. We leave\n",
-    "this as an exercise."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0d0aca26",
-   "metadata": {},
-   "source": [
-    "## PCR and PLS Regression\n",
-    "\n",
-    "### Principal Components Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2120ab5",
-   "metadata": {},
-   "source": [
-    "Principal components regression (PCR) can be performed using\n",
-    "`PCA()`  from the `sklearn.decomposition`\n",
-    "module. We now apply PCR to the  `Hitters`  data, in order to\n",
-    "predict `Salary`. Again, ensure that the missing values have\n",
-    "been removed from the data, as described in Section~\\ref{Ch6-varselect-lab:lab-1-subset-selection-methods}.\n",
-    "\n",
-    "We use `LinearRegression()`  to fit the regression model\n",
-    "here. Note that it fits an intercept by default, unlike\n",
-    "the `OLS()` function seen earlier in Section~\\ref{Ch6-varselect-lab:lab-1-subset-selection-methods}."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "cf302d4c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.350411Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.350323Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.353307Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.353084Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([0.09846131, 0.4758765 ])"
+      "cell_type": "markdown",
+      "id": "2246bb2b",
+      "metadata": {
+        "id": "2246bb2b"
+      },
+      "source": [
+        "We plot the paths to get a sense of how the coefficients vary with $\\lambda$.\n",
+        "To control the location of the legend we first set `legend` to `False` in the\n",
+        "plot method, adding it afterward with the `legend()` method of `ax`."
       ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pca = PCA(n_components=2)\n",
-    "linreg = skl.LinearRegression()\n",
-    "pipe = Pipeline([('pca', pca),\n",
-    "                 ('linreg', linreg)])\n",
-    "pipe.fit(X, Y)\n",
-    "pipe.named_steps['linreg'].coef_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b909182b",
-   "metadata": {},
-   "source": [
-    "When performing PCA, the results vary depending\n",
-    "on whether the data has been *standardized* or not.\n",
-    "As in the earlier examples, this can be accomplished\n",
-    "by including an additional step in the pipeline."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "af4145b6",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.354462Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.354377Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.357347Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.357140Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([106.36859204,  21.60350456])"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "08bc997b",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.312975Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.312908Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.473880Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.473585Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "08bc997b",
+        "outputId": "5be9179e-0523-4bc1-9feb-26cbd4612b31"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "path_fig, ax = subplots(figsize=(8,8))\n",
+        "soln_path.plot(ax=ax, legend=False)\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Standardized coefficients', fontsize=20)\n",
+        "ax.legend(loc='upper left');\n"
       ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipe = Pipeline([('scaler', scaler), \n",
-    "                 ('pca', pca),\n",
-    "                 ('linreg', linreg)])\n",
-    "pipe.fit(X, Y)\n",
-    "pipe.named_steps['linreg'].coef_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0da4ae12",
-   "metadata": {},
-   "source": [
-    "We can of course use CV to choose the number of components, by\n",
-    "using `skm.GridSearchCV`, in this\n",
-    "case fixing the parameters to vary the\n",
-    "`n_components`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "16e047ed",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.358664Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.358558Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.448710Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.448481Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-6 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-6 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-6 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-6 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-6 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                                       (&#x27;pca&#x27;, PCA(n_components=2)),\n",
-       "                                       (&#x27;linreg&#x27;, LinearRegression())]),\n",
-       "             param_grid={&#x27;pca__n_components&#x27;: range(1, 20)},\n",
-       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
-       "                                       (&#x27;pca&#x27;, PCA(n_components=2)),\n",
-       "                                       (&#x27;linreg&#x27;, LinearRegression())]),\n",
-       "             param_grid={&#x27;pca__n_components&#x27;: range(1, 20)},\n",
-       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;pca&#x27;, PCA(n_components=17)),\n",
-       "                (&#x27;linreg&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PCA<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PCA(n_components=17)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      "cell_type": "markdown",
+      "id": "ae73ca09",
+      "metadata": {
+        "id": "ae73ca09"
+      },
+      "source": [
+        "(We have used `latex` formatting in the horizontal label, in order to format the Greek $\\lambda$ appropriately.)\n",
+        "We expect the coefficient estimates to be much smaller, in terms of\n",
+        "$\\ell_2$ norm, when a large value of $\\lambda$ is used, as compared to\n",
+        "when a small value of $\\lambda$ is used. (Recall that the  $\\ell_2$ norm is the square root of the sum of squared coefficient values.) We display  the coefficients at the $40$th step,\n",
+        "where $\\lambda$ is 25.535."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0f7359c7",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.475363Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.475242Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.477806Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.477534Z"
+        },
+        "id": "0f7359c7",
+        "outputId": "c0e3fdd0-bcc4-4297-8749-166f4e1aeca0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(25.53538897200662,\n",
+              " AtBat           5.433750\n",
+              " Hits            6.223582\n",
+              " HmRun           4.585498\n",
+              " Runs            5.880855\n",
+              " RBI             6.195921\n",
+              " Walks           6.277975\n",
+              " Years           5.299767\n",
+              " CAtBat          7.147501\n",
+              " CHits           7.539495\n",
+              " CHmRun          7.182344\n",
+              " CRuns           7.728649\n",
+              " CRBI            7.790702\n",
+              " CWalks          6.592901\n",
+              " League[N]       0.042445\n",
+              " Division[W]    -3.107159\n",
+              " PutOuts         4.605263\n",
+              " Assists         0.378371\n",
+              " Errors         -0.135196\n",
+              " NewLeague[N]    0.150323\n",
+              " Name: -3.240065292879872, dtype: float64)"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
-       "                                       ('pca', PCA(n_components=2)),\n",
-       "                                       ('linreg', LinearRegression())]),\n",
-       "             param_grid={'pca__n_components': range(1, 20)},\n",
-       "             scoring='neg_mean_squared_error')"
+      "source": [
+        "beta_hat = soln_path.loc[soln_path.index[39]]\n",
+        "lambdas[39], beta_hat\n"
       ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param_grid = {'pca__n_components': range(1, 20)}\n",
-    "grid = skm.GridSearchCV(pipe,\n",
-    "                        param_grid,\n",
-    "                        cv=kfold,\n",
-    "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "148bf752",
-   "metadata": {},
-   "source": [
-    "Let’s plot the results as we have for other methods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "7390847f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.450064Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.449993Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.518771Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.518504Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "9baa8b00",
+      "metadata": {
+        "id": "9baa8b00"
+      },
+      "source": [
+        "Let’s compute the $\\ell_2$ norm of the standardized coefficients."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pcr_fig, ax = subplots(figsize=(8,8))\n",
-    "n_comp = param_grid['pca__n_components']\n",
-    "ax.errorbar(n_comp,\n",
-    "            -grid.cv_results_['mean_test_score'],\n",
-    "            grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
-    "ax.set_xlabel('# principal components', fontsize=20)\n",
-    "ax.set_xticks(n_comp[::2])\n",
-    "ax.set_ylim([50000,250000]);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4269a0db",
-   "metadata": {},
-   "source": [
-    "We see that the smallest cross-validation error occurs when\n",
-    "17\n",
-    "components are used. However, from the plot we also see that the\n",
-    "cross-validation error is roughly the same when only one component is\n",
-    "included in the model. This suggests that a model that uses just a\n",
-    "small number of components might suffice.\n",
-    "\n",
-    "The CV score is provided for each possible number of components from\n",
-    "1 to 19 inclusive. The `PCA()` method complains\n",
-    "if we try to fit an intercept only with `n_components=0`\n",
-    "so we also compute the MSE for just the null model with\n",
-    "these splits."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "1e084cbb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.520137Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.520059Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.525293Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.525077Z"
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "204139.30692994667"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d2984305",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.479160Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.479064Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.481153Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.480921Z"
+        },
+        "id": "d2984305",
+        "outputId": "5bc092b5-a846-4db1-ff4c-b2f3eb128485"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "24.17061720144378"
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "np.linalg.norm(beta_hat)\n"
       ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Xn = np.zeros((X.shape[0], 1))\n",
-    "cv_null = skm.cross_validate(linreg,\n",
-    "                             Xn,\n",
-    "                             Y,\n",
-    "                             cv=kfold,\n",
-    "                             scoring='neg_mean_squared_error')\n",
-    "-cv_null['test_score'].mean()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c0d69df",
-   "metadata": {},
-   "source": [
-    "The `explained_variance_ratio_`\n",
-    "attribute of our `PCA` object provides the *percentage of variance explained* in the predictors and in the response using\n",
-    "different numbers of components. This concept is discussed in greater\n",
-    "detail in Section~\\ref{Ch10:sec:pca}."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "c7616c02",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.526523Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.526454Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.528563Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.528353Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([0.3831424 , 0.21841076])"
+      "cell_type": "markdown",
+      "id": "5111fc6f",
+      "metadata": {
+        "id": "5111fc6f"
+      },
+      "source": [
+        "In contrast, here is the $\\ell_2$ norm when $\\lambda$ is 2.44e-01.\n",
+        "Note the much larger $\\ell_2$ norm of the\n",
+        "coefficients associated with this smaller value of $\\lambda$."
       ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipe.named_steps['pca'].explained_variance_ratio_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a2ee7cb1",
-   "metadata": {},
-   "source": [
-    "Briefly, we can think of\n",
-    "this as the amount of information about the predictors\n",
-    "that is captured using $M$ principal components. For example, setting\n",
-    "$M=1$ only captures 38.31% of the variance, while $M=2$ captures an additional 21.84%, for a total of 60.15% of the variance.\n",
-    "By  $M=6$ it increases to\n",
-    "88.63%. Beyond this the increments continue to diminish, until we use all $M=p=19$ components, which captures all  100% of the variance.\n",
-    "\n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a0ce7e4",
-   "metadata": {},
-   "source": [
-    "### Partial Least Squares\n",
-    "Partial least squares (PLS) is implemented in the\n",
-    "`PLSRegression()`  function.\n",
-    "\n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "9c69429e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.529851Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.529780Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.532907Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.532670Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-7 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-7 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-7 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-7 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-7 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-7 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-7 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PLSRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;PLSRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cross_decomposition.PLSRegression.html\">?<span>Documentation for PLSRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression()</pre></div> </div></div></div></div>"
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9a3424a4",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.482508Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.482402Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.484554Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.484320Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "9a3424a4",
+        "outputId": "77bd05db-3f3f-45f3-ebfb-ba01c1928957"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(0.24374766133488554, 160.42371017725839)"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "PLSRegression()"
+      "source": [
+        "beta_hat = soln_path.loc[soln_path.index[59]]\n",
+        "lambdas[59], np.linalg.norm(beta_hat)\n"
       ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pls = PLSRegression(n_components=2, \n",
-    "                    scale=True)\n",
-    "pls.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a00592ba",
-   "metadata": {},
-   "source": [
-    "As was the case in PCR, we will want to\n",
-    "use CV to choose the number of components."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "043f27fa",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.534240Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.534173Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.610294Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.610076Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-8 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-8 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-8 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-8 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-8 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-8 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-8 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=PLSRegression(),\n",
-       "             param_grid={&#x27;n_components&#x27;: range(1, 20)},\n",
-       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=PLSRegression(),\n",
-       "             param_grid={&#x27;n_components&#x27;: range(1, 20)},\n",
-       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: PLSRegression</label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression(n_components=12)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PLSRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cross_decomposition.PLSRegression.html\">?<span>Documentation for PLSRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression(n_components=12)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      "cell_type": "markdown",
+      "id": "50239927",
+      "metadata": {
+        "id": "50239927"
+      },
+      "source": [
+        "Above we normalized `X` upfront, and fit the ridge model using `Xs`.\n",
+        "The `Pipeline()`  object\n",
+        "in `sklearn` provides a clear way to separate feature\n",
+        "normalization from the fitting of the ridge model itself."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c864d25f",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.485813Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.485727Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.495072Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.494829Z"
+        },
+        "id": "c864d25f",
+        "outputId": "7726e029-74a4-4bfb-bde6-931af081e9be"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.24374766133488554, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                ('ridge', ElasticNet(alpha=0.24374766133488554, l1_ratio=0))])"
+            ]
+          },
+          "execution_count": 28,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
-       "             estimator=PLSRegression(),\n",
-       "             param_grid={'n_components': range(1, 20)},\n",
-       "             scoring='neg_mean_squared_error')"
+      "source": [
+        "ridge = skl.ElasticNet(alpha=lambdas[59], l1_ratio=0)\n",
+        "scaler = StandardScaler(with_mean=True,  with_std=True)\n",
+        "pipe = Pipeline(steps=[('scaler', scaler), ('ridge', ridge)])\n",
+        "pipe.fit(X, Y)\n"
       ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param_grid = {'n_components':range(1, 20)}\n",
-    "grid = skm.GridSearchCV(pls,\n",
-    "                        param_grid,\n",
-    "                        cv=kfold,\n",
-    "                        scoring='neg_mean_squared_error')\n",
-    "grid.fit(X, Y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1bc745c",
-   "metadata": {},
-   "source": [
-    "As for our other methods, we plot the MSE."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "b702fb04",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:33.611587Z",
-     "iopub.status.busy": "2024-06-04T23:19:33.611516Z",
-     "iopub.status.idle": "2024-06-04T23:19:33.678469Z",
-     "shell.execute_reply": "2024-06-04T23:19:33.678245Z"
-    }
-   },
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "id": "71c8c514",
+      "metadata": {
+        "id": "71c8c514"
+      },
+      "source": [
+        "We show that it gives the same $\\ell_2$ norm as in our previous fit on the standardized data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "8a07da6e",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.496384Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.496277Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.498333Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.498078Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "8a07da6e",
+        "outputId": "9f360b73-0606-476f-bddf-4c0b5d4320ff"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "160.4237101772591"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "np.linalg.norm(ridge.coef_)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6a20ea7e",
+      "metadata": {
+        "id": "6a20ea7e"
+      },
+      "source": [
+        " Notice that the operation `pipe.fit(X, Y)` above has changed the `ridge` object, and in particular has added attributes such as `coef_` that were not there before.\n",
+        "### Estimating Test Error of Ridge Regression\n",
+        "Choosing an *a priori* value of $\\lambda$ for ridge regression is\n",
+        "difficult if not impossible. We will want to use the validation method\n",
+        "or cross-validation to select the tuning parameter. The reader may not\n",
+        "be surprised that the  `Pipeline()` approach can be used in\n",
+        "`skm.cross_validate()` with either a validation method\n",
+        "(i.e. `validation`) or $k$-fold cross-validation.\n",
+        "\n",
+        "We fix the random state of the splitter\n",
+        "so that the results obtained will be reproducible."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c10e3ae5",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.499658Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.499553Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.505630Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.505369Z"
+        },
+        "id": "c10e3ae5",
+        "outputId": "eba67252-e359-4b05-c060-9b4b033dc18d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([134214.00419204])"
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "validation = skm.ShuffleSplit(n_splits=1,\n",
+        "                              test_size=0.5,\n",
+        "                              random_state=0)\n",
+        "ridge.alpha = 0.01\n",
+        "results = skm.cross_validate(ridge,\n",
+        "                             X,\n",
+        "                             Y,\n",
+        "                             scoring='neg_mean_squared_error',\n",
+        "                             cv=validation)\n",
+        "-results['test_score']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c5f962ee",
+      "metadata": {
+        "id": "c5f962ee"
+      },
+      "source": [
+        "The test MSE is 1.342e+05.  Note\n",
+        "that if we had instead simply fit a model with just an intercept, we\n",
+        "would have predicted each test observation using the mean of the\n",
+        "training observations. We can get the same result by fitting a ridge regression model\n",
+        "with a *very* large value of $\\lambda$. Note that `1e10`\n",
+        "means $10^{10}$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0df2b24d",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.507058Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.506964Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.512846Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.512640Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "0df2b24d",
+        "outputId": "fbc9f531-099d-4b62-c33b-6cc043a0dbe6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([231788.32155285])"
+            ]
+          },
+          "execution_count": 31,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "ridge.alpha = 1e10\n",
+        "results = skm.cross_validate(ridge,\n",
+        "                             X,\n",
+        "                             Y,\n",
+        "                             scoring='neg_mean_squared_error',\n",
+        "                             cv=validation)\n",
+        "-results['test_score']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e78a776c",
+      "metadata": {
+        "id": "e78a776c"
+      },
+      "source": [
+        "Obviously choosing $\\lambda=0.01$ is arbitrary,  so we will  use cross-validation or the validation-set\n",
+        "approach to choose the tuning parameter $\\lambda$.\n",
+        "The object `GridSearchCV()`  allows exhaustive\n",
+        "grid search to choose such a parameter.\n",
+        "\n",
+        "We first use the validation set method\n",
+        "to choose $\\lambda$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9b76df85",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.514070Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.514003Z",
+          "iopub.status.idle": "2024-06-04T23:19:27.851311Z",
+          "shell.execute_reply": "2024-06-04T23:19:27.851092Z"
+        },
+        "id": "9b76df85",
+        "outputId": "83b502ba-9d3f-47b5-e3fa-70ddb78e4861"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-2 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-2 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-2 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-2 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-2 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.005899006046740856, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                ('ridge', ElasticNet(alpha=0.005899006046740856, l1_ratio=0))])"
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "param_grid = {'ridge__alpha': lambdas}\n",
+        "grid = skm.GridSearchCV(pipe,\n",
+        "                        param_grid,\n",
+        "                        cv=validation,\n",
+        "                        scoring='neg_mean_squared_error')\n",
+        "grid.fit(X, Y)\n",
+        "grid.best_params_['ridge__alpha']\n",
+        "grid.best_estimator_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9befd23e",
+      "metadata": {
+        "id": "9befd23e"
+      },
+      "source": [
+        "Alternatively, we can use 5-fold cross-validation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3e4c1b53",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:27.852563Z",
+          "iopub.status.busy": "2024-06-04T23:19:27.852495Z",
+          "iopub.status.idle": "2024-06-04T23:19:30.080670Z",
+          "shell.execute_reply": "2024-06-04T23:19:30.080412Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "3e4c1b53",
+        "outputId": "0f5bf6c7-be3a-4933-ac2b-eb719ffb345a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-3 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-3 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-3 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-3 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-3 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-3 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.01185247763144249, l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                ('ridge', ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "grid = skm.GridSearchCV(pipe,\n",
+        "                        param_grid,\n",
+        "                        cv=kfold,\n",
+        "                        scoring='neg_mean_squared_error')\n",
+        "grid.fit(X, Y)\n",
+        "grid.best_params_['ridge__alpha']\n",
+        "grid.best_estimator_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cae429da",
+      "metadata": {
+        "id": "cae429da"
+      },
+      "source": [
+        "Recall we set up the `kfold` object for 5-fold cross-validation on page~\\pageref{line:choos-among-models}. We now plot the cross-validated MSE as a function of $-\\log(\\lambda)$, which has shrinkage decreasing from left\n",
+        "to right."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "39ff3251",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:30.082088Z",
+          "iopub.status.busy": "2024-06-04T23:19:30.082012Z",
+          "iopub.status.idle": "2024-06-04T23:19:30.194197Z",
+          "shell.execute_reply": "2024-06-04T23:19:30.193921Z"
+        },
+        "id": "39ff3251",
+        "outputId": "6c94ef4c-3bc6-4482-f6ac-2cae1b4cdbea"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "ridge_fig, ax = subplots(figsize=(8,8))\n",
+        "ax.errorbar(-np.log(lambdas),\n",
+        "            -grid.cv_results_['mean_test_score'],\n",
+        "            yerr=grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
+        "ax.set_ylim([50000,250000])\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "854e0c21",
+      "metadata": {
+        "id": "854e0c21"
+      },
+      "source": [
+        "One can cross-validate different metrics to choose a parameter. The default\n",
+        "metric for `skl.ElasticNet()` is test $R^2$.\n",
+        "Let’s compare $R^2$ to MSE for cross-validation here."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "766ab0b0",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:30.195678Z",
+          "iopub.status.busy": "2024-06-04T23:19:30.195576Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.425386Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.425155Z"
+        },
+        "id": "766ab0b0",
+        "outputId": "ed4ae437-b07a-4e55-e22c-e0096177c725"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-4 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-4 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-4 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-4 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                                       (&#x27;ridge&#x27;,\n",
+              "                                        ElasticNet(alpha=10000000000.0,\n",
+              "                                                   l1_ratio=0))]),\n",
+              "             param_grid={&#x27;ridge__alpha&#x27;: array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606...\n",
+              "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                                       (&#x27;ridge&#x27;,\n",
+              "                                        ElasticNet(alpha=10000000000.0,\n",
+              "                                                   l1_ratio=0))]),\n",
+              "             param_grid={&#x27;ridge__alpha&#x27;: array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606...\n",
+              "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;, ElasticNet(alpha=0.01185247763144249, l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNet<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNet.html\">?<span>Documentation for ElasticNet</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNet(alpha=0.01185247763144249, l1_ratio=0)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                                       ('ridge',\n",
+              "                                        ElasticNet(alpha=10000000000.0,\n",
+              "                                                   l1_ratio=0))]),\n",
+              "             param_grid={'ridge__alpha': array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606...\n",
+              "       4.67486141e-03, 3.70474772e-03, 2.93594921e-03, 2.32668954e-03,\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05])})"
+            ]
+          },
+          "execution_count": 35,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "grid_r2 = skm.GridSearchCV(pipe,\n",
+        "                           param_grid,\n",
+        "                           cv=kfold)\n",
+        "grid_r2.fit(X, Y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f3887a9c",
+      "metadata": {
+        "id": "f3887a9c"
+      },
+      "source": [
+        "Finally, let’s plot the results for cross-validated $R^2$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "642f1de3",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.426644Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.426570Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.492503Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.492264Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "642f1de3",
+        "outputId": "908a2d47-9892-44e8-e4b0-0e8c9ac0513d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "r2_fig, ax = subplots(figsize=(8,8))\n",
+        "ax.errorbar(-np.log(lambdas),\n",
+        "            grid_r2.cv_results_['mean_test_score'],\n",
+        "            yerr=grid_r2.cv_results_['std_test_score'] / np.sqrt(K))\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Cross-validated $R^2$', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0cb6f97b",
+      "metadata": {
+        "id": "0cb6f97b"
+      },
+      "source": [
+        "### Fast Cross-Validation for Solution Paths\n",
+        "The ridge, lasso, and elastic net can be efficiently fit along a sequence of $\\lambda$ values, creating what is known as a *solution path* or *regularization path*. Hence there is specialized code to fit\n",
+        "such paths, and to choose a suitable value of $\\lambda$ using cross-validation. Even with\n",
+        "identical splits the results will not agree *exactly* with our `grid`\n",
+        "above because the standardization of each feature  in `grid` is carried out on each fold,\n",
+        "while in `pipeCV` below it is carried out only once.\n",
+        "Nevertheless, the results are similar as the normalization\n",
+        "is relatively stable across folds."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "016ffe7c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.493853Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.493772Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.763066Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.762828Z"
+        },
+        "id": "016ffe7c",
+        "outputId": "9cf809ce-af23-4a0d-9a29-1bf602c0ad1e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-5 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-5 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-5 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-5 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-5 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;,\n",
+              "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
+              "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
+              "       5.37495461e+03, 4.25955961e+03,...\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
+              "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "                              l1_ratio=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;ridge&#x27;,\n",
+              "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
+              "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
+              "       5.37495461e+03, 4.25955961e+03,...\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
+              "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "                              l1_ratio=0))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ElasticNetCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.ElasticNetCV.html\">?<span>Documentation for ElasticNetCV</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
+              "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
+              "       5.37495461e+03, 4.25955961e+03, 3.37562814e+03, 2.67512757e+03,\n",
+              "       2.11999285e+03, 1.680058...\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
+              "             cv=KFold(n_splits=5, random_state=0, shuffle=True), l1_ratio=0)</pre></div> </div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                ('ridge',\n",
+              "                 ElasticNetCV(alphas=array([2.22093791e+05, 1.76005531e+05, 1.39481373e+05, 1.10536603e+05,\n",
+              "       8.75983676e+04, 6.94202082e+04, 5.50143278e+04, 4.35979140e+04,\n",
+              "       3.45506012e+04, 2.73807606e+04, 2.16987845e+04, 1.71959156e+04,\n",
+              "       1.36274691e+04, 1.07995362e+04, 8.55844774e+03, 6.78242347e+03,\n",
+              "       5.37495461e+03, 4.25955961e+03,...\n",
+              "       1.84386167e-03, 1.46122884e-03, 1.15799887e-03, 9.17694298e-04,\n",
+              "       7.27257037e-04, 5.76338765e-04, 4.56738615e-04, 3.61957541e-04,\n",
+              "       2.86845161e-04, 2.27319885e-04, 1.80147121e-04, 1.42763513e-04,\n",
+              "       1.13137642e-04, 8.96596467e-05, 7.10537367e-05, 5.63088712e-05,\n",
+              "       4.46238174e-05, 3.53636122e-05, 2.80250579e-05, 2.22093791e-05]),\n",
+              "                              cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "                              l1_ratio=0))])"
+            ]
+          },
+          "execution_count": 37,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "ridgeCV = skl.ElasticNetCV(alphas=lambdas,\n",
+        "                           l1_ratio=0,\n",
+        "                           cv=kfold)\n",
+        "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
+        "                         ('ridge', ridgeCV)])\n",
+        "pipeCV.fit(X, Y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b6845efe",
+      "metadata": {
+        "id": "b6845efe"
+      },
+      "source": [
+        "Let’s produce a plot again of the cross-validation error to see that\n",
+        "it is similar to using `skm.GridSearchCV`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "52f6cf29",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.764408Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.764316Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.836546Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.836313Z"
+        },
+        "id": "52f6cf29",
+        "outputId": "d86de2a7-33ab-45a3-87c9-4a206220440e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "tuned_ridge = pipeCV.named_steps['ridge']\n",
+        "ridgeCV_fig, ax = subplots(figsize=(8,8))\n",
+        "ax.errorbar(-np.log(lambdas),\n",
+        "            tuned_ridge.mse_path_.mean(1),\n",
+        "            yerr=tuned_ridge.mse_path_.std(1) / np.sqrt(K))\n",
+        "ax.axvline(-np.log(tuned_ridge.alpha_), c='k', ls='--')\n",
+        "ax.set_ylim([50000,250000])\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Cross-validated MSE', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "80e07995",
+      "metadata": {
+        "id": "80e07995"
+      },
+      "source": [
+        "We see that the value of $\\lambda$ that results in the\n",
+        "smallest cross-validation error is 1.19e-02, available\n",
+        "as the value `tuned_ridge.alpha_`. What is the test MSE\n",
+        "associated with this value of $\\lambda$?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2b49c3d7",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.838011Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.837899Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.840000Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.839740Z"
+        },
+        "id": "2b49c3d7",
+        "outputId": "3c45d687-3289-40af-daf7-abfbc73984dd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "115526.70630987917"
+            ]
+          },
+          "execution_count": 39,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "np.min(tuned_ridge.mse_path_.mean(1))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0961957c",
+      "metadata": {
+        "id": "0961957c"
+      },
+      "source": [
+        "This represents a further improvement over the test MSE that we got\n",
+        "using $\\lambda=4$.  Finally, `tuned_ridge.coef_`\n",
+        "has the coefficients fit on the entire data set\n",
+        "at this value of  $\\lambda$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "973e7e79",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.841440Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.841345Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.843427Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.843185Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "973e7e79",
+        "outputId": "9448545b-da26-497c-d656-26a3636b1e75"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([-222.80877051,  238.77246614,    3.21103754,   -2.93050845,\n",
+              "          3.64888723,  108.90953869,  -50.81896152, -105.15731984,\n",
+              "        122.00714801,   57.1859509 ,  210.35170348,  118.05683748,\n",
+              "       -150.21959435,   30.36634231,  -61.62459095,   77.73832472,\n",
+              "         40.07350744,  -25.02151514,  -13.68429544])"
+            ]
+          },
+          "execution_count": 40,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tuned_ridge.coef_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b19ffa3f",
+      "metadata": {
+        "id": "b19ffa3f"
+      },
+      "source": [
+        "As expected, none of the coefficients are zero—ridge regression does\n",
+        "not perform variable selection!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bfb6c72e",
+      "metadata": {
+        "id": "bfb6c72e"
+      },
+      "source": [
+        "### Evaluating Test Error of Cross-Validated Ridge\n",
+        "Choosing $\\lambda$ using cross-validation provides a single regression\n",
+        "estimator, similar to fitting a linear regression model as we saw in\n",
+        "Chapter~\\ref{Ch3:linreg}. It is therefore reasonable to estimate what its test error\n",
+        "is. We run into a problem here in that cross-validation will have\n",
+        "*touched* all of its data in choosing $\\lambda$, hence we have no\n",
+        "further data to estimate test error. A compromise is to do an initial\n",
+        "split of the data into two disjoint sets: a training set and a test set.\n",
+        "We then fit a cross-validation\n",
+        "tuned ridge regression on the training set, and evaluate its performance on the test set.\n",
+        "We might call this cross-validation nested\n",
+        "within the validation set approach. A priori there is no reason to use\n",
+        "half of the data for each of the two sets in validation. Below, we use\n",
+        "75% for training and 25% for test, with the estimator being ridge\n",
+        "regression tuned using 5-fold cross-validation.  This can be achieved\n",
+        "in code as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a9140b3a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.844819Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.844727Z",
+          "iopub.status.idle": "2024-06-04T23:19:32.846512Z",
+          "shell.execute_reply": "2024-06-04T23:19:32.846267Z"
+        },
+        "id": "a9140b3a"
+      },
+      "outputs": [],
+      "source": [
+        "outer_valid = skm.ShuffleSplit(n_splits=1,\n",
+        "                               test_size=0.25,\n",
+        "                               random_state=1)\n",
+        "inner_cv = skm.KFold(n_splits=5,\n",
+        "                     shuffle=True,\n",
+        "                     random_state=2)\n",
+        "ridgeCV = skl.ElasticNetCV(alphas=lambdas,\n",
+        "                           l1_ratio=0,\n",
+        "                           cv=inner_cv)\n",
+        "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
+        "                         ('ridge', ridgeCV)]);\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "8fc4aea0",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:32.847712Z",
+          "iopub.status.busy": "2024-06-04T23:19:32.847645Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.096196Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.095971Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "8fc4aea0",
+        "outputId": "69127d36-95f5-4d15-c283-2a0a2a138b7d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([132393.84003227])"
+            ]
+          },
+          "execution_count": 42,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "results = skm.cross_validate(pipeCV,\n",
+        "                             X,\n",
+        "                             Y,\n",
+        "                             cv=outer_valid,\n",
+        "                             scoring='neg_mean_squared_error')\n",
+        "-results['test_score']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e27147ec",
+      "metadata": {
+        "id": "e27147ec"
+      },
+      "source": [
+        "    "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d4f126e9",
+      "metadata": {
+        "id": "d4f126e9"
+      },
+      "source": [
+        "### The Lasso\n",
+        "We saw that ridge regression with a wise choice of $\\lambda$ can\n",
+        "outperform least squares as well as the null model on the\n",
+        " `Hitters`  data set. We now ask whether the lasso can yield\n",
+        "either a more accurate or a more interpretable model than ridge\n",
+        "regression. In order to fit a lasso model, we once again use the\n",
+        "`ElasticNetCV()`  function; however, this time we use the argument\n",
+        "`l1_ratio=1`. Other than that change, we proceed just as we did in\n",
+        "fitting a ridge model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "008ecc7c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.097496Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.097429Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.139203Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.138992Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "008ecc7c",
+        "outputId": "4c193041-9048-4f2e-bdea-7840ec2dab73"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "3.1472370031649866"
+            ]
+          },
+          "execution_count": 43,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "lassoCV = skl.ElasticNetCV(n_alphas=100,\n",
+        "                           l1_ratio=1,\n",
+        "                           cv=kfold)\n",
+        "pipeCV = Pipeline(steps=[('scaler', scaler),\n",
+        "                         ('lasso', lassoCV)])\n",
+        "pipeCV.fit(X, Y)\n",
+        "tuned_lasso = pipeCV.named_steps['lasso']\n",
+        "tuned_lasso.alpha_\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1b909861",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.140345Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.140279Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.148367Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.148166Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "1b909861"
+      },
+      "outputs": [],
+      "source": [
+        "lambdas, soln_array = skl.Lasso.path(Xs,\n",
+        "                                    Y,\n",
+        "                                    l1_ratio=1,\n",
+        "                                    n_alphas=100)[:2]\n",
+        "soln_path = pd.DataFrame(soln_array.T,\n",
+        "                         columns=D.columns,\n",
+        "                         index=-np.log(lambdas))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "abec44b3",
+      "metadata": {
+        "id": "abec44b3"
+      },
+      "source": [
+        "We can see from the coefficient plot of the standardized coefficients that depending on the choice of\n",
+        "tuning parameter, some of the coefficients will be exactly equal to\n",
+        "zero."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ff719798",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.149542Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.149477Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.268439Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.268204Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "ff719798",
+        "outputId": "ef9e0bc3-bbbb-49ae-8b07-a5500a4ec828"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "path_fig, ax = subplots(figsize=(8,8))\n",
+        "soln_path.plot(ax=ax, legend=False)\n",
+        "ax.legend(loc='upper left')\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Standardized coefficiients', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "201ccdfb",
+      "metadata": {
+        "id": "201ccdfb"
+      },
+      "source": [
+        "The smallest cross-validated error is lower than the test set MSE of the null model\n",
+        "and of least squares, and very similar to the test MSE of 115526.71 of ridge\n",
+        "regression (page~\\pageref{page:MSECVRidge}) with $\\lambda$ chosen by cross-validation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f21ad8dc",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.269744Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.269654Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.271587Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.271375Z"
+        },
+        "id": "f21ad8dc",
+        "outputId": "44aeb46c-9962-45e8-8a10-44568fd0cd2c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "114690.73118253727"
+            ]
+          },
+          "execution_count": 46,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "np.min(tuned_lasso.mse_path_.mean(1))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "136f171f",
+      "metadata": {
+        "id": "136f171f"
+      },
+      "source": [
+        "Let’s again produce a plot of the cross-validation error.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7624e483",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.272793Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.272706Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.345703Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.345426Z"
+        },
+        "id": "7624e483",
+        "outputId": "24ffb2ea-8246-4719-cf03-1b5157f30ee7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "lassoCV_fig, ax = subplots(figsize=(8,8))\n",
+        "ax.errorbar(-np.log(tuned_lasso.alphas_),\n",
+        "            tuned_lasso.mse_path_.mean(1),\n",
+        "            yerr=tuned_lasso.mse_path_.std(1) / np.sqrt(K))\n",
+        "ax.axvline(-np.log(tuned_lasso.alpha_), c='k', ls='--')\n",
+        "ax.set_ylim([50000,250000])\n",
+        "ax.set_xlabel('$-\\log(\\lambda)$', fontsize=20)\n",
+        "ax.set_ylabel('Cross-validated MSE', fontsize=20);"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "c2b85ea4",
+      "metadata": {
+        "id": "c2b85ea4"
+      },
+      "source": [
+        "However, the lasso has a substantial advantage over ridge regression\n",
+        "in that the resulting coefficient estimates are sparse. Here we see\n",
+        "that 6 of the 19 coefficient estimates are exactly zero. So the lasso\n",
+        "model with $\\lambda$ chosen by cross-validation contains only 13\n",
+        "variables."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b389c7d4",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.347189Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.347081Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.349101Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.348883Z"
+        },
+        "id": "b389c7d4",
+        "outputId": "4b34cd62-8b66-4516-9d51-81dae220cbd3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([-210.01008773,  243.4550306 ,    0.        ,    0.        ,\n",
+              "          0.        ,   97.69397357,  -41.52283116,   -0.        ,\n",
+              "          0.        ,   39.62298193,  205.75273856,  124.55456561,\n",
+              "       -126.29986768,   15.70262427,  -59.50157967,   75.24590036,\n",
+              "         21.62698014,  -12.04423675,   -0.        ])"
+            ]
+          },
+          "execution_count": 48,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tuned_lasso.coef_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "acc5ed38",
+      "metadata": {
+        "id": "acc5ed38"
+      },
+      "source": [
+        "As in ridge regression, we could evaluate the test error\n",
+        "of cross-validated lasso by first splitting into\n",
+        "test and training sets and internally running\n",
+        "cross-validation on the training set. We leave\n",
+        "this as an exercise."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0d0aca26",
+      "metadata": {
+        "id": "0d0aca26"
+      },
+      "source": [
+        "## PCR and PLS Regression\n",
+        "\n",
+        "### Principal Components Regression"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c2120ab5",
+      "metadata": {
+        "id": "c2120ab5"
+      },
+      "source": [
+        "Principal components regression (PCR) can be performed using\n",
+        "`PCA()`  from the `sklearn.decomposition`\n",
+        "module. We now apply PCR to the  `Hitters`  data, in order to\n",
+        "predict `Salary`. Again, ensure that the missing values have\n",
+        "been removed from the data, as described in Section~\\ref{Ch6-varselect-lab:lab-1-subset-selection-methods}.\n",
+        "\n",
+        "We use `LinearRegression()`  to fit the regression model\n",
+        "here. Note that it fits an intercept by default, unlike\n",
+        "the `OLS()` function seen earlier in Section~\\ref{Ch6-varselect-lab:lab-1-subset-selection-methods}."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cf302d4c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.350411Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.350323Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.353307Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.353084Z"
+        },
+        "id": "cf302d4c",
+        "outputId": "33eeb41a-2071-405c-cdb7-37b52f7d923d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([0.09846131, 0.4758765 ])"
+            ]
+          },
+          "execution_count": 49,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pca = PCA(n_components=2)\n",
+        "linreg = skl.LinearRegression()\n",
+        "pipe = Pipeline([('pca', pca),\n",
+        "                 ('linreg', linreg)])\n",
+        "pipe.fit(X, Y)\n",
+        "pipe.named_steps['linreg'].coef_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b909182b",
+      "metadata": {
+        "id": "b909182b"
+      },
+      "source": [
+        "When performing PCA, the results vary depending\n",
+        "on whether the data has been *standardized* or not.\n",
+        "As in the earlier examples, this can be accomplished\n",
+        "by including an additional step in the pipeline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "af4145b6",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.354462Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.354377Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.357347Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.357140Z"
+        },
+        "id": "af4145b6",
+        "outputId": "e7196f2d-b094-47b5-da24-74d7db3d3c06"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([106.36859204,  21.60350456])"
+            ]
+          },
+          "execution_count": 50,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pipe = Pipeline([('scaler', scaler),\n",
+        "                 ('pca', pca),\n",
+        "                 ('linreg', linreg)])\n",
+        "pipe.fit(X, Y)\n",
+        "pipe.named_steps['linreg'].coef_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0da4ae12",
+      "metadata": {
+        "id": "0da4ae12"
+      },
+      "source": [
+        "We can of course use CV to choose the number of components, by\n",
+        "using `skm.GridSearchCV`, in this\n",
+        "case fixing the parameters to vary the\n",
+        "`n_components`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "16e047ed",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.358664Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.358558Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.448710Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.448481Z"
+        },
+        "id": "16e047ed",
+        "outputId": "6878bfea-42dd-4798-c89b-1b7a79d9525f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-6 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-6 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-6 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-6 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-6 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                                       (&#x27;pca&#x27;, PCA(n_components=2)),\n",
+              "                                       (&#x27;linreg&#x27;, LinearRegression())]),\n",
+              "             param_grid={&#x27;pca__n_components&#x27;: range(1, 20)},\n",
+              "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                                       (&#x27;pca&#x27;, PCA(n_components=2)),\n",
+              "                                       (&#x27;linreg&#x27;, LinearRegression())]),\n",
+              "             param_grid={&#x27;pca__n_components&#x27;: range(1, 20)},\n",
+              "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;pca&#x27;, PCA(n_components=17)),\n",
+              "                (&#x27;linreg&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PCA<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PCA(n_components=17)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+              "                                       ('pca', PCA(n_components=2)),\n",
+              "                                       ('linreg', LinearRegression())]),\n",
+              "             param_grid={'pca__n_components': range(1, 20)},\n",
+              "             scoring='neg_mean_squared_error')"
+            ]
+          },
+          "execution_count": 51,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "param_grid = {'pca__n_components': range(1, 20)}\n",
+        "grid = skm.GridSearchCV(pipe,\n",
+        "                        param_grid,\n",
+        "                        cv=kfold,\n",
+        "                        scoring='neg_mean_squared_error')\n",
+        "grid.fit(X, Y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "148bf752",
+      "metadata": {
+        "id": "148bf752"
+      },
+      "source": [
+        "Let’s plot the results as we have for other methods."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "7390847f",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.450064Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.449993Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.518771Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.518504Z"
+        },
+        "id": "7390847f",
+        "outputId": "99379d77-dbbc-4fee-eca5-fb9416973bab"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "pcr_fig, ax = subplots(figsize=(8,8))\n",
+        "n_comp = param_grid['pca__n_components']\n",
+        "ax.errorbar(n_comp,\n",
+        "            -grid.cv_results_['mean_test_score'],\n",
+        "            grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
+        "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
+        "ax.set_xlabel('# principal components', fontsize=20)\n",
+        "ax.set_xticks(n_comp[::2])\n",
+        "ax.set_ylim([50000,250000]);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4269a0db",
+      "metadata": {
+        "id": "4269a0db"
+      },
+      "source": [
+        "We see that the smallest cross-validation error occurs when\n",
+        "17\n",
+        "components are used. However, from the plot we also see that the\n",
+        "cross-validation error is roughly the same when only one component is\n",
+        "included in the model. This suggests that a model that uses just a\n",
+        "small number of components might suffice.\n",
+        "\n",
+        "The CV score is provided for each possible number of components from\n",
+        "1 to 19 inclusive. The `PCA()` method complains\n",
+        "if we try to fit an intercept only with `n_components=0`\n",
+        "so we also compute the MSE for just the null model with\n",
+        "these splits."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1e084cbb",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.520137Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.520059Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.525293Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.525077Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "1e084cbb",
+        "outputId": "4dc57561-ca2e-4ab4-e3f4-0538303d6930"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "204139.30692994667"
+            ]
+          },
+          "execution_count": 53,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "Xn = np.zeros((X.shape[0], 1))\n",
+        "cv_null = skm.cross_validate(linreg,\n",
+        "                             Xn,\n",
+        "                             Y,\n",
+        "                             cv=kfold,\n",
+        "                             scoring='neg_mean_squared_error')\n",
+        "-cv_null['test_score'].mean()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0c0d69df",
+      "metadata": {
+        "id": "0c0d69df"
+      },
+      "source": [
+        "The `explained_variance_ratio_`\n",
+        "attribute of our `PCA` object provides the *percentage of variance explained* in the predictors and in the response using\n",
+        "different numbers of components. This concept is discussed in greater\n",
+        "detail in Section~\\ref{Ch10:sec:pca}."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c7616c02",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.526523Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.526454Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.528563Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.528353Z"
+        },
+        "id": "c7616c02",
+        "outputId": "1241a9a4-f174-4408-f68f-be57d5e5ebee"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([0.3831424 , 0.21841076])"
+            ]
+          },
+          "execution_count": 54,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pipe.named_steps['pca'].explained_variance_ratio_\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a2ee7cb1",
+      "metadata": {
+        "id": "a2ee7cb1"
+      },
+      "source": [
+        "Briefly, we can think of\n",
+        "this as the amount of information about the predictors\n",
+        "that is captured using $M$ principal components. For example, setting\n",
+        "$M=1$ only captures 38.31% of the variance, while $M=2$ captures an additional 21.84%, for a total of 60.15% of the variance.\n",
+        "By  $M=6$ it increases to\n",
+        "88.63%. Beyond this the increments continue to diminish, until we use all $M=p=19$ components, which captures all  100% of the variance.\n",
+        "\n",
+        ""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2a0ce7e4",
+      "metadata": {
+        "id": "2a0ce7e4"
+      },
+      "source": [
+        "### Partial Least Squares\n",
+        "Partial least squares (PLS) is implemented in the\n",
+        "`PLSRegression()`  function.\n",
+        "\n",
+        ""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9c69429e",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.529851Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.529780Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.532907Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.532670Z"
+        },
+        "id": "9c69429e",
+        "outputId": "efd01c81-e588-41e2-9a79-a66777f14ebe"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-7 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-7 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-7 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-7 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-7 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-7 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-7 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PLSRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;PLSRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cross_decomposition.PLSRegression.html\">?<span>Documentation for PLSRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression()</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "PLSRegression()"
+            ]
+          },
+          "execution_count": 55,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pls = PLSRegression(n_components=2,\n",
+        "                    scale=True)\n",
+        "pls.fit(X, Y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a00592ba",
+      "metadata": {
+        "id": "a00592ba"
+      },
+      "source": [
+        "As was the case in PCR, we will want to\n",
+        "use CV to choose the number of components."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "043f27fa",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.534240Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.534173Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.610294Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.610076Z"
+        },
+        "id": "043f27fa",
+        "outputId": "f79cfba6-abc8-4bb4-f66f-8e32a6dfff0e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-8 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: black;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-8 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: block;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-8 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-8 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-8 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 1ex;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-8 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-8 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=PLSRegression(),\n",
+              "             param_grid={&#x27;n_components&#x27;: range(1, 20)},\n",
+              "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=PLSRegression(),\n",
+              "             param_grid={&#x27;n_components&#x27;: range(1, 20)},\n",
+              "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: PLSRegression</label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression(n_components=12)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PLSRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.cross_decomposition.PLSRegression.html\">?<span>Documentation for PLSRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PLSRegression(n_components=12)</pre></div> </div></div></div></div></div></div></div></div></div>"
+            ],
+            "text/plain": [
+              "GridSearchCV(cv=KFold(n_splits=5, random_state=0, shuffle=True),\n",
+              "             estimator=PLSRegression(),\n",
+              "             param_grid={'n_components': range(1, 20)},\n",
+              "             scoring='neg_mean_squared_error')"
+            ]
+          },
+          "execution_count": 56,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "param_grid = {'n_components':range(1, 20)}\n",
+        "grid = skm.GridSearchCV(pls,\n",
+        "                        param_grid,\n",
+        "                        cv=kfold,\n",
+        "                        scoring='neg_mean_squared_error')\n",
+        "grid.fit(X, Y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c1bc745c",
+      "metadata": {
+        "id": "c1bc745c"
+      },
+      "source": [
+        "As for our other methods, we plot the MSE."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b702fb04",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:33.611587Z",
+          "iopub.status.busy": "2024-06-04T23:19:33.611516Z",
+          "iopub.status.idle": "2024-06-04T23:19:33.678469Z",
+          "shell.execute_reply": "2024-06-04T23:19:33.678245Z"
+        },
+        "id": "b702fb04",
+        "outputId": "693696fe-2e04-492a-80ed-3ef8c51b5afd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "pls_fig, ax = subplots(figsize=(8,8))\n",
+        "n_comp = param_grid['n_components']\n",
+        "ax.errorbar(n_comp,\n",
+        "            -grid.cv_results_['mean_test_score'],\n",
+        "            grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
+        "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
+        "ax.set_xlabel('# principal components', fontsize=20)\n",
+        "ax.set_xticks(n_comp[::2])\n",
+        "ax.set_ylim([50000,250000]);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fc9ec466",
+      "metadata": {
+        "id": "fc9ec466"
+      },
+      "source": [
+        "CV error is minimized at 12,\n",
+        "though there is little noticable difference between this point and a much lower number like 2 or 3 components.\n",
+        "\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
-   ],
-   "source": [
-    "pls_fig, ax = subplots(figsize=(8,8))\n",
-    "n_comp = param_grid['n_components']\n",
-    "ax.errorbar(n_comp,\n",
-    "            -grid.cv_results_['mean_test_score'],\n",
-    "            grid.cv_results_['std_test_score'] / np.sqrt(K))\n",
-    "ax.set_ylabel('Cross-validated MSE', fontsize=20)\n",
-    "ax.set_xlabel('# principal components', fontsize=20)\n",
-    "ax.set_xticks(n_comp[::2])\n",
-    "ax.set_ylim([50000,250000]);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc9ec466",
-   "metadata": {},
-   "source": [
-    "CV error is minimized at 12,\n",
-    "though there is little noticable difference between this point and a much lower number like 2 or 3 components.\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
+  ],
+  "metadata": {
+    "jupytext": {
+      "cell_metadata_filter": "-all",
+      "main_language": "python",
+      "notebook_metadata_filter": "-all"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/Ch07-nonlin-lab.ipynb b/Ch07-nonlin-lab.ipynb
index 7534f6dc..4884963b 100644
--- a/Ch07-nonlin-lab.ipynb
+++ b/Ch07-nonlin-lab.ipynb
@@ -1,3263 +1,6057 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "73b6c8c9",
-   "metadata": {},
-   "source": [
-    "# Non-Linear Modeling\n",
-    "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch07-nonlin-lab.ipynb\">\n",
-    "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-    "</a>\n",
-    "\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch07-nonlin-lab.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c645659",
-   "metadata": {},
-   "source": [
-    "In this lab, we demonstrate some of the nonlinear models discussed in\n",
-    "this chapter. We use the `Wage`  data as a running example, and show that many of the complex non-linear fitting procedures discussed can easily be implemented in `Python`.\n",
-    "\n",
-    "As usual, we start with some of our standard imports."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "4f8339f1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:34.911110Z",
-     "iopub.status.busy": "2024-06-04T23:19:34.910732Z",
-     "iopub.status.idle": "2024-06-04T23:19:35.700317Z",
-     "shell.execute_reply": "2024-06-04T23:19:35.700028Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np, pandas as pd\n",
-    "from matplotlib.pyplot import subplots\n",
-    "import statsmodels.api as sm\n",
-    "from ISLP import load_data\n",
-    "from ISLP.models import (summarize,\n",
-    "                         poly,\n",
-    "                         ModelSpec as MS)\n",
-    "from statsmodels.stats.anova import anova_lm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e8fa3d7b",
-   "metadata": {},
-   "source": [
-    "We again collect the new imports\n",
-    "needed for this lab. Many of these are developed specifically for the\n",
-    "`ISLP` package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "816d4b81",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:35.702094Z",
-     "iopub.status.busy": "2024-06-04T23:19:35.701965Z",
-     "iopub.status.idle": "2024-06-04T23:19:35.722229Z",
-     "shell.execute_reply": "2024-06-04T23:19:35.722033Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from pygam import (s as s_gam,\n",
-    "                   l as l_gam,\n",
-    "                   f as f_gam,\n",
-    "                   LinearGAM,\n",
-    "                   LogisticGAM)\n",
-    "\n",
-    "from ISLP.transforms import (BSpline,\n",
-    "                             NaturalSpline)\n",
-    "from ISLP.models import bs, ns\n",
-    "from ISLP.pygam import (approx_lam,\n",
-    "                        degrees_of_freedom,\n",
-    "                        plot as plot_gam,\n",
-    "                        anova as anova_gam)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da7fd8b8",
-   "metadata": {},
-   "source": [
-    "## Polynomial Regression and Step Functions\n",
-    "We start by demonstrating how Figure~\\ref{Ch7:fig:poly} can be reproduced.\n",
-    "Let's  begin by loading the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "bd30b197",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:35.723538Z",
-     "iopub.status.busy": "2024-06-04T23:19:35.723455Z",
-     "iopub.status.idle": "2024-06-04T23:19:35.730561Z",
-     "shell.execute_reply": "2024-06-04T23:19:35.730367Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "Wage = load_data('Wage')\n",
-    "y = Wage['wage']\n",
-    "age = Wage['age']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2539b250",
-   "metadata": {},
-   "source": [
-    "Throughout most of this lab, our response is `Wage['wage']`, which\n",
-    "we have stored as `y` above. \n",
-    "As in Section~\\ref{Ch3-linreg-lab:non-linear-transformations-of-the-predictors}, we will use the `poly()` function to create a model matrix\n",
-    "that will fit a $4$th degree polynomial in `age`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "fe40e0fb",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:35.731830Z",
-     "iopub.status.busy": "2024-06-04T23:19:35.731745Z",
-     "iopub.status.idle": "2024-06-04T23:19:35.758719Z",
-     "shell.execute_reply": "2024-06-04T23:19:35.758161Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coef</th>\n",
-       "      <th>std err</th>\n",
-       "      <th>t</th>\n",
-       "      <th>P&gt;|t|</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>intercept</th>\n",
-       "      <td>111.7036</td>\n",
-       "      <td>0.729</td>\n",
-       "      <td>153.283</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>poly(age, degree=4)[0]</th>\n",
-       "      <td>447.0679</td>\n",
-       "      <td>39.915</td>\n",
-       "      <td>11.201</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>poly(age, degree=4)[1]</th>\n",
-       "      <td>-478.3158</td>\n",
-       "      <td>39.915</td>\n",
-       "      <td>-11.983</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>poly(age, degree=4)[2]</th>\n",
-       "      <td>125.5217</td>\n",
-       "      <td>39.915</td>\n",
-       "      <td>3.145</td>\n",
-       "      <td>0.002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>poly(age, degree=4)[3]</th>\n",
-       "      <td>-77.9112</td>\n",
-       "      <td>39.915</td>\n",
-       "      <td>-1.952</td>\n",
-       "      <td>0.051</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/bertramwooster/ISLP_labs/blob/new_branch/Ch07-nonlin-lab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "73b6c8c9",
+      "metadata": {
+        "id": "73b6c8c9"
+      },
+      "source": [
+        "# Non-Linear Modeling\n",
+        "\n",
+        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch07-nonlin-lab.ipynb\">\n",
+        "<img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+        "</a>\n",
+        "\n",
+        "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intro-stat-learning/ISLP_labs/v2.2?labpath=Ch07-nonlin-lab.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0c645659",
+      "metadata": {
+        "id": "0c645659"
+      },
+      "source": [
+        "In this lab, we demonstrate some of the nonlinear models discussed in\n",
+        "this chapter. We use the `Wage`  data as a running example, and show that many of the complex non-linear fitting procedures discussed can easily be implemented in `Python`.\n",
+        "\n",
+        "As usual, we start with some of our standard imports."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "4f8339f1",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:34.911110Z",
+          "iopub.status.busy": "2024-06-04T23:19:34.910732Z",
+          "iopub.status.idle": "2024-06-04T23:19:35.700317Z",
+          "shell.execute_reply": "2024-06-04T23:19:35.700028Z"
+        },
+        "id": "4f8339f1"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np, pandas as pd\n",
+        "from matplotlib.pyplot import subplots\n",
+        "import statsmodels.api as sm\n",
+        "from statsmodels.stats.anova import anova_lm"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install ISLP"
       ],
-      "text/plain": [
-       "                            coef  std err        t  P>|t|\n",
-       "intercept               111.7036    0.729  153.283  0.000\n",
-       "poly(age, degree=4)[0]  447.0679   39.915   11.201  0.000\n",
-       "poly(age, degree=4)[1] -478.3158   39.915  -11.983  0.000\n",
-       "poly(age, degree=4)[2]  125.5217   39.915    3.145  0.002\n",
-       "poly(age, degree=4)[3]  -77.9112   39.915   -1.952  0.051"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "poly_age = MS([poly('age', degree=4)]).fit(Wage)\n",
-    "M = sm.OLS(y, poly_age.transform(Wage)).fit()\n",
-    "summarize(M)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be02ce11",
-   "metadata": {},
-   "source": [
-    "This polynomial is constructed using the function `poly()`,\n",
-    "which creates\n",
-    "a special *transformer* `Poly()` (using `sklearn` terminology\n",
-    "for feature transformations such as `PCA()` seen in Section \\ref{Ch6-varselect-lab:principal-components-regression}) which\n",
-    "allows for easy evaluation of the polynomial at new data points. Here `poly()` is referred to as a *helper* function, and sets up the transformation; `Poly()` is the actual workhorse that computes the transformation. See also \n",
-    "the \n",
-    "discussion of transformations on\n",
-    "page~\\pageref{Ch3-linreg-lab:using-transformations-fit-and-transform}. \n",
-    "\n",
-    "In the code above, the first line executes the `fit()` method\n",
-    "using the dataframe\n",
-    "`Wage`. This recomputes and stores as attributes any parameters needed by `Poly()`\n",
-    "on the training data, and these will be used on all subsequent\n",
-    "evaluations of the `transform()` method. For example, it is used\n",
-    "on the second line, as well as in the plotting function developed below.\n",
-    "\n",
-    "\n",
-    "    \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "64316183",
-   "metadata": {},
-   "source": [
-    "We now create a grid of values for `age` at which we want\n",
-    "predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "23a9ff8e",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:35.762173Z",
-     "iopub.status.busy": "2024-06-04T23:19:35.761857Z",
-     "iopub.status.idle": "2024-06-04T23:19:35.766441Z",
-     "shell.execute_reply": "2024-06-04T23:19:35.765894Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "age_grid = np.linspace(age.min(),\n",
-    "                       age.max(),\n",
-    "                       100)\n",
-    "age_df = pd.DataFrame({'age': age_grid})\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c10f12d4",
-   "metadata": {},
-   "source": [
-    "Finally, we wish to plot the data and add the fit from the fourth-degree polynomial. As we will make\n",
-    "several similar plots below, we first write a function\n",
-    "to create all the ingredients and produce the plot.  Our function\n",
-    "takes in a model specification (here a basis specified by a\n",
-    "transform), as well as a grid of `age` values. The function\n",
-    "produces a fitted curve as well as 95% confidence bands. By using\n",
-    "an argument for `basis` we can produce and plot the results with several different\n",
-    "transforms, such as the splines we will see shortly. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ca7a1d45",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:35.771011Z",
-     "iopub.status.busy": "2024-06-04T23:19:35.770581Z",
-     "iopub.status.idle": "2024-06-04T23:19:35.776229Z",
-     "shell.execute_reply": "2024-06-04T23:19:35.775762Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "def plot_wage_fit(age_df, \n",
-    "                  basis,\n",
-    "                  title):\n",
-    "\n",
-    "    X = basis.transform(Wage)\n",
-    "    Xnew = basis.transform(age_df)\n",
-    "    M = sm.OLS(y, X).fit()\n",
-    "    preds = M.get_prediction(Xnew)\n",
-    "    bands = preds.conf_int(alpha=0.05)\n",
-    "    fig, ax = subplots(figsize=(8,8))\n",
-    "    ax.scatter(age,\n",
-    "               y,\n",
-    "               facecolor='gray',\n",
-    "               alpha=0.5)\n",
-    "    for val, ls in zip([preds.predicted_mean,\n",
-    "                      bands[:,0],\n",
-    "                      bands[:,1]],\n",
-    "                     ['b','r--','r--']):\n",
-    "        ax.plot(age_df.values, val, ls, linewidth=3)\n",
-    "    ax.set_title(title, fontsize=20)\n",
-    "    ax.set_xlabel('Age', fontsize=20)\n",
-    "    ax.set_ylabel('Wage', fontsize=20);\n",
-    "    return ax\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "14d6a540",
-   "metadata": {},
-   "source": [
-    "We include an argument `alpha` to `ax.scatter()`\n",
-    "to add some transparency to the points. This provides a visual indication\n",
-    "of density. Notice the use of the `zip()` function in the\n",
-    "`for` loop above (see Section~\\ref{Ch2-statlearn-lab:for-loops}).\n",
-    "We have three lines to plot, each with different colors and line\n",
-    "types. Here `zip()` conveniently bundles these together as\n",
-    "iterators in the loop. {In `Python`{} speak, an \"iterator\" is an object with a finite number of values, that can be iterated on, as in a loop.}\n",
-    "\n",
-    "We now plot the fit of the fourth-degree polynomial using this\n",
-    "function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "b05a60ba",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:35.779177Z",
-     "iopub.status.busy": "2024-06-04T23:19:35.778961Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.009418Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.008910Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_wage_fit(age_df, \n",
-    "              poly_age,\n",
-    "              'Degree-4 Polynomial');\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "648104d9",
-   "metadata": {},
-   "source": [
-    " \n",
-    "\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eed651b8",
-   "metadata": {},
-   "source": [
-    "With  polynomial regression we must decide on the degree of\n",
-    "the polynomial to use. Sometimes we just wing it, and decide to use\n",
-    "second or third degree polynomials, simply to obtain a nonlinear fit. But we can\n",
-    "make such a decision in a more systematic way. One way to do this is through hypothesis\n",
-    "tests, which we demonstrate here. We now fit a series of models ranging from\n",
-    "linear (degree-one) to degree-five polynomials,\n",
-    "and look to determine the simplest model that is sufficient to\n",
-    "explain the relationship between `wage` and `age`. We use the\n",
-    "`anova_lm()`  function, which performs a series of ANOVA\n",
-    "tests.\n",
-    "An \\emph{analysis of\n",
-    "  variance}  or ANOVA tests the null\n",
-    "hypothesis that a model $\\mathcal{M}_1$ is sufficient to explain the\n",
-    "data against the alternative hypothesis that a more complex model\n",
-    "$\\mathcal{M}_2$ is required. The determination is based on an F-test.\n",
-    "To perform the test, the models  $\\mathcal{M}_1$ and $\\mathcal{M}_2$ must be *nested*:\n",
-    "the space spanned by the predictors in $\\mathcal{M}_1$ must be a subspace of the\n",
-    "space spanned by the predictors in $\\mathcal{M}_2$. In this case, we\n",
-    "fit five different polynomial\n",
-    "models and sequentially compare the simpler model to the more complex\n",
-    "model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c338a92a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.012673Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.012313Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.072501Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.071990Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>df_resid</th>\n",
-       "      <th>ssr</th>\n",
-       "      <th>df_diff</th>\n",
-       "      <th>ss_diff</th>\n",
-       "      <th>F</th>\n",
-       "      <th>Pr(&gt;F)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <th>1</th>\n",
-       "      <td>2997.0</td>\n",
-       "      <td>4.793430e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>228786.010128</td>\n",
-       "      <td>143.593107</td>\n",
-       "      <td>2.363850e-32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2996.0</td>\n",
-       "      <td>4.777674e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>15755.693664</td>\n",
-       "      <td>9.888756</td>\n",
-       "      <td>1.679202e-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2995.0</td>\n",
-       "      <td>4.771604e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6070.152124</td>\n",
-       "      <td>3.809813</td>\n",
-       "      <td>5.104620e-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2994.0</td>\n",
-       "      <td>4.770322e+06</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1282.563017</td>\n",
-       "      <td>0.804976</td>\n",
-       "      <td>3.696820e-01</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "metadata": {
+        "collapsed": true,
+        "id": "lgH0836hoZqu",
+        "outputId": "2a10ed0b-67b1-4926-f7bb-da17c312d8c2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "lgH0836hoZqu",
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting ISLP\n",
+            "  Downloading ISLP-0.4.0-py3-none-any.whl.metadata (7.0 kB)\n",
+            "Requirement already satisfied: numpy>=1.7.1 in /usr/local/lib/python3.12/dist-packages (from ISLP) (2.0.2)\n",
+            "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.12/dist-packages (from ISLP) (1.16.2)\n",
+            "Requirement already satisfied: pandas>=0.20 in /usr/local/lib/python3.12/dist-packages (from ISLP) (2.2.2)\n",
+            "Requirement already satisfied: lxml in /usr/local/lib/python3.12/dist-packages (from ISLP) (5.4.0)\n",
+            "Requirement already satisfied: scikit-learn>=1.2 in /usr/local/lib/python3.12/dist-packages (from ISLP) (1.6.1)\n",
+            "Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from ISLP) (1.5.2)\n",
+            "Requirement already satisfied: statsmodels>=0.13 in /usr/local/lib/python3.12/dist-packages (from ISLP) (0.14.5)\n",
+            "Collecting lifelines (from ISLP)\n",
+            "  Downloading lifelines-0.30.0-py3-none-any.whl.metadata (3.2 kB)\n",
+            "Collecting pygam (from ISLP)\n",
+            "  Downloading pygam-0.10.1-py3-none-any.whl.metadata (9.7 kB)\n",
+            "Requirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (from ISLP) (2.8.0+cu126)\n",
+            "Collecting pytorch-lightning (from ISLP)\n",
+            "  Downloading pytorch_lightning-2.5.5-py3-none-any.whl.metadata (20 kB)\n",
+            "Collecting torchmetrics (from ISLP)\n",
+            "  Downloading torchmetrics-1.8.2-py3-none-any.whl.metadata (22 kB)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas>=0.20->ISLP) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas>=0.20->ISLP) (2025.2)\n",
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+            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=1.2->ISLP) (3.6.0)\n",
+            "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.12/dist-packages (from statsmodels>=0.13->ISLP) (1.0.2)\n",
+            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.12/dist-packages (from statsmodels>=0.13->ISLP) (25.0)\n",
+            "Requirement already satisfied: matplotlib>=3.0 in /usr/local/lib/python3.12/dist-packages (from lifelines->ISLP) (3.10.0)\n",
+            "Requirement already satisfied: autograd>=1.5 in /usr/local/lib/python3.12/dist-packages (from lifelines->ISLP) (1.8.0)\n",
+            "Collecting autograd-gamma>=0.3 (from lifelines->ISLP)\n",
+            "  Downloading autograd-gamma-0.5.0.tar.gz (4.0 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting formulaic>=0.2.2 (from lifelines->ISLP)\n",
+            "  Downloading formulaic-1.2.1-py3-none-any.whl.metadata (7.0 kB)\n",
+            "Requirement already satisfied: progressbar2<5,>=4.2.0 in /usr/local/lib/python3.12/dist-packages (from pygam->ISLP) (4.5.0)\n",
+            "Requirement already satisfied: tqdm>=4.57.0 in /usr/local/lib/python3.12/dist-packages (from pytorch-lightning->ISLP) (4.67.1)\n",
+            "Requirement already satisfied: PyYAML>5.4 in /usr/local/lib/python3.12/dist-packages (from pytorch-lightning->ISLP) (6.0.3)\n",
+            "Requirement already satisfied: fsspec>=2022.5.0 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]>=2022.5.0->pytorch-lightning->ISLP) (2025.3.0)\n",
+            "Requirement already satisfied: typing-extensions>4.5.0 in /usr/local/lib/python3.12/dist-packages (from pytorch-lightning->ISLP) (4.15.0)\n",
+            "Collecting lightning-utilities>=0.10.0 (from pytorch-lightning->ISLP)\n",
+            "  Downloading lightning_utilities-0.15.2-py3-none-any.whl.metadata (5.7 kB)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (3.20.0)\n",
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+            "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (1.13.3)\n",
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+            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (12.6.77)\n",
+            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (12.6.77)\n",
+            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (12.6.80)\n",
+            "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (9.10.2.21)\n",
+            "Requirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (12.6.4.1)\n",
+            "Requirement already satisfied: nvidia-cufft-cu12==11.3.0.4 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (11.3.0.4)\n",
+            "Requirement already satisfied: nvidia-curand-cu12==10.3.7.77 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (10.3.7.77)\n",
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+            "Requirement already satisfied: nvidia-nvtx-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (12.6.77)\n",
+            "Requirement already satisfied: nvidia-nvjitlink-cu12==12.6.85 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (12.6.85)\n",
+            "Requirement already satisfied: nvidia-cufile-cu12==1.11.1.6 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (1.11.1.6)\n",
+            "Requirement already satisfied: triton==3.4.0 in /usr/local/lib/python3.12/dist-packages (from torch->ISLP) (3.4.0)\n",
+            "Collecting interface-meta>=1.2.0 (from formulaic>=0.2.2->lifelines->ISLP)\n",
+            "  Downloading interface_meta-1.3.0-py3-none-any.whl.metadata (6.7 kB)\n",
+            "Requirement already satisfied: narwhals>=1.17 in /usr/local/lib/python3.12/dist-packages (from formulaic>=0.2.2->lifelines->ISLP) (2.9.0)\n",
+            "Requirement already satisfied: wrapt>=1.0 in /usr/local/lib/python3.12/dist-packages (from formulaic>=0.2.2->lifelines->ISLP) (2.0.0)\n",
+            "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]>=2022.5.0->pytorch-lightning->ISLP) (3.13.1)\n",
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+            "Downloading ISLP-0.4.0-py3-none-any.whl (3.6 MB)\n",
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+            "\u001b[?25hDownloading pytorch_lightning-2.5.5-py3-none-any.whl (832 kB)\n",
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+            "\u001b[?25hDownloading torchmetrics-1.8.2-py3-none-any.whl (983 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m983.2/983.2 kB\u001b[0m \u001b[31m53.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading formulaic-1.2.1-py3-none-any.whl (117 kB)\n",
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+            "\u001b[?25hDownloading lightning_utilities-0.15.2-py3-none-any.whl (29 kB)\n",
+            "Downloading interface_meta-1.3.0-py3-none-any.whl (14 kB)\n",
+            "Building wheels for collected packages: autograd-gamma\n",
+            "  Building wheel for autograd-gamma (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for autograd-gamma: filename=autograd_gamma-0.5.0-py3-none-any.whl size=4030 sha256=152eeab22c27b23165d1b0ffda34448b5bd45245c43eb8ade2d078922cfbf00a\n",
+            "  Stored in directory: /root/.cache/pip/wheels/50/37/21/0a719b9d89c635e89ff24bd93b862882ad675279552013b2fb\n",
+            "Successfully built autograd-gamma\n",
+            "Installing collected packages: lightning-utilities, interface-meta, autograd-gamma, pygam, formulaic, torchmetrics, lifelines, pytorch-lightning, ISLP\n",
+            "Successfully installed ISLP-0.4.0 autograd-gamma-0.5.0 formulaic-1.2.1 interface-meta-1.3.0 lifelines-0.30.0 lightning-utilities-0.15.2 pygam-0.10.1 pytorch-lightning-2.5.5 torchmetrics-1.8.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from ISLP import load_data\n",
+        "from ISLP.models import (summarize,\n",
+        "                         poly,\n",
+        "                         ModelSpec as MS)"
       ],
-      "text/plain": [
-       "   df_resid           ssr  df_diff        ss_diff           F        Pr(>F)\n",
-       "0    2998.0  5.022216e+06      0.0            NaN         NaN           NaN\n",
-       "1    2997.0  4.793430e+06      1.0  228786.010128  143.593107  2.363850e-32\n",
-       "2    2996.0  4.777674e+06      1.0   15755.693664    9.888756  1.679202e-03\n",
-       "3    2995.0  4.771604e+06      1.0    6070.152124    3.809813  5.104620e-02\n",
-       "4    2994.0  4.770322e+06      1.0    1282.563017    0.804976  3.696820e-01"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models = [MS([poly('age', degree=d)]) \n",
-    "          for d in range(1, 6)]\n",
-    "Xs = [model.fit_transform(Wage) for model in models]\n",
-    "anova_lm(*[sm.OLS(y, X_).fit()\n",
-    "           for X_ in Xs])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8a8c2c29",
-   "metadata": {},
-   "source": [
-    "Notice the `*` in the `anova_lm()` line above. This\n",
-    "function takes a variable number of non-keyword arguments, in this case fitted models.\n",
-    "When these models are provided as a list (as is done here), it must be \n",
-    "prefixed by `*`.\n",
-    "\n",
-    "The p-value comparing the linear `models[0]` to the quadratic\n",
-    "`models[1]` is essentially zero, indicating that a linear\n",
-    "fit is not sufficient. {Indexing starting at zero is confusing for the polynomial degree example, since `models[1]` is quadratic rather than linear!} Similarly the p-value comparing the quadratic\n",
-    "`models[1]` to the cubic `models[2]` is very low (0.0017), so the\n",
-    "quadratic fit is also insufficient. The p-value comparing the cubic\n",
-    "and degree-four polynomials, `models[2]` and `models[3]`, is\n",
-    "approximately 5%, while the degree-five polynomial `models[4]` seems\n",
-    "unnecessary because its p-value is 0.37. Hence, either a cubic or a\n",
-    "quartic polynomial appear to provide a reasonable fit to the data, but\n",
-    "lower- or higher-order models are not justified.\n",
-    "\n",
-    "In this case, instead of using the `anova()`  function, we could\n",
-    "have obtained these p-values more succinctly by exploiting the fact\n",
-    "that `poly()`  creates orthogonal polynomials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "a1f74b79",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.075597Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.075369Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.090416Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.089945Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coef</th>\n",
-       "      <th>std err</th>\n",
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-       "      <th>P&gt;|t|</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>intercept</th>\n",
-       "      <td>111.7036</td>\n",
-       "      <td>0.729</td>\n",
-       "      <td>153.283</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>poly(age, degree=4)[0]</th>\n",
-       "      <td>447.0679</td>\n",
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-       "      <td>39.915</td>\n",
-       "      <td>-11.983</td>\n",
-       "      <td>0.000</td>\n",
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-       "    <tr>\n",
-       "      <th>poly(age, degree=4)[2]</th>\n",
-       "      <td>125.5217</td>\n",
-       "      <td>39.915</td>\n",
-       "      <td>3.145</td>\n",
-       "      <td>0.002</td>\n",
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-       "    <tr>\n",
-       "      <th>poly(age, degree=4)[3]</th>\n",
-       "      <td>-77.9112</td>\n",
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-       "</div>"
+      "metadata": {
+        "id": "D5tOMsWAoJFg"
+      },
+      "id": "D5tOMsWAoJFg",
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e8fa3d7b",
+      "metadata": {
+        "id": "e8fa3d7b"
+      },
+      "source": [
+        "We again collect the new imports\n",
+        "needed for this lab. Many of these are developed specifically for the\n",
+        "`ISLP` package."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "816d4b81",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:35.702094Z",
+          "iopub.status.busy": "2024-06-04T23:19:35.701965Z",
+          "iopub.status.idle": "2024-06-04T23:19:35.722229Z",
+          "shell.execute_reply": "2024-06-04T23:19:35.722033Z"
+        },
+        "id": "816d4b81"
+      },
+      "outputs": [],
+      "source": [
+        "from pygam import (s as s_gam,\n",
+        "                   l as l_gam,\n",
+        "                   f as f_gam,\n",
+        "                   LinearGAM,\n",
+        "                   LogisticGAM)\n",
+        "\n",
+        "from ISLP.transforms import (BSpline,\n",
+        "                             NaturalSpline)\n",
+        "from ISLP.models import bs, ns\n",
+        "from ISLP.pygam import (approx_lam,\n",
+        "                        degrees_of_freedom,\n",
+        "                        plot as plot_gam,\n",
+        "                        anova as anova_gam)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "da7fd8b8",
+      "metadata": {
+        "id": "da7fd8b8"
+      },
+      "source": [
+        "## Polynomial Regression and Step Functions\n",
+        "We start by demonstrating how Figure~\\ref{Ch7:fig:poly} can be reproduced.\n",
+        "Let's  begin by loading the data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "bd30b197",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:35.723538Z",
+          "iopub.status.busy": "2024-06-04T23:19:35.723455Z",
+          "iopub.status.idle": "2024-06-04T23:19:35.730561Z",
+          "shell.execute_reply": "2024-06-04T23:19:35.730367Z"
+        },
+        "id": "bd30b197"
+      },
+      "outputs": [],
+      "source": [
+        "Wage = load_data('Wage')\n",
+        "y = Wage['wage']\n",
+        "age = Wage['age']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2539b250",
+      "metadata": {
+        "id": "2539b250"
+      },
+      "source": [
+        "Throughout most of this lab, our response is `Wage['wage']`, which\n",
+        "we have stored as `y` above.\n",
+        "As in Section~\\ref{Ch3-linreg-lab:non-linear-transformations-of-the-predictors}, we will use the `poly()` function to create a model matrix\n",
+        "that will fit a $4$th degree polynomial in `age`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "fe40e0fb",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:35.731830Z",
+          "iopub.status.busy": "2024-06-04T23:19:35.731745Z",
+          "iopub.status.idle": "2024-06-04T23:19:35.758719Z",
+          "shell.execute_reply": "2024-06-04T23:19:35.758161Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "fe40e0fb",
+        "outputId": "30e89fa2-3346-42ab-e9dd-ee982c8654a2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                            coef  std err        t  P>|t|\n",
+              "intercept               111.7036    0.729  153.283  0.000\n",
+              "poly(age, degree=4)[0]  447.0679   39.915   11.201  0.000\n",
+              "poly(age, degree=4)[1] -478.3158   39.915  -11.983  0.000\n",
+              "poly(age, degree=4)[2]  125.5217   39.915    3.145  0.002\n",
+              "poly(age, degree=4)[3]  -77.9112   39.915   -1.952  0.051"
+            ],
+            "text/html": [
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+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>coef</th>\n",
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+              "  <tbody>\n",
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+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(age, degree=4)[0]</th>\n",
+              "      <td>447.0679</td>\n",
+              "      <td>39.915</td>\n",
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+              "    <tr>\n",
+              "      <th>poly(age, degree=4)[1]</th>\n",
+              "      <td>-478.3158</td>\n",
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+              "      <td>-11.983</td>\n",
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+              "      <th>poly(age, degree=4)[2]</th>\n",
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+              "      <td>3.145</td>\n",
+              "      <td>0.002</td>\n",
+              "    </tr>\n",
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+              "  <style>\n",
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+              "      display:flex;\n",
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+              "\n",
+              "    .colab-df-convert {\n",
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+              "\n",
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+              "\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
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+              "        const element = document.querySelector('#df-cf41b225-1b3a-4a66-8a81-570d17fe1b70');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "</svg>\n",
+              "      </button>\n",
+              "\n",
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+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
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+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
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+              "      spin 1s steps(1) infinite;\n",
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+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
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+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "      </script>\n",
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+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"summarize(M)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"coef\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 338.9981813862915,\n        \"min\": -478.3158,\n        \"max\": 447.0679,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          447.0679,\n          -77.9112,\n          -478.3158\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std err\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.52451195326135,\n        \"min\": 0.729,\n        \"max\": 39.915,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          39.915,\n          0.729\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"t\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69.01823125232927,\n        \"min\": -11.983,\n        \"max\": 153.283,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          11.201,\n          -1.952\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P>|t|\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.022600884938426637,\n        \"min\": 0.0,\n        \"max\": 0.051,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
       ],
-      "text/plain": [
-       "                            coef  std err        t  P>|t|\n",
-       "intercept               111.7036    0.729  153.283  0.000\n",
-       "poly(age, degree=4)[0]  447.0679   39.915   11.201  0.000\n",
-       "poly(age, degree=4)[1] -478.3158   39.915  -11.983  0.000\n",
-       "poly(age, degree=4)[2]  125.5217   39.915    3.145  0.002\n",
-       "poly(age, degree=4)[3]  -77.9112   39.915   -1.952  0.051"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summarize(M)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea22f1d6",
-   "metadata": {},
-   "source": [
-    "Notice that the p-values are the same, and in fact the square of\n",
-    "the  t-statistics are equal to the F-statistics from the\n",
-    "`anova_lm()`  function;  for example: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "570892d1",
-   "metadata": {
-    "execution": {
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-    {
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-      "text/plain": [
-       "143.59228900000002"
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-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
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-   "source": [
-    "(-11.983)**2\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dcb5ee4d",
-   "metadata": {},
-   "source": [
-    "However, the ANOVA method works whether or not we used orthogonal\n",
-    "polynomials, provided the models are nested. For example, we can use\n",
-    "`anova_lm()`  to compare the following three\n",
-    "models, which all have a linear term in `education` and a\n",
-    "polynomial in `age` of different degrees:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "3ff20916",
-   "metadata": {
-    "execution": {
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+      "source": [
+        "poly_age = MS([poly('age', degree=4)]).fit(Wage)\n",
+        "M = sm.OLS(y, poly_age.transform(Wage)).fit()\n",
+        "summarize(M)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "be02ce11",
+      "metadata": {
+        "id": "be02ce11"
+      },
+      "source": [
+        "This polynomial is constructed using the function `poly()`,\n",
+        "which creates\n",
+        "a special *transformer* `Poly()` (using `sklearn` terminology\n",
+        "for feature transformations such as `PCA()` seen in Section \\ref{Ch6-varselect-lab:principal-components-regression}) which\n",
+        "allows for easy evaluation of the polynomial at new data points. Here `poly()` is referred to as a *helper* function, and sets up the transformation; `Poly()` is the actual workhorse that computes the transformation. See also\n",
+        "the\n",
+        "discussion of transformations on\n",
+        "page~\\pageref{Ch3-linreg-lab:using-transformations-fit-and-transform}.\n",
+        "\n",
+        "In the code above, the first line executes the `fit()` method\n",
+        "using the dataframe\n",
+        "`Wage`. This recomputes and stores as attributes any parameters needed by `Poly()`\n",
+        "on the training data, and these will be used on all subsequent\n",
+        "evaluations of the `transform()` method. For example, it is used\n",
+        "on the second line, as well as in the plotting function developed below.\n",
+        "\n",
+        "\n",
+        "    \n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "64316183",
+      "metadata": {
+        "id": "64316183"
+      },
+      "source": [
+        "We now create a grid of values for `age` at which we want\n",
+        "predictions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "23a9ff8e",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:35.762173Z",
+          "iopub.status.busy": "2024-06-04T23:19:35.761857Z",
+          "iopub.status.idle": "2024-06-04T23:19:35.766441Z",
+          "shell.execute_reply": "2024-06-04T23:19:35.765894Z"
+        },
+        "id": "23a9ff8e"
+      },
+      "outputs": [],
+      "source": [
+        "age_grid = np.linspace(age.min(),\n",
+        "                       age.max(),\n",
+        "                       100)\n",
+        "age_df = pd.DataFrame({'age': age_grid})\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c10f12d4",
+      "metadata": {
+        "id": "c10f12d4"
+      },
+      "source": [
+        "Finally, we wish to plot the data and add the fit from the fourth-degree polynomial. As we will make\n",
+        "several similar plots below, we first write a function\n",
+        "to create all the ingredients and produce the plot.  Our function\n",
+        "takes in a model specification (here a basis specified by a\n",
+        "transform), as well as a grid of `age` values. The function\n",
+        "produces a fitted curve as well as 95% confidence bands. By using\n",
+        "an argument for `basis` we can produce and plot the results with several different\n",
+        "transforms, such as the splines we will see shortly."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "ca7a1d45",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:35.771011Z",
+          "iopub.status.busy": "2024-06-04T23:19:35.770581Z",
+          "iopub.status.idle": "2024-06-04T23:19:35.776229Z",
+          "shell.execute_reply": "2024-06-04T23:19:35.775762Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "ca7a1d45"
+      },
+      "outputs": [],
+      "source": [
+        "def plot_wage_fit(age_df,\n",
+        "                  basis,\n",
+        "                  title):\n",
+        "\n",
+        "    X = basis.transform(Wage)\n",
+        "    Xnew = basis.transform(age_df)\n",
+        "    M = sm.OLS(y, X).fit()\n",
+        "    preds = M.get_prediction(Xnew)\n",
+        "    bands = preds.conf_int(alpha=0.05)\n",
+        "    fig, ax = subplots(figsize=(8,8))\n",
+        "    ax.scatter(age,\n",
+        "               y,\n",
+        "               facecolor='gray',\n",
+        "               alpha=0.5)\n",
+        "    for val, ls in zip([preds.predicted_mean,\n",
+        "                      bands[:,0],\n",
+        "                      bands[:,1]],\n",
+        "                     ['b','r--','r--']):\n",
+        "        ax.plot(age_df.values, val, ls, linewidth=3)\n",
+        "    ax.set_title(title, fontsize=20)\n",
+        "    ax.set_xlabel('Age', fontsize=20)\n",
+        "    ax.set_ylabel('Wage', fontsize=20);\n",
+        "    return ax\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "14d6a540",
+      "metadata": {
+        "id": "14d6a540"
+      },
+      "source": [
+        "We include an argument `alpha` to `ax.scatter()`\n",
+        "to add some transparency to the points. This provides a visual indication\n",
+        "of density. Notice the use of the `zip()` function in the\n",
+        "`for` loop above (see Section~\\ref{Ch2-statlearn-lab:for-loops}).\n",
+        "We have three lines to plot, each with different colors and line\n",
+        "types. Here `zip()` conveniently bundles these together as\n",
+        "iterators in the loop. {In `Python`{} speak, an \"iterator\" is an object with a finite number of values, that can be iterated on, as in a loop.}\n",
+        "\n",
+        "We now plot the fit of the fourth-degree polynomial using this\n",
+        "function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "b05a60ba",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:35.779177Z",
+          "iopub.status.busy": "2024-06-04T23:19:35.778961Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.009418Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.008910Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "b05a60ba",
+        "outputId": "b588c6ba-2732-4290-bf51-1305312144ae",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 740
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "   df_resid           ssr  df_diff        ss_diff           F        Pr(>F)\n",
-       "0    2997.0  3.902335e+06      0.0            NaN         NaN           NaN\n",
-       "1    2996.0  3.759472e+06      1.0  142862.701185  113.991883  3.838075e-26\n",
-       "2    2995.0  3.753546e+06      1.0    5926.207070    4.728593  2.974318e-02"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "models = [MS(['education', poly('age', degree=d)])\n",
-    "          for d in range(1, 4)]\n",
-    "XEs = [model.fit_transform(Wage)\n",
-    "       for model in models]\n",
-    "anova_lm(*[sm.OLS(y, X_).fit() for X_ in XEs])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12da652b",
-   "metadata": {},
-   "source": [
-    "As an alternative to using hypothesis tests and ANOVA, we could choose\n",
-    "the polynomial degree using cross-validation, as discussed in Chapter~\\ref{Ch5:resample}.\n",
-    "\n",
-    "Next we consider the task of predicting whether an individual earns\n",
-    "more than $250,000 per year. We proceed much as before, except\n",
-    "that first we create the appropriate response vector, and then apply\n",
-    "the `glm()`  function using the binomial family in order\n",
-    "to fit a polynomial logistic regression model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "2952bde3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.154502Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.154251Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.179360Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.177817Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coef</th>\n",
-       "      <th>std err</th>\n",
-       "      <th>z</th>\n",
-       "      <th>P&gt;|z|</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>intercept</th>\n",
-       "      <td>-4.3012</td>\n",
-       "      <td>0.345</td>\n",
-       "      <td>-12.457</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>poly(age, degree=4)[0]</th>\n",
-       "      <td>71.9642</td>\n",
-       "      <td>26.133</td>\n",
-       "      <td>2.754</td>\n",
-       "      <td>0.006</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>poly(age, degree=4)[1]</th>\n",
-       "      <td>-85.7729</td>\n",
-       "      <td>35.929</td>\n",
-       "      <td>-2.387</td>\n",
-       "      <td>0.017</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>poly(age, degree=4)[2]</th>\n",
-       "      <td>34.1626</td>\n",
-       "      <td>19.697</td>\n",
-       "      <td>1.734</td>\n",
-       "      <td>0.083</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>poly(age, degree=4)[3]</th>\n",
-       "      <td>-47.4008</td>\n",
-       "      <td>24.105</td>\n",
-       "      <td>-1.966</td>\n",
-       "      <td>0.049</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "source": [
+        "plot_wage_fit(age_df,\n",
+        "              poly_age,\n",
+        "              'Degree-4 Polynomial');\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "648104d9",
+      "metadata": {
+        "id": "648104d9"
+      },
+      "source": [
+        "\n",
+        "\n",
+        "    "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "eed651b8",
+      "metadata": {
+        "id": "eed651b8"
+      },
+      "source": [
+        "With  polynomial regression we must decide on the degree of\n",
+        "the polynomial to use. Sometimes we just wing it, and decide to use\n",
+        "second or third degree polynomials, simply to obtain a nonlinear fit. But we can\n",
+        "make such a decision in a more systematic way. One way to do this is through hypothesis\n",
+        "tests, which we demonstrate here. We now fit a series of models ranging from\n",
+        "linear (degree-one) to degree-five polynomials,\n",
+        "and look to determine the simplest model that is sufficient to\n",
+        "explain the relationship between `wage` and `age`. We use the\n",
+        "`anova_lm()`  function, which performs a series of ANOVA\n",
+        "tests.\n",
+        "An \\emph{analysis of\n",
+        "  variance}  or ANOVA tests the null\n",
+        "hypothesis that a model $\\mathcal{M}_1$ is sufficient to explain the\n",
+        "data against the alternative hypothesis that a more complex model\n",
+        "$\\mathcal{M}_2$ is required. The determination is based on an F-test.\n",
+        "To perform the test, the models  $\\mathcal{M}_1$ and $\\mathcal{M}_2$ must be *nested*:\n",
+        "the space spanned by the predictors in $\\mathcal{M}_1$ must be a subspace of the\n",
+        "space spanned by the predictors in $\\mathcal{M}_2$. In this case, we\n",
+        "fit five different polynomial\n",
+        "models and sequentially compare the simpler model to the more complex\n",
+        "model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "c338a92a",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.012673Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.012313Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.072501Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.071990Z"
+        },
+        "id": "c338a92a",
+        "outputId": "096427d8-6176-445f-9091-c58883c5b367",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   df_resid           ssr  df_diff        ss_diff           F        Pr(>F)\n",
+              "0    2998.0  5.022216e+06      0.0            NaN         NaN           NaN\n",
+              "1    2997.0  4.793430e+06      1.0  228786.010128  143.593107  2.363850e-32\n",
+              "2    2996.0  4.777674e+06      1.0   15755.693664    9.888756  1.679202e-03\n",
+              "3    2995.0  4.771604e+06      1.0    6070.152124    3.809813  5.104620e-02\n",
+              "4    2994.0  4.770322e+06      1.0    1282.563017    0.804976  3.696820e-01"
+            ],
+            "text/html": [
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+              "  <div id=\"df-c66e1140-86f6-4185-a848-eef9a44862c4\" class=\"colab-df-container\">\n",
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+              "        vertical-align: middle;\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>df_resid</th>\n",
+              "      <th>ssr</th>\n",
+              "      <th>df_diff</th>\n",
+              "      <th>ss_diff</th>\n",
+              "      <th>F</th>\n",
+              "      <th>Pr(&gt;F)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2998.0</td>\n",
+              "      <td>5.022216e+06</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2997.0</td>\n",
+              "      <td>4.793430e+06</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>228786.010128</td>\n",
+              "      <td>143.593107</td>\n",
+              "      <td>2.363850e-32</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2996.0</td>\n",
+              "      <td>4.777674e+06</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>15755.693664</td>\n",
+              "      <td>9.888756</td>\n",
+              "      <td>1.679202e-03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2995.0</td>\n",
+              "      <td>4.771604e+06</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>6070.152124</td>\n",
+              "      <td>3.809813</td>\n",
+              "      <td>5.104620e-02</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2994.0</td>\n",
+              "      <td>4.770322e+06</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1282.563017</td>\n",
+              "      <td>0.804976</td>\n",
+              "      <td>3.696820e-01</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c66e1140-86f6-4185-a848-eef9a44862c4')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c66e1140-86f6-4185-a848-eef9a44862c4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c66e1140-86f6-4185-a848-eef9a44862c4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-992a5af1-6ea8-4eaf-98b3-fd239b1c8142\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-992a5af1-6ea8-4eaf-98b3-fd239b1c8142')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-992a5af1-6ea8-4eaf-98b3-fd239b1c8142 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"           for X_ in Xs])\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"df_resid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5811388300841898,\n        \"min\": 2994.0,\n        \"max\": 2998.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2997.0,\n          2994.0,\n          2996.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ssr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 109487.88655804638,\n        \"min\": 4770321.685809545,\n        \"max\": 5022216.1047426965,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4793430.094614205,\n          4770321.685809545,\n          4777674.400950388\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"df_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44721359549995804,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ss_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 110705.4236184561,\n        \"min\": 1282.5630168449134,\n        \"max\": 228786.01012849156,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          15755.693663816899,\n          1282.5630168449134\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69.48211465479159,\n        \"min\": 0.8049758329415485,\n        \"max\": 143.59310743389804,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          9.8887559239021,\n          0.8049758329415485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pr(>F)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17763848502126353,\n        \"min\": 2.363849650526062e-32,\n        \"max\": 0.36968196597565506,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0016792019183144258,\n          0.36968196597565506\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
       ],
-      "text/plain": [
-       "                           coef  std err       z  P>|z|\n",
-       "intercept               -4.3012    0.345 -12.457  0.000\n",
-       "poly(age, degree=4)[0]  71.9642   26.133   2.754  0.006\n",
-       "poly(age, degree=4)[1] -85.7729   35.929  -2.387  0.017\n",
-       "poly(age, degree=4)[2]  34.1626   19.697   1.734  0.083\n",
-       "poly(age, degree=4)[3] -47.4008   24.105  -1.966  0.049"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X = poly_age.transform(Wage)\n",
-    "high_earn = Wage['high_earn'] = y > 250 # shorthand\n",
-    "glm = sm.GLM(y > 250,\n",
-    "             X,\n",
-    "             family=sm.families.Binomial())\n",
-    "B = glm.fit()\n",
-    "summarize(B)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "907c249a",
-   "metadata": {},
-   "source": [
-    "Once again, we make predictions using the `get_prediction()`  method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c999b609",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.182205Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.181928Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.188474Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.187832Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "newX = poly_age.transform(age_df)\n",
-    "preds = B.get_prediction(newX)\n",
-    "bands = preds.conf_int(alpha=0.05)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "37cf225e",
-   "metadata": {},
-   "source": [
-    "We now plot the estimated relationship."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "5f18a110",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.191550Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.191370Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.311190Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.310416Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = subplots(figsize=(8,8))\n",
-    "rng = np.random.default_rng(0)\n",
-    "ax.scatter(age +\n",
-    "           0.2 * rng.uniform(size=y.shape[0]),\n",
-    "           np.where(high_earn, 0.198, 0.002),\n",
-    "           fc='gray',\n",
-    "           marker='|')\n",
-    "for val, ls in zip([preds.predicted_mean,\n",
-    "                  bands[:,0],\n",
-    "                  bands[:,1]],\n",
-    "                 ['b','r--','r--']):\n",
-    "    ax.plot(age_df.values, val, ls, linewidth=3)\n",
-    "ax.set_title('Degree-4 Polynomial', fontsize=20)\n",
-    "ax.set_xlabel('Age', fontsize=20)\n",
-    "ax.set_ylim([0,0.2])\n",
-    "ax.set_ylabel('P(Wage > 250)', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ad21a81",
-   "metadata": {},
-   "source": [
-    "We have drawn the `age` values corresponding to the observations with\n",
-    "`wage` values above 250 as gray marks on the top of the plot, and\n",
-    "those with `wage` values below 250 are shown as gray marks on the\n",
-    "bottom of the plot. We added a small amount of noise to jitter\n",
-    "the `age` values a bit so that observations with the same `age`\n",
-    "value do not cover each other up. This type of plot is often called a\n",
-    "*rug plot*.\n",
-    "\n",
-    "In order to fit a step function, as discussed in\n",
-    "Section~\\ref{Ch7:sec:scolstep-function},   we first use the `pd.qcut()`\n",
-    "function to discretize `age` based on quantiles.  Then  we use `pd.get_dummies()` to create the\n",
-    "columns of the model matrix for this categorical variable. Note that this function will\n",
-    "include *all* columns for a given categorical, rather than the usual approach which drops one\n",
-    "of the levels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "62e053ea",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.315065Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.314672Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.333569Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.333006Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coef</th>\n",
-       "      <th>std err</th>\n",
-       "      <th>t</th>\n",
-       "      <th>P&gt;|t|</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>(17.999, 33.75]</th>\n",
-       "      <td>94.1584</td>\n",
-       "      <td>1.478</td>\n",
-       "      <td>63.692</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(33.75, 42.0]</th>\n",
-       "      <td>116.6608</td>\n",
-       "      <td>1.470</td>\n",
-       "      <td>79.385</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>(42.0, 51.0]</th>\n",
-       "      <td>119.1887</td>\n",
-       "      <td>1.416</td>\n",
-       "      <td>84.147</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>(51.0, 80.0]</th>\n",
-       "      <td>116.5717</td>\n",
-       "      <td>1.559</td>\n",
-       "      <td>74.751</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "source": [
+        "models = [MS([poly('age', degree=d)])\n",
+        "          for d in range(1, 6)]\n",
+        "Xs = [model.fit_transform(Wage) for model in models]\n",
+        "anova_lm(*[sm.OLS(y, X_).fit()\n",
+        "           for X_ in Xs])\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8a8c2c29",
+      "metadata": {
+        "id": "8a8c2c29"
+      },
+      "source": [
+        "Notice the `*` in the `anova_lm()` line above. This\n",
+        "function takes a variable number of non-keyword arguments, in this case fitted models.\n",
+        "When these models are provided as a list (as is done here), it must be\n",
+        "prefixed by `*`.\n",
+        "\n",
+        "The p-value comparing the linear `models[0]` to the quadratic\n",
+        "`models[1]` is essentially zero, indicating that a linear\n",
+        "fit is not sufficient. {Indexing starting at zero is confusing for the polynomial degree example, since `models[1]` is quadratic rather than linear!} Similarly the p-value comparing the quadratic\n",
+        "`models[1]` to the cubic `models[2]` is very low (0.0017), so the\n",
+        "quadratic fit is also insufficient. The p-value comparing the cubic\n",
+        "and degree-four polynomials, `models[2]` and `models[3]`, is\n",
+        "approximately 5%, while the degree-five polynomial `models[4]` seems\n",
+        "unnecessary because its p-value is 0.37. Hence, either a cubic or a\n",
+        "quartic polynomial appear to provide a reasonable fit to the data, but\n",
+        "lower- or higher-order models are not justified.\n",
+        "\n",
+        "In this case, instead of using the `anova()`  function, we could\n",
+        "have obtained these p-values more succinctly by exploiting the fact\n",
+        "that `poly()`  creates orthogonal polynomials."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "a1f74b79",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.075597Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.075369Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.090416Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.089945Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "a1f74b79",
+        "outputId": "09484ec6-0da7-423f-f680-1011ce3e010b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                            coef  std err        t  P>|t|\n",
+              "intercept               111.7036    0.729  153.283  0.000\n",
+              "poly(age, degree=4)[0]  447.0679   39.915   11.201  0.000\n",
+              "poly(age, degree=4)[1] -478.3158   39.915  -11.983  0.000\n",
+              "poly(age, degree=4)[2]  125.5217   39.915    3.145  0.002\n",
+              "poly(age, degree=4)[3]  -77.9112   39.915   -1.952  0.051"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b84ef48c-e9b8-45c7-aa55-4d204ea4eb64\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
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+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>coef</th>\n",
+              "      <th>std err</th>\n",
+              "      <th>t</th>\n",
+              "      <th>P&gt;|t|</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>111.7036</td>\n",
+              "      <td>0.729</td>\n",
+              "      <td>153.283</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(age, degree=4)[0]</th>\n",
+              "      <td>447.0679</td>\n",
+              "      <td>39.915</td>\n",
+              "      <td>11.201</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(age, degree=4)[1]</th>\n",
+              "      <td>-478.3158</td>\n",
+              "      <td>39.915</td>\n",
+              "      <td>-11.983</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(age, degree=4)[2]</th>\n",
+              "      <td>125.5217</td>\n",
+              "      <td>39.915</td>\n",
+              "      <td>3.145</td>\n",
+              "      <td>0.002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(age, degree=4)[3]</th>\n",
+              "      <td>-77.9112</td>\n",
+              "      <td>39.915</td>\n",
+              "      <td>-1.952</td>\n",
+              "      <td>0.051</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b84ef48c-e9b8-45c7-aa55-4d204ea4eb64')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b84ef48c-e9b8-45c7-aa55-4d204ea4eb64 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b84ef48c-e9b8-45c7-aa55-4d204ea4eb64');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
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+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-1e72a818-14fe-434c-a3e0-115a3e15716f button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"summarize(M)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"coef\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 338.9981813862915,\n        \"min\": -478.3158,\n        \"max\": 447.0679,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          447.0679,\n          -77.9112,\n          -478.3158\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std err\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.52451195326135,\n        \"min\": 0.729,\n        \"max\": 39.915,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          39.915,\n          0.729\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"t\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69.01823125232927,\n        \"min\": -11.983,\n        \"max\": 153.283,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          11.201,\n          -1.952\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P>|t|\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.022600884938426637,\n        \"min\": 0.0,\n        \"max\": 0.051,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
       ],
-      "text/plain": [
-       "                     coef  std err       t  P>|t|\n",
-       "(17.999, 33.75]   94.1584    1.478  63.692    0.0\n",
-       "(33.75, 42.0]    116.6608    1.470  79.385    0.0\n",
-       "(42.0, 51.0]     119.1887    1.416  84.147    0.0\n",
-       "(51.0, 80.0]     116.5717    1.559  74.751    0.0"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cut_age = pd.qcut(age, 4)\n",
-    "summarize(sm.OLS(y, pd.get_dummies(cut_age)).fit())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dbe7cfaa",
-   "metadata": {},
-   "source": [
-    "Here `pd.qcut()`  automatically picked the cutpoints based on the quantiles 25%, 50% and 75%, which results in four regions.  We could also have specified our own\n",
-    "quantiles directly instead of the argument `4`. For cuts not based\n",
-    "on quantiles we would use the `pd.cut()` function.\n",
-    "The function `pd.qcut()` (and `pd.cut()`) returns an ordered categorical variable.\n",
-    "  The regression model then creates a set of\n",
-    "dummy variables for use in the regression. Since `age` is the only variable in the model, the value $94,158.40 is the average salary for those under 33.75 years of\n",
-    "age, and the other coefficients are the average\n",
-    "salary for those in the other age groups.  We can produce\n",
-    "predictions and plots just as we did in the case of the polynomial\n",
-    "fit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81d470c1",
-   "metadata": {},
-   "source": [
-    "## Splines\n",
-    "In order to fit regression splines, we use transforms\n",
-    "from the `ISLP` package. The actual spline\n",
-    "evaluation functions are in the `scipy.interpolate` package;\n",
-    "we have simply wrapped them as transforms\n",
-    "similar to `Poly()` and `PCA()`.\n",
-    "\n",
-    "In Section~\\ref{Ch7:sec:scolr-splin}, we saw\n",
-    "that regression splines can be fit by constructing an appropriate\n",
-    "matrix of basis functions.  The `BSpline()`  function generates the\n",
-    "entire matrix of basis functions for splines with the specified set of\n",
-    "knots. By default, the B-splines produced are cubic. To change the degree, use\n",
-    "the argument `degree`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "f032f577",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.336489Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.336258Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.343862Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.343366Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3000, 7)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bs_ = BSpline(internal_knots=[25,40,60], intercept=True).fit(age)\n",
-    "bs_age = bs_.transform(age)\n",
-    "bs_age.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed2f4578",
-   "metadata": {},
-   "source": [
-    "This results in a seven-column matrix, which is what is expected for a cubic-spline basis with 3 interior knots. \n",
-    "We can form this same matrix using the `bs()` object,\n",
-    "which facilitates adding this to a model-matrix builder (as in `poly()` versus its workhorse `Poly()`) described in Section~\\ref{Ch7-nonlin-lab:polynomial-regression-and-step-functions}.\n",
-    "\n",
-    "We now fit a cubic spline model to the `Wage`  data. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "c3c38823",
-   "metadata": {
-    "execution": {
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-     "iopub.status.idle": "2024-06-04T23:19:36.380669Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.380119Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
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-       "      <th>P&gt;|t|</th>\n",
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-       "      <td>3.520</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
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-       "      <th>bs(age, internal_knots=[25, 40, 60])[5]</th>\n",
-       "      <td>16.6061</td>\n",
-       "      <td>19.126</td>\n",
-       "      <td>0.868</td>\n",
-       "      <td>0.385</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "source": [
+        "summarize(M)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ea22f1d6",
+      "metadata": {
+        "id": "ea22f1d6"
+      },
+      "source": [
+        "Notice that the p-values are the same, and in fact the square of\n",
+        "the  t-statistics are equal to the F-statistics from the\n",
+        "`anova_lm()`  function;  for example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "570892d1",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.092926Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.092726Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.096105Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.095602Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "570892d1",
+        "outputId": "953c60fa-49fa-4d2d-8af9-39be70f9e031",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "143.59228900000002"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
       ],
-      "text/plain": [
-       "                                            coef  std err      t  P>|t|\n",
-       "intercept                                60.4937    9.460  6.394  0.000\n",
-       "bs(age, internal_knots=[25, 40, 60])[0]   3.9805   12.538  0.317  0.751\n",
-       "bs(age, internal_knots=[25, 40, 60])[1]  44.6310    9.626  4.636  0.000\n",
-       "bs(age, internal_knots=[25, 40, 60])[2]  62.8388   10.755  5.843  0.000\n",
-       "bs(age, internal_knots=[25, 40, 60])[3]  55.9908   10.706  5.230  0.000\n",
-       "bs(age, internal_knots=[25, 40, 60])[4]  50.6881   14.402  3.520  0.000\n",
-       "bs(age, internal_knots=[25, 40, 60])[5]  16.6061   19.126  0.868  0.385"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bs_age = MS([bs('age', internal_knots=[25,40,60])])\n",
-    "Xbs = bs_age.fit_transform(Wage)\n",
-    "M = sm.OLS(y, Xbs).fit()\n",
-    "summarize(M)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54bacda5",
-   "metadata": {},
-   "source": [
-    "The column names are a little cumbersome, and have caused us to truncate the printed summary. They can be set on construction using the `name` argument as follows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "d01c2c26",
-   "metadata": {
-    "execution": {
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-     "shell.execute_reply": "2024-06-04T23:19:36.415940Z"
-    }
-   },
-   "outputs": [
-    {
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-       "      <td>0.385</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "source": [
+        "(-11.983)**2\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "dcb5ee4d",
+      "metadata": {
+        "id": "dcb5ee4d"
+      },
+      "source": [
+        "However, the ANOVA method works whether or not we used orthogonal\n",
+        "polynomials, provided the models are nested. For example, we can use\n",
+        "`anova_lm()`  to compare the following three\n",
+        "models, which all have a linear term in `education` and a\n",
+        "polynomial in `age` of different degrees:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "3ff20916",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.098614Z",
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+          "iopub.status.idle": "2024-06-04T23:19:36.147734Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.145914Z"
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+        "id": "3ff20916",
+        "outputId": "88cb889a-69a2-4bda-f556-0ec17cc245ac",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   df_resid           ssr  df_diff        ss_diff           F        Pr(>F)\n",
+              "0    2997.0  3.902335e+06      0.0            NaN         NaN           NaN\n",
+              "1    2996.0  3.759472e+06      1.0  142862.701185  113.991883  3.838075e-26\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "        element.appendChild(docLink);\n",
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+              "</svg>\n",
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+              "<style>\n",
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+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-bb05c753-a330-4b1c-896b-7ce885309a45 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"anova_lm(*[sm\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"df_resid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": 2995.0,\n        \"max\": 2997.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2997.0,\n          2996.0,\n          2995.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ssr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 84244.69373992537,\n        \"min\": 3753546.123252735,\n        \"max\": 3902335.0315079046,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3902335.0315079046,\n          3759472.33032245,\n          3753546.123252735\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"df_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5773502691896258,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ss_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 96828.72358115122,\n        \"min\": 5926.207069715019,\n        \"max\": 142862.7011854546,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5926.207069715019,\n          142862.7011854546\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77.26081353550519,\n        \"min\": 4.728592533829217,\n        \"max\": 113.99188287571945,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4.728592533829217,\n          113.99188287571945\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pr(>F)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02103160202887249,\n        \"min\": 3.8380745038929406e-26,\n        \"max\": 0.029743176827664978,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.029743176827664978,\n          3.8380745038929406e-26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
       ],
-      "text/plain": [
-       "               coef  std err      t  P>|t|\n",
-       "intercept   60.4937    9.460  6.394  0.000\n",
-       "bs(age)[0]   3.9805   12.538  0.317  0.751\n",
-       "bs(age)[1]  44.6310    9.626  4.636  0.000\n",
-       "bs(age)[2]  62.8388   10.755  5.843  0.000\n",
-       "bs(age)[3]  55.9908   10.706  5.230  0.000\n",
-       "bs(age)[4]  50.6881   14.402  3.520  0.000\n",
-       "bs(age)[5]  16.6061   19.126  0.868  0.385"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bs_age = MS([bs('age',\n",
-    "                internal_knots=[25,40,60],\n",
-    "                name='bs(age)')])\n",
-    "Xbs = bs_age.fit_transform(Wage)\n",
-    "M = sm.OLS(y, Xbs).fit()\n",
-    "summarize(M)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4b7fab9b",
-   "metadata": {},
-   "source": [
-    "Notice that there are 6 spline coefficients rather than 7. This is because, by default,\n",
-    "`bs()` assumes `intercept=False`, since we typically have an overall intercept in the model.\n",
-    "So it generates the spline basis with the given knots,  and then discards one of the basis functions to account for the intercept. \n",
-    "\n",
-    "We could also use the `df` (degrees of freedom) option to\n",
-    "specify the complexity of the spline.  We see above that with 3 knots,\n",
-    "the spline basis has 6 columns or degrees of freedom.  When we specify\n",
-    "`df=6` rather than the actual knots, `bs()` will produce a\n",
-    "spline with 3 knots chosen at uniform quantiles of the training data.\n",
-    "We can see these chosen knots most easily using `Bspline()` directly:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "672d44ae",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.421424Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.421207Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.429792Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.429086Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([33.75, 42.  , 51.  ])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "BSpline(df=6).fit(age).internal_knots_\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "57fe2029",
-   "metadata": {},
-   "source": [
-    " When asking for six degrees of freedom,\n",
-    "the transform chooses knots at ages 33.75, 42.0, and 51.0,\n",
-    "which correspond to the 25th, 50th, and 75th percentiles of\n",
-    "`age`.\n",
-    "\n",
-    "When using B-splines we need not limit ourselves to cubic polynomials\n",
-    "(i.e. `degree=3`). For instance, using `degree=0` results\n",
-    "in piecewise constant functions, as in our example with\n",
-    "`pd.qcut()` above.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "4a3f93ee",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.432272Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.432078Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.457934Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.457375Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coef</th>\n",
-       "      <th>std err</th>\n",
-       "      <th>t</th>\n",
-       "      <th>P&gt;|t|</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>intercept</th>\n",
-       "      <td>94.1584</td>\n",
-       "      <td>1.478</td>\n",
-       "      <td>63.687</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bs(age, df=3, degree=0)[0]</th>\n",
-       "      <td>22.3490</td>\n",
-       "      <td>2.152</td>\n",
-       "      <td>10.388</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bs(age, df=3, degree=0)[1]</th>\n",
-       "      <td>24.8076</td>\n",
-       "      <td>2.044</td>\n",
-       "      <td>12.137</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>bs(age, df=3, degree=0)[2]</th>\n",
-       "      <td>22.7814</td>\n",
-       "      <td>2.087</td>\n",
-       "      <td>10.917</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "source": [
+        "models = [MS(['education', poly('age', degree=d)])\n",
+        "          for d in range(1, 4)]\n",
+        "XEs = [model.fit_transform(Wage)\n",
+        "       for model in models]\n",
+        "anova_lm(*[sm.OLS(y, X_).fit() for X_ in XEs])\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "12da652b",
+      "metadata": {
+        "id": "12da652b"
+      },
+      "source": [
+        "As an alternative to using hypothesis tests and ANOVA, we could choose\n",
+        "the polynomial degree using cross-validation, as discussed in Chapter~\\ref{Ch5:resample}.\n",
+        "\n",
+        "Next we consider the task of predicting whether an individual earns\n",
+        "more than $250,000 per year. We proceed much as before, except\n",
+        "that first we create the appropriate response vector, and then apply\n",
+        "the `glm()`  function using the binomial family in order\n",
+        "to fit a polynomial logistic regression model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "2952bde3",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.154502Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.154251Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.179360Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.177817Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "2952bde3",
+        "outputId": "efecf3f0-231d-4317-c04b-9b01029f6abd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                           coef  std err       z  P>|z|\n",
+              "intercept               -4.3012    0.345 -12.457  0.000\n",
+              "poly(age, degree=4)[0]  71.9642   26.133   2.754  0.006\n",
+              "poly(age, degree=4)[1] -85.7729   35.929  -2.387  0.017\n",
+              "poly(age, degree=4)[2]  34.1626   19.697   1.734  0.083\n",
+              "poly(age, degree=4)[3] -47.4008   24.105  -1.966  0.049"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1bc65d5a-6c52-43b5-935c-ca2e7416f429\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>coef</th>\n",
+              "      <th>std err</th>\n",
+              "      <th>z</th>\n",
+              "      <th>P&gt;|z|</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>-4.3012</td>\n",
+              "      <td>0.345</td>\n",
+              "      <td>-12.457</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(age, degree=4)[0]</th>\n",
+              "      <td>71.9642</td>\n",
+              "      <td>26.133</td>\n",
+              "      <td>2.754</td>\n",
+              "      <td>0.006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(age, degree=4)[1]</th>\n",
+              "      <td>-85.7729</td>\n",
+              "      <td>35.929</td>\n",
+              "      <td>-2.387</td>\n",
+              "      <td>0.017</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(age, degree=4)[2]</th>\n",
+              "      <td>34.1626</td>\n",
+              "      <td>19.697</td>\n",
+              "      <td>1.734</td>\n",
+              "      <td>0.083</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>poly(age, degree=4)[3]</th>\n",
+              "      <td>-47.4008</td>\n",
+              "      <td>24.105</td>\n",
+              "      <td>-1.966</td>\n",
+              "      <td>0.049</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1bc65d5a-6c52-43b5-935c-ca2e7416f429')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1bc65d5a-6c52-43b5-935c-ca2e7416f429 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-1bc65d5a-6c52-43b5-935c-ca2e7416f429');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "  .colab-df-quickchart {\n",
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+              "    fill: var(--button-hover-fill-color);\n",
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+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
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+              "  @keyframes spin {\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
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+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"summarize(B)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"coef\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.792744768340874,\n        \"min\": -85.7729,\n        \"max\": 71.9642,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          71.9642,\n          -47.4008,\n          -85.7729\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std err\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.104371911694205,\n        \"min\": 0.345,\n        \"max\": 35.929,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          26.133,\n          24.105,\n          35.929\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.020064559454491,\n        \"min\": -12.457,\n        \"max\": 2.754,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.754,\n          -1.966,\n          -2.387\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P>|z|\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.034677081768799405,\n        \"min\": 0.0,\n        \"max\": 0.083,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.006,\n          0.049,\n          0.017\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
       ],
-      "text/plain": [
-       "                               coef  std err       t  P>|t|\n",
-       "intercept                   94.1584    1.478  63.687    0.0\n",
-       "bs(age, df=3, degree=0)[0]  22.3490    2.152  10.388    0.0\n",
-       "bs(age, df=3, degree=0)[1]  24.8076    2.044  12.137    0.0\n",
-       "bs(age, df=3, degree=0)[2]  22.7814    2.087  10.917    0.0"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bs_age0 = MS([bs('age',\n",
-    "                 df=3, \n",
-    "                 degree=0)]).fit(Wage)\n",
-    "Xbs0 = bs_age0.transform(Wage)\n",
-    "summarize(sm.OLS(y, Xbs0).fit())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad6376f5",
-   "metadata": {},
-   "source": [
-    "This fit should be compared with cell [15] where we use `qcut()`\n",
-    "to create four bins by cutting at the 25%, 50% and 75% quantiles of\n",
-    "`age`.  Since we specified `df=3` for degree-zero splines here, there will also be\n",
-    "knots at the same three quantiles. Although the coefficients appear different, we\n",
-    "see that this is a result of the different coding. For example, the\n",
-    "first coefficient is identical in both cases, and is the mean response\n",
-    "in the first bin. For the second coefficient, we have\n",
-    "$94.158 + 22.349 = 116.507 \\approx 116.611$, the latter being the mean\n",
-    "in the second bin in cell [15]. Here the intercept is coded by a column\n",
-    "of ones, so the second, third and fourth coefficients are increments\n",
-    "for those bins. Why is the sum not exactly the same? It turns out that\n",
-    "the `qcut()` uses $\\leq$, while `bs()` uses $<$ when\n",
-    "deciding bin membership.\n",
-    "\n",
-    "\n",
-    "    \n",
-    " \n",
-    "\n",
-    " \n",
-    "\n",
-    "    \n",
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99749b1e",
-   "metadata": {},
-   "source": [
-    "In order to fit a natural spline, we use the `NaturalSpline()` \n",
-    "transform with the corresponding helper `ns()`.  Here we fit a natural spline with five\n",
-    "degrees of freedom (excluding the intercept) and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "bd2b4215",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.460525Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.460295Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.489739Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.489248Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>coef</th>\n",
-       "      <th>std err</th>\n",
-       "      <th>t</th>\n",
-       "      <th>P&gt;|t|</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>intercept</th>\n",
-       "      <td>60.4752</td>\n",
-       "      <td>4.708</td>\n",
-       "      <td>12.844</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ns(age, df=5)[0]</th>\n",
-       "      <td>61.5267</td>\n",
-       "      <td>4.709</td>\n",
-       "      <td>13.065</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ns(age, df=5)[1]</th>\n",
-       "      <td>55.6912</td>\n",
-       "      <td>5.717</td>\n",
-       "      <td>9.741</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ns(age, df=5)[2]</th>\n",
-       "      <td>46.8184</td>\n",
-       "      <td>4.948</td>\n",
-       "      <td>9.463</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ns(age, df=5)[3]</th>\n",
-       "      <td>83.2036</td>\n",
-       "      <td>11.918</td>\n",
-       "      <td>6.982</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ns(age, df=5)[4]</th>\n",
-       "      <td>6.8770</td>\n",
-       "      <td>9.484</td>\n",
-       "      <td>0.725</td>\n",
-       "      <td>0.468</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "source": [
+        "X = poly_age.transform(Wage)\n",
+        "high_earn = Wage['high_earn'] = y > 250 # shorthand\n",
+        "glm = sm.GLM(y > 250,\n",
+        "             X,\n",
+        "             family=sm.families.Binomial())\n",
+        "B = glm.fit()\n",
+        "summarize(B)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "907c249a",
+      "metadata": {
+        "id": "907c249a"
+      },
+      "source": [
+        "Once again, we make predictions using the `get_prediction()`  method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "c999b609",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.182205Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.181928Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.188474Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.187832Z"
+        },
+        "id": "c999b609"
+      },
+      "outputs": [],
+      "source": [
+        "newX = poly_age.transform(age_df)\n",
+        "preds = B.get_prediction(newX)\n",
+        "bands = preds.conf_int(alpha=0.05)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "37cf225e",
+      "metadata": {
+        "id": "37cf225e"
+      },
+      "source": [
+        "We now plot the estimated relationship."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "5f18a110",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.191550Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.191370Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.311190Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.310416Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "5f18a110",
+        "outputId": "8ce1d412-d2b7-466c-9920-d7a2f2bd95c6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 740
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "                     coef  std err       t  P>|t|\n",
-       "intercept         60.4752    4.708  12.844  0.000\n",
-       "ns(age, df=5)[0]  61.5267    4.709  13.065  0.000\n",
-       "ns(age, df=5)[1]  55.6912    5.717   9.741  0.000\n",
-       "ns(age, df=5)[2]  46.8184    4.948   9.463  0.000\n",
-       "ns(age, df=5)[3]  83.2036   11.918   6.982  0.000\n",
-       "ns(age, df=5)[4]   6.8770    9.484   0.725  0.468"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ns_age = MS([ns('age', df=5)]).fit(Wage)\n",
-    "M_ns = sm.OLS(y, ns_age.transform(Wage)).fit()\n",
-    "summarize(M_ns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d5bc0531",
-   "metadata": {},
-   "source": [
-    "We now plot the natural spline using our plotting function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "9162a058",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.493122Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.492786Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.668184Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.667651Z"
-    }
-   },
-   "outputs": [
+      "source": [
+        "fig, ax = subplots(figsize=(8,8))\n",
+        "rng = np.random.default_rng(0)\n",
+        "ax.scatter(age +\n",
+        "           0.2 * rng.uniform(size=y.shape[0]),\n",
+        "           np.where(high_earn, 0.198, 0.002),\n",
+        "           fc='gray',\n",
+        "           marker='|')\n",
+        "for val, ls in zip([preds.predicted_mean,\n",
+        "                  bands[:,0],\n",
+        "                  bands[:,1]],\n",
+        "                 ['b','r--','r--']):\n",
+        "    ax.plot(age_df.values, val, ls, linewidth=3)\n",
+        "ax.set_title('Degree-4 Polynomial', fontsize=20)\n",
+        "ax.set_xlabel('Age', fontsize=20)\n",
+        "ax.set_ylim([0,0.2])\n",
+        "ax.set_ylabel('P(Wage > 250)', fontsize=20);\n"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "5ad21a81",
+      "metadata": {
+        "id": "5ad21a81"
+      },
+      "source": [
+        "We have drawn the `age` values corresponding to the observations with\n",
+        "`wage` values above 250 as gray marks on the top of the plot, and\n",
+        "those with `wage` values below 250 are shown as gray marks on the\n",
+        "bottom of the plot. We added a small amount of noise to jitter\n",
+        "the `age` values a bit so that observations with the same `age`\n",
+        "value do not cover each other up. This type of plot is often called a\n",
+        "*rug plot*.\n",
+        "\n",
+        "In order to fit a step function, as discussed in\n",
+        "Section~\\ref{Ch7:sec:scolstep-function},   we first use the `pd.qcut()`\n",
+        "function to discretize `age` based on quantiles.  Then  we use `pd.get_dummies()` to create the\n",
+        "columns of the model matrix for this categorical variable. Note that this function will\n",
+        "include *all* columns for a given categorical, rather than the usual approach which drops one\n",
+        "of the levels."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_wage_fit(age_df,\n",
-    "              ns_age,\n",
-    "              'Natural spline, df=5');\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb85535d",
-   "metadata": {},
-   "source": [
-    "## Smoothing Splines and GAMs\n",
-    "A smoothing spline is a special case of a GAM with squared-error loss\n",
-    "and a single feature. To fit GAMs in `Python` we will use the\n",
-    "`pygam` package which can be installed via `pip install pygam`. The\n",
-    "estimator `LinearGAM()` uses squared-error loss.\n",
-    "The GAM is specified by associating each column\n",
-    "of a model matrix with a particular smoothing operation:\n",
-    "`s` for smoothing spline; `l` for linear, and `f` for factor or categorical variables.\n",
-    "The argument `0` passed to `s` below indicates that this smoother will\n",
-    "apply to the first column of a feature matrix. Below, we pass it a\n",
-    "matrix with a single column: `X_age`. The argument `lam` is the penalty parameter $\\lambda$ as discussed in Section~\\ref{Ch7:sec5.2}."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "86ff9343",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.671284Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.671050Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.705757Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.705247Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LinearGAM(callbacks=[Deviance(), Diffs()], fit_intercept=True, \n",
-       "   max_iter=100, scale=None, terms=s(0) + intercept, tol=0.0001, \n",
-       "   verbose=False)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_age = np.asarray(age).reshape((-1,1))\n",
-    "gam = LinearGAM(s_gam(0, lam=0.6))\n",
-    "gam.fit(X_age, y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "879ee931",
-   "metadata": {},
-   "source": [
-    "The `pygam` library generally expects a matrix of features so we reshape `age` to be a matrix (a two-dimensional array) instead\n",
-    "of a vector (i.e. a one-dimensional array). The `-1` in the call to the `reshape()` method tells `numpy` to impute the\n",
-    "size of that dimension based on the remaining entries of the shape tuple.\n",
-    " \n",
-    "Let’s investigate how the fit changes with the smoothing parameter `lam`.\n",
-    "The function `np.logspace()` is similar to `np.linspace()` but spaces points\n",
-    "evenly on the log-scale. Below we vary `lam` from $10^{-2}$ to $10^6$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "44812968",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.708880Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.708673Z",
-     "iopub.status.idle": "2024-06-04T23:19:36.976882Z",
-     "shell.execute_reply": "2024-06-04T23:19:36.976289Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "62e053ea",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.315065Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.314672Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.333569Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.333006Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "62e053ea",
+        "outputId": "f155d343-51b8-4178-f206-5b8e638ad573",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     coef  std err       t  P>|t|\n",
+              "(17.999, 33.75]   94.1584    1.478  63.692    0.0\n",
+              "(33.75, 42.0]    116.6608    1.470  79.385    0.0\n",
+              "(42.0, 51.0]     119.1887    1.416  84.147    0.0\n",
+              "(51.0, 80.0]     116.5717    1.559  74.751    0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8f354d01-710a-422a-89d5-30a1a8def832\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>coef</th>\n",
+              "      <th>std err</th>\n",
+              "      <th>t</th>\n",
+              "      <th>P&gt;|t|</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>(17.999, 33.75]</th>\n",
+              "      <td>94.1584</td>\n",
+              "      <td>1.478</td>\n",
+              "      <td>63.692</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>(33.75, 42.0]</th>\n",
+              "      <td>116.6608</td>\n",
+              "      <td>1.470</td>\n",
+              "      <td>79.385</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>(42.0, 51.0]</th>\n",
+              "      <td>119.1887</td>\n",
+              "      <td>1.416</td>\n",
+              "      <td>84.147</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>(51.0, 80.0]</th>\n",
+              "      <td>116.5717</td>\n",
+              "      <td>1.559</td>\n",
+              "      <td>74.751</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f354d01-710a-422a-89d5-30a1a8def832')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
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+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
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+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8f354d01-710a-422a-89d5-30a1a8def832 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8f354d01-710a-422a-89d5-30a1a8def832');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-dc6ed8b7-6bec-47fd-bb8f-be8c5d0f22f8\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-dc6ed8b7-6bec-47fd-bb8f-be8c5d0f22f8 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"summarize(sm\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"coef\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.720625861275497,\n        \"min\": 94.1584,\n        \"max\": 119.1887,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          116.6608,\n          116.5717,\n          94.1584\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std err\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05898799312854552,\n        \"min\": 1.416,\n        \"max\": 1.559,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.47,\n          1.559,\n          1.478\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"t\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.75316214766603,\n        \"min\": 63.692,\n        \"max\": 84.147,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          79.385,\n          74.751,\n          63.692\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P>|t|\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "cut_age = pd.qcut(age, 4)\n",
+        "summarize(sm.OLS(y, pd.get_dummies(cut_age)).fit())\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = subplots(figsize=(8,8))\n",
-    "ax.scatter(age, y, facecolor='gray', alpha=0.5)\n",
-    "for lam in np.logspace(-2, 6, 5):\n",
-    "    gam = LinearGAM(s_gam(0, lam=lam)).fit(X_age, y)\n",
-    "    ax.plot(age_grid,\n",
-    "            gam.predict(age_grid),\n",
-    "            label='{:.1e}'.format(lam),\n",
-    "            linewidth=3)\n",
-    "ax.set_xlabel('Age', fontsize=20)\n",
-    "ax.set_ylabel('Wage', fontsize=20);\n",
-    "ax.legend(title='$\\lambda$');\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f18b58db",
-   "metadata": {},
-   "source": [
-    "The `pygam` package can perform a search for an optimal smoothing parameter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "968e1e42",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:36.979615Z",
-     "iopub.status.busy": "2024-06-04T23:19:36.979395Z",
-     "iopub.status.idle": "2024-06-04T23:19:37.195601Z",
-     "shell.execute_reply": "2024-06-04T23:19:37.195005Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "\u001b[38;2;255;0;0m  0%\u001b[39m \u001b[38;2;255;0;0m(0 of 11)\u001b[39m |                         | Elapsed Time: 0:00:00 ETA:  --:--:--"
-     ]
+      "cell_type": "markdown",
+      "id": "dbe7cfaa",
+      "metadata": {
+        "id": "dbe7cfaa"
+      },
+      "source": [
+        "Here `pd.qcut()`  automatically picked the cutpoints based on the quantiles 25%, 50% and 75%, which results in four regions.  We could also have specified our own\n",
+        "quantiles directly instead of the argument `4`. For cuts not based\n",
+        "on quantiles we would use the `pd.cut()` function.\n",
+        "The function `pd.qcut()` (and `pd.cut()`) returns an ordered categorical variable.\n",
+        "  The regression model then creates a set of\n",
+        "dummy variables for use in the regression. Since `age` is the only variable in the model, the value $94,158.40 is the average salary for those under 33.75 years of\n",
+        "age, and the other coefficients are the average\n",
+        "salary for those in the other age groups.  We can produce\n",
+        "predictions and plots just as we did in the case of the polynomial\n",
+        "fit."
+      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "\u001b[38;2;255;189;0m 45%\u001b[39m \u001b[38;2;255;189;0m(5 of 11)\u001b[39m |###########              | Elapsed Time: 0:00:00 ETA:   0:00:00"
-     ]
+      "cell_type": "markdown",
+      "id": "81d470c1",
+      "metadata": {
+        "id": "81d470c1"
+      },
+      "source": [
+        "## Splines\n",
+        "In order to fit regression splines, we use transforms\n",
+        "from the `ISLP` package. The actual spline\n",
+        "evaluation functions are in the `scipy.interpolate` package;\n",
+        "we have simply wrapped them as transforms\n",
+        "similar to `Poly()` and `PCA()`.\n",
+        "\n",
+        "In Section~\\ref{Ch7:sec:scolr-splin}, we saw\n",
+        "that regression splines can be fit by constructing an appropriate\n",
+        "matrix of basis functions.  The `BSpline()`  function generates the\n",
+        "entire matrix of basis functions for splines with the specified set of\n",
+        "knots. By default, the B-splines produced are cubic. To change the degree, use\n",
+        "the argument `degree`."
+      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(11 of 11)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00"
-     ]
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "f032f577",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.336489Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.336258Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.343862Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.343366Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "f032f577",
+        "outputId": "8b704342-33c6-4b4e-b31a-1e59ed076718",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(3000, 7)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ],
+      "source": [
+        "bs_ = BSpline(internal_knots=[25,40,60], intercept=True).fit(age)\n",
+        "bs_age = bs_.transform(age)\n",
+        "bs_age.shape\n"
+      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "id": "ed2f4578",
+      "metadata": {
+        "id": "ed2f4578"
+      },
+      "source": [
+        "This results in a seven-column matrix, which is what is expected for a cubic-spline basis with 3 interior knots.\n",
+        "We can form this same matrix using the `bs()` object,\n",
+        "which facilitates adding this to a model-matrix builder (as in `poly()` versus its workhorse `Poly()`) described in Section~\\ref{Ch7-nonlin-lab:polynomial-regression-and-step-functions}.\n",
+        "\n",
+        "We now fit a cubic spline model to the `Wage`  data."
+      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "c3c38823",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.346759Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.346545Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.380669Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.380119Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "c3c38823",
+        "outputId": "735af989-3ef4-4733-8bc2-4bfa9a58abce",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 288
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                            coef  std err      t  P>|t|\n",
+              "intercept                                60.4937    9.460  6.394  0.000\n",
+              "bs(age, internal_knots=[25, 40, 60])[0]   3.9805   12.538  0.317  0.751\n",
+              "bs(age, internal_knots=[25, 40, 60])[1]  44.6310    9.626  4.636  0.000\n",
+              "bs(age, internal_knots=[25, 40, 60])[2]  62.8388   10.755  5.843  0.000\n",
+              "bs(age, internal_knots=[25, 40, 60])[3]  55.9908   10.706  5.230  0.000\n",
+              "bs(age, internal_knots=[25, 40, 60])[4]  50.6881   14.402  3.520  0.000\n",
+              "bs(age, internal_knots=[25, 40, 60])[5]  16.6061   19.126  0.868  0.385"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-30377c56-0a61-4485-afe2-7d11e5fe05ec\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>coef</th>\n",
+              "      <th>std err</th>\n",
+              "      <th>t</th>\n",
+              "      <th>P&gt;|t|</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>60.4937</td>\n",
+              "      <td>9.460</td>\n",
+              "      <td>6.394</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age, internal_knots=[25, 40, 60])[0]</th>\n",
+              "      <td>3.9805</td>\n",
+              "      <td>12.538</td>\n",
+              "      <td>0.317</td>\n",
+              "      <td>0.751</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age, internal_knots=[25, 40, 60])[1]</th>\n",
+              "      <td>44.6310</td>\n",
+              "      <td>9.626</td>\n",
+              "      <td>4.636</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age, internal_knots=[25, 40, 60])[2]</th>\n",
+              "      <td>62.8388</td>\n",
+              "      <td>10.755</td>\n",
+              "      <td>5.843</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age, internal_knots=[25, 40, 60])[3]</th>\n",
+              "      <td>55.9908</td>\n",
+              "      <td>10.706</td>\n",
+              "      <td>5.230</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age, internal_knots=[25, 40, 60])[4]</th>\n",
+              "      <td>50.6881</td>\n",
+              "      <td>14.402</td>\n",
+              "      <td>3.520</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age, internal_knots=[25, 40, 60])[5]</th>\n",
+              "      <td>16.6061</td>\n",
+              "      <td>19.126</td>\n",
+              "      <td>0.868</td>\n",
+              "      <td>0.385</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
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+              "  <style>\n",
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+              "      gap: 12px;\n",
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+              "\n",
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+              "      background-color: #434B5C;\n",
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+              "        document.querySelector('#df-30377c56-0a61-4485-afe2-7d11e5fe05ec button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-30377c56-0a61-4485-afe2-7d11e5fe05ec');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
+              "    <div id=\"df-c265df81-e61d-448c-a990-84b11884e454\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c265df81-e61d-448c-a990-84b11884e454')\"\n",
+              "                title=\"Suggest charts\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
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+              "  }\n",
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+              "  .colab-df-quickchart {\n",
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+              "    width: 32px;\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
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+              "\n",
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+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
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+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"summarize(M)\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"coef\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.893001159576716,\n        \"min\": 3.9805,\n        \"max\": 62.8388,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          60.4937,\n          3.9805,\n          50.6881\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std err\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.4442092810148126,\n        \"min\": 9.46,\n        \"max\": 19.126,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          9.46,\n          12.538,\n          14.402\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"t\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3960597317461096,\n        \"min\": 0.317,\n        \"max\": 6.394,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6.394,\n          0.317,\n          3.52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P>|t|\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2966112575328828,\n        \"min\": 0.0,\n        \"max\": 0.751,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.751,\n          0.385\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ],
+      "source": [
+        "bs_age = MS([bs('age', internal_knots=[25,40,60])])\n",
+        "Xbs = bs_age.fit_transform(Wage)\n",
+        "M = sm.OLS(y, Xbs).fit()\n",
+        "summarize(M)\n"
       ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gam_opt = gam.gridsearch(X_age, y)\n",
-    "ax.plot(age_grid,\n",
-    "        gam_opt.predict(age_grid),\n",
-    "        label='Grid search',\n",
-    "        linewidth=4)\n",
-    "ax.legend()\n",
-    "fig\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bba7cf02",
-   "metadata": {},
-   "source": [
-    "Alternatively, we can fix the degrees of freedom of the smoothing\n",
-    "spline using a function included in the `ISLP.pygam` package. Below we\n",
-    "find a value of $\\lambda$ that gives us roughly four degrees of\n",
-    "freedom. We note here that these degrees of freedom include the\n",
-    "unpenalized intercept and linear term of the smoothing spline, hence there are at least two\n",
-    "degrees of freedom."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "891e3b75",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:37.198482Z",
-     "iopub.status.busy": "2024-06-04T23:19:37.198260Z",
-     "iopub.status.idle": "2024-06-04T23:19:37.214885Z",
-     "shell.execute_reply": "2024-06-04T23:19:37.214249Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.000000100001664"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "age_term = gam.terms[0]\n",
-    "lam_4 = approx_lam(X_age, age_term, 4)\n",
-    "age_term.lam = lam_4\n",
-    "degrees_of_freedom(X_age, age_term)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7688d014",
-   "metadata": {},
-   "source": [
-    "Let’s vary the degrees of freedom in a similar plot to above. We choose the degrees of freedom\n",
-    "as the desired degrees of freedom plus one to account for the fact that these smoothing\n",
-    "splines always have an intercept term. Hence, a value of one for `df` is just a linear fit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "54715029",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:37.218081Z",
-     "iopub.status.busy": "2024-06-04T23:19:37.217683Z",
-     "iopub.status.idle": "2024-06-04T23:19:37.501731Z",
-     "shell.execute_reply": "2024-06-04T23:19:37.500504Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "54bacda5",
+      "metadata": {
+        "id": "54bacda5"
+      },
+      "source": [
+        "The column names are a little cumbersome, and have caused us to truncate the printed summary. They can be set on construction using the `name` argument as follows."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = subplots(figsize=(8,8))\n",
-    "ax.scatter(X_age,\n",
-    "           y,\n",
-    "           facecolor='gray',\n",
-    "           alpha=0.3)\n",
-    "for df in [1,3,4,8,15]:\n",
-    "    lam = approx_lam(X_age, age_term, df+1)\n",
-    "    age_term.lam = lam\n",
-    "    gam.fit(X_age, y)\n",
-    "    ax.plot(age_grid,\n",
-    "            gam.predict(age_grid),\n",
-    "            label='{:d}'.format(df),\n",
-    "            linewidth=4)\n",
-    "ax.set_xlabel('Age', fontsize=20)\n",
-    "ax.set_ylabel('Wage', fontsize=20);\n",
-    "ax.legend(title='Degrees of freedom');\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1cec1eac",
-   "metadata": {},
-   "source": [
-    "### Additive Models with Several Terms\n",
-    "The strength of generalized additive models lies in their ability to fit multivariate regression models with more flexibility than linear models. We demonstrate two approaches: the first in a more manual fashion using natural splines and piecewise constant functions, and the second  using the `pygam` package and smoothing splines.\n",
-    "\n",
-    "We now fit a GAM by hand to predict\n",
-    "`wage` using natural spline functions of `year` and `age`,\n",
-    "treating `education` as a qualitative predictor, as in (\\ref{Ch7:nsmod}).\n",
-    "Since this is just a big linear regression model\n",
-    "using an appropriate choice of basis functions, we can simply do this\n",
-    "using the `sm.OLS()`  function.\n",
-    "\n",
-    "We will build the model matrix in a more manual fashion here, since we wish\n",
-    "to access the pieces separately when constructing partial dependence plots."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "b3cd8b66",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:37.505073Z",
-     "iopub.status.busy": "2024-06-04T23:19:37.504839Z",
-     "iopub.status.idle": "2024-06-04T23:19:37.519804Z",
-     "shell.execute_reply": "2024-06-04T23:19:37.519297Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "ns_age = NaturalSpline(df=4).fit(age)\n",
-    "ns_year = NaturalSpline(df=5).fit(Wage['year'])\n",
-    "Xs = [ns_age.transform(age),\n",
-    "      ns_year.transform(Wage['year']),\n",
-    "      pd.get_dummies(Wage['education']).values]\n",
-    "X_bh = np.hstack(Xs)\n",
-    "gam_bh = sm.OLS(y, X_bh).fit()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a1668c8",
-   "metadata": {},
-   "source": [
-    "Here the function `NaturalSpline()` is the workhorse supporting\n",
-    "the `ns()` helper function.  We chose to use all columns of the\n",
-    "indicator matrix for the categorical variable `education`, making an intercept redundant.\n",
-    "Finally, we stacked the three component matrices horizontally to form the model matrix `X_bh`. \n",
-    "\n",
-    "We now show how to construct partial dependence plots for each of the terms in our rudimentary GAM. We can do this by hand,\n",
-    "given grids for `age` and `year`.\n",
-    " We simply predict with new $X$ matrices, fixing all but one of the features at a time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "ea8d6bc5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:37.523112Z",
-     "iopub.status.busy": "2024-06-04T23:19:37.522834Z",
-     "iopub.status.idle": "2024-06-04T23:19:37.644337Z",
-     "shell.execute_reply": "2024-06-04T23:19:37.643251Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "age_grid = np.linspace(age.min(),\n",
-    "                       age.max(),\n",
-    "                       100)\n",
-    "X_age_bh = X_bh.copy()[:100]\n",
-    "X_age_bh[:] = X_bh[:].mean(0)[None,:]\n",
-    "X_age_bh[:,:4] = ns_age.transform(age_grid)\n",
-    "preds = gam_bh.get_prediction(X_age_bh)\n",
-    "bounds_age = preds.conf_int(alpha=0.05)\n",
-    "partial_age = preds.predicted_mean\n",
-    "center = partial_age.mean()\n",
-    "partial_age -= center\n",
-    "bounds_age -= center\n",
-    "fig, ax = subplots(figsize=(8,8))\n",
-    "ax.plot(age_grid, partial_age, 'b', linewidth=3)\n",
-    "ax.plot(age_grid, bounds_age[:,0], 'r--', linewidth=3)\n",
-    "ax.plot(age_grid, bounds_age[:,1], 'r--', linewidth=3)\n",
-    "ax.set_xlabel('Age')\n",
-    "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of age on wage', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ae3a3bb",
-   "metadata": {},
-   "source": [
-    "Let's explain in some detail what we did above. The idea is to create a new prediction matrix, where all but the columns belonging to `age` are constant (and set to  their training-data means). The four columns for `age` are filled in with the natural spline basis evaluated at the 100 values in `age_grid`.\n",
-    "\n",
-    "* We made a grid of length 100 in `age`, and created a matrix `X_age_bh` with 100 rows and the same number of columns as `X_bh`.\n",
-    "* We replaced every row of this matrix with the column means of the original.\n",
-    "* We then replace just the first four columns representing `age` with the natural spline basis computed at the values in `age_grid`. \n",
-    "\n",
-    "The remaining steps should by now be familiar.\n",
-    "\n",
-    "We also look at the effect of `year` on `wage`; the process is the same."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "812a47a1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:37.648530Z",
-     "iopub.status.busy": "2024-06-04T23:19:37.647173Z",
-     "iopub.status.idle": "2024-06-04T23:19:37.756140Z",
-     "shell.execute_reply": "2024-06-04T23:19:37.755581Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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uGzBggOE51qxZk2gspvbs2WN47OnTpxO9b4sWLRQAyltvvZWsr6EoivLTTz8Zvk5ip4+ND0CWL19udntqJbHz5883fB1ry4L0yU9ISIhZkvr9998bku3E3vdiYmKUHDlyWD2ISYoyZcpY/dszTmLTp09vNpuYXIcOHTI8X5cuXazeb9++fYb7ffnll2a3Gyexp06dsvgc8fHxSvbs2RUAyvvvv5+ieLt27aoAULJmzaoa//fffxVAZkmPHj1q+P/jx48N94mNjVUCAgIUAMo333yToq+/Zs0aw/f56NEj1W137twxvEd89NFHVp/j5MmTiSaxqfXeSYIbu9zQ8OHDVRso0qVLh1q1ahk2S4WEhGDNmjXw9fW1+hwPHz7E5cuXcfbsWcMlc+bMAIBTp04l+vWrVq1q2FSkhf/++w9Xr17FP//8Y4g9ffr0AIBz584hJiYmVb9eXFyc4bWtV68esmfPbvF+Hh4eaNu2rdXn2b17N8LCwgAAH330UaJfs0aNGgBk48uxY8es3s94A52p999/3/C6mG5o+fPPPwEANWvWNPzcXxXLgQMHrN6nbt26CAkJsXhbQEAAChUqBAC4du2a6rZ79+4ZNmO0aNECadKksfgc6dKlQ4sWLax+/fXr1yMuLg5p0qTBu+++a/2bQcL3c/fuXdy8edPifdKnT49GjRpZfY7y5csDMP9+AGDdunUAgPz58+O9995LNBZT+p9LkSJF8MYbbyR6X/33ceTIkWRv8tL/PqRPnx4ffPCB1ft17NjR7DG20Lx5cwQFBQEA5s6da3b7/fv3DZvpWrduDS8vdXVJ/evWuHHjRN/3vLy88OabbwJI/PcZABRFQWhoKC5duqR6n8yRIweAV79PNmnSBAEBAYne51WMX/MOHTpYvV/VqlVRrFgxs8eYeuONN1CqVCmLt+l0OpQtWxaA5d/rpKhZsyYA2Vx34cIFw/ju3bsBAMWLF0f58uWRL18+KIqCPXv2GO5z/PhxQ8e3pGx+i4iIwI0bN1SfA8ZVMkx/Pjt37kRcXBwA+R2ypnTp0ihdurTV21P7vdPdsU4sGeTLlw8fffQRvv76a4sJxd9//41JkyZh27ZtFneG6r1qp7+1N0FbOnPmDH755Rds3Lgx0W5k8fHx+O+//6wmVClx9epVvHjxAgBQsWLFRO9rXB3C1NGjRw3/t7aD1xJr36+Pj0+ib7be3t4oW7Ysdu7ciTNnzliMZfPmzUneSZ7Y6160aNFEHxscHAwAZm1JjeNKyms7depUi7fpv58XL16YJTiJCQ0NRe7cuc3GCxUqBA8P63ME1r6fmJgYnD17FgBQrVq1ZDcb0X8fFy9eTNYO/ydPniTrd14fY7ly5RItj5UlSxbkzZsXN27cMDzGFvz9/fHZZ59h+vTpWL58OSZNmqQ6oFm4cKEhUf/8889Vj42Li8PJkycBADNnzsTMmTOT9DWt/T6vX78e06dPx549exJto2uP90n9a+7j44MyZcoket/KlSvj/PnzuHz5MqKjo+Hj42N2n5T+nSaVPokFpFW6/uvpJwH0yWmtWrVw/fp17Nq1C82aNVPdx9PTE9WqVbP4/I8ePcL48ePxxx9/4PLly4nu/jf9+Rj//uoPQq2pUKGC1YOU1H7vdHeciXVDXbt2xZkzZ3DmzBmcPXsWV65cwdOnT3Ht2jWMGTPG4ofZsGHDUK1aNSxfvjzRBBZAoiWIAKlHa0+//fYbypUrh7lz5ybpzeBV8SeX8ev1qkQhS5YsVm978OBBir6+PoE2FRwcDE9PzyTFY/ozT0ksib2u1mZQ9fQJoX4mRM9RX9ukfj+mpb+ePHli+GBNzoGKXmp/H9boX/ekJL5Zs2ZVPcZW9LO+z549w8qVK1W36WdnK1eujOLFi6tue/LkSYrKjZm+ZoqioGPHjmjcuDHWr1//ykTOHu+T+tc8ODj4lQdn+p+ToihWS/Sl9O80qbJmzYoiRYoAgKqMon4m1jiJtXafsmXLWmyccezYMRQtWhSjRo3CpUuXXlm+yvTnY/yavGoGNbHbU/u9091xJtYNWerYlZjt27dj+PDhAOQU59dff41q1aohd+7cSJs2reHNcciQIRgxYsQrn+9ViVNqunDhArp06YLY2FiEhITgm2++wdtvv428efMiICDAMIs0Z84cw+m2V725vY7XaeNr/MFw/PjxJBeIz5kzp81ieffdd63W27S31Ph+MmXKZKj1mBT58uVL8de0Bf33Ubp0aSxatCjJj9Of4k4uR2pLXa5cOZQtWxYnTpzA3Llz0aZNGwDAoUOHDEtOTGdhAfXfVceOHdGrV68kfT3Tmco5c+bgt99+AwCUKVMGX331FSpXrowcOXIgTZo0hve9Nm3aYOHCha98n0nN90lH+jm9Sq1atXDx4kVDUnrnzh1cvXoVOp3OMFOr//f06dN48uQJ0qdPb6h1azybqxcdHY0WLVrg8ePH8Pb2Ro8ePdC0aVMULlwYGTJkMCwhuXbtGgoUKADAdp8Djvje6cyYxNIrzZ49G4DMDBw8eNDqUaatZ1pSYt68eYiNjYWnpyd2795t9XSYLWM3nlG5f/9+ovdN7PaMGTMa/p85c2aryWlSPX78GHFxcYl+WOrj0Z8mNI7l7t27iI6OTtYBUWpL7df22bNnKFasmF0PtIwFBwfDw8MD8fHxuHfvXrIfr/8+nj9/btOfS3BwMO7du/fK1xxIOBVq+jtkCx07dkS3bt2we/duXL9+Hfny5TPMwqZJkwaffPKJ2WOM41IUJcWvm/59smDBgti/fz/8/f0t3s+e75P67+3x48eIjY1NdDZW/3PS6XR2P1tmrGbNmpg5c6ZhXax+TX/x4sUNnz158uQxLFPZs2cPcuXKZdgvYGk97I4dOwzrdKdNm6Zaq20ssZ+N8Wvy8OHDRA/8Hj58aPU2R3nvdBVcTkCv9M8//wAAateunehpEuM1m68rtWYO9LGXLl060fVcqRm7qQIFChg+0I4cOZLofRO7Xb9pApD1ya8rOjo60c0lsbGxhrWCpm+2+liOHj2aaAciWzPevJQar21UVJRNfxdexdvb2/Ba7927N9mzQcYba2y5jk4f4/HjxxM9Ff/gwQND5z97fGC3bNkS/v7+UBQF8+bNQ2RkJJYuXQoA+PDDDy2eZvbx8UGJEiUAvN7flf695r333rOawCqKguPHj6f4aySX/jWPjo42/C1bc/jwYQCyntvSelh7MU5Cd+3aZbaUwPR+xvfx8PBA9erVzZ5T/7MBgI8//tjq107sb1//OwIg0c2yr3oeR3nvdBVMYumV9B9SERERVu9z4sQJHDp0KNW+pp+fn+H/yW3Haiwpsd+7d8+wY9QWjFtFbtmyxeoMW3x8vKENpyV16tQxrEmbNGlSqpzuSuzrrV692rAOrE6dOqrb9Lvmw8LCLO4Gt5fs2bMbdlWvWLHC6tqxiIgILF++3OrzNGnSxHDgNGHChFSPMzmaNGkCALh+/TrWrl2brMfqfy6KomDixImpHpue/vfh6dOnWLVqldX7/fbbb4bfU9PfIVsICgoyVO6YP38+Vq5caZihs7SUQE//ul24cAGbN29O0ddOynvN2rVrUzTDnlLGr/mcOXOs3u/AgQOGJRf2+DklJlu2bIZqJLt27TLb1KVnnMTq76NviW7K+EDL2s8nPj7eMJtuSa1atQxrfhcuXGj1fqdOnUp0csBR3jtdht2LepEmktqxyxJ9fcW0adMqly9fNrv9wYMHSokSJRKtjacoSrK+/u7duw33N+32YyqxOrE9evQw1Kb9+++/zW6PiIhQ1ceFlbq0r1sn9s8//zQ8f5MmTZTY2Fiz+xjX1ISFOrGKklCjFJCONdbaRCqKooSGhiqzZ882GzdtdrB3716z+9y7d0/JnTu3AkjLV9Mari9fvjR0t0qXLl2iLWEVRQr879q1y2w8qb8Tib3+kyZNMjxP165dLT6+U6dOqtfWUrMDfd1UAMq4ceMSjefatWvK4sWLkxWnscR+Z+/du2doEvCqZge3bt0yG9PXlvT09FSWLVuWaBynT59OtJ6tNcbNDnLmzGmxqcPJkycNTURs3ezAmPF7R9asWRUASoECBRJtGhAaGmqINVu2bFY7qemtW7fOrF6qvhNh9uzZVfVL9a5cuWKoo2rtezauE2ut7XRyVahQQQGkw5SlBgtPnz41xO7h4WHx98247WxiUuvnqf971bfK1el0ZjVV9a+VTqcz1Ie11kntjz/+MLyuo0aNsngf4/dWa6+//rMQsNzs4MWLF69sdpBa750kmMS6iddJYo1b0WbPnl2ZNGmS8vfffyt///238vPPPyvZsmVTdDqd8uabb6ZaEhseHm5oY1iuXDlly5YtysWLF5XLly8rly9fVl68eGG4b2IJweHDhw23pU+fXhk5cqSye/du5dChQ8q0adOUQoUKKQCUqlWr2jSJVRT1G2DlypWVpUuXKseOHVM2btyofPzxxwoAwweOtTfRly9fqloali5dWpkyZYqyb98+5cSJE8qOHTuUyZMnK02bNlV8fHyU8uXLmz2HcdvZPHnyKH5+fsqAAQOUvXv3KocPH1amTJmi+rC1ltAdOHBA8fX1NSRMLVu2VFasWKEcPXpUOXz4sLJ27VplyJAhhg9ISw0aUiOJjYmJUcqWLWt4rgYNGihr1qxRjh07pqxZs0apV6+e2WtrKYl9/Pixkj9/fsN9atSoofz666/KgQMHlOPHjytbt25Vxo4dq9SpU0fx8PBQPvzww2TFaexVDTqM2876+/srPXv2VDZu3KicOHFC2bt3rzJ9+nTl3XffVfLnz2/22CtXrijBwcGqg6ZFixYphw4dUo4ePaps2LBBGTlypKHhQt++fRON1RrjtrNZsmRRfvnlF+XQoUPK33//rQwfPtyQFFpqO6tniyRWURSlcOHCqkRixIgRr3zMH3/8Yeik5ufnp3Tp0kVZu3atcuzYMeXgwYPKypUrlX79+hl+R0ybYvz888+Gr1e4cGHlt99+Uw4dOqTs3r1bGTp0qBIUFKT4+fkZ2kbbK4k1bjvr4+Oj9O3bV9m1a5dy5MgRZdasWarf+Ve1nbVXEmvc/QuAUqJEiUTj0l/Wrl1r8X7Pnz9XQkJCDO9VX3zxhbJp0ybl6NGjytKlS5V33nnH7HPA0ut/+fJlw8Gbvu3sjh07lKNHjyrz5s0zNF4xbnFtSWq8d5JgEusmXieJVRRFad++verNwvji6empTJgw4ZUfzMn9+qZHxsYX4yTkVV/XtJ2r6aVv376v7BCWGklseHi46k3S9FK2bFnl2LFjr/wQCw8PVz744INEvyf9pXbt2maPN/6gOXLkiKFnuKWLpbaoxg4cOGCYVXjVZf78+WaPT40kVlGkm06RIkWsfu169eopmzdvTjSJVRSZBdW3MH3VpX379smOUy8pXebmzZun+Pv7JxqDtWTh4sWLqu5viV2GDx+eaKyJGTlypKF9raWLr6+vxZ+7nq2SWOOOYh4eHhZnrC35888/VQcA1i4eHh7Kjh07VI+Njo42HDBZuvj7+yvLly9P9Hu2RRKrKIqyefNmw6ymtUu3bt2sntmxdxJ7+/Zts9gS+3r6n8mTJ0+sPuemTZsMEyOWLrVq1VLOnj37ytd/y5YthjMlli5Dhw5VBg8erAByMGTN6753kuCaWEqSOXPmYOHChahevToCAgLg6+uLPHnyoHXr1ti/f3+Sy9Ikx+jRozF79mxUr149STVNrRkyZAjWr1+PevXqIUOGDPDx8UHOnDnxwQcfYMuWLRg7dmwqR25ZQEAAdu3ahcmTJ6NixYpIly4dAgICUKZMGYwaNQr79+9P0g7ugIAA/PHHH9i7dy86duyIIkWKICAgAF5eXggODkbFihXRrVs3bNiwAVu3bk30uSpUqIDjx4+jZ8+eKFCgAPz8/JAxY0Y0aNAAGzZseOW6yipVquDy5cuYMWMGGjVqhOzZs8PHxwd+fn7IlSsX6tWrh5EjR+LChQuGkke2kD17dpw4cQI//PADSpYsCX9/f6RPnx5VqlTBtGnTsHHjxiRtVsmaNSv27NmDdevWoWXLlsifPz/SpEkDb29vZM6cGW+99Rb69u2L3bt3J7rGMDW0bdsWV69exaBBg1C+fHmkT58enp6eyJAhA6pUqYKBAwdi06ZNFh9buHBhnDx5EosXL8aHH36I3Llzw9/fHz4+PsiWLRtq1aqF7777DseOHcOQIUNSHOPAgQNx4sQJdOrUybCBMW3atChWrBh69epl85+7NcYdlerWrZvkSh5NmjTB9evXMXbsWLz99tvIkiULvL294e/vj3z58qFx48YYP348bty4gdq1a6se6+3tjfXr12PSpEmoUKEC0qRJA39/fxQsWBBdunTB8ePH0bx581T9PpOqXr16uHLlCgYOHIgyZcogMDAQvr6+yJ07N1q2bIm9e/diypQpiTbosKccOXIYSl0B1jtwGY+XKlUq0aoK9evXx9GjR9GqVStkz57d8Ddds2ZNzJo1C9u3b0fatGlfGVvdunVx9uxZfPHFF8iTJw98fHyQJUsWNGrUCJs2bcKwYcMQHh4OABbX5+o5ynuns9Mpig2LYhKRw2nXrh3mz5+PPHny4MaNG1qHQ5Tqtm7dinr16gEAli1blmjLYaLUVqdOHWzfvh3VqlXD3r17tQ7HpTnGYRcREVEq0c+SZ8yYEU2bNtU4GnInd+/exZ49ewDIbCvZFpNYIiJyGVevXjW0nW3fvr2hGxNRarhy5YrV2yIjI9GuXTvExMQAAJcB2AE7dhERkVO7c+cOXrx4gWvXruHbb79FbGws/Pz80Lt3b61DIxfTsWNHREREoEWLFihfvjyCg4Px7NkzHD16FNOmTTMkuR06dFA1YyHbYBJLREROrWXLloauTXojRoxA9uzZNYqIXNnRo0cT7cr1/vvvY/LkyXaMyH0xiSUiIpeQJk0aFC5cGF999RXatm2rdTjkgsaPH4/Vq1djx44duH37Nh4+fAhFURASEoIqVaqgbdu2aNiwodZhug1WJyAiIiIip+NWM7Hx8fG4e/cuAgICDH3SiYiIiMhxKIqCZ8+eIXv27InWL3arJPbu3bvIlSuX1mEQERER0SvcunUr0WYlbpXEBgQEAJAXJTAwUONoiIiIiMhUeHg4cuXKZcjbrHGrJFa/hCAwMJBJLBEREZEDe9XSTzY7ICIiIiKnwySWiIiIiJwOk1giIiIicjpMYomIiIjI6TCJJSIiIiKnwySWiIiIiJwOk1giIiIicjpMYomIiIjI6TCJJSIiIiKnwySWiIiIiJwOk1giIiIicjpMYomIiIjI6TCJJSIiIiKnwySWiIiIiJwOk1giIiIicjpMYomIiIjI6TCJJSIiIiKnwySWiIiIiJwOk1giIiIicjpMYomIiIjI6TCJJSIiIiKnwySWiIiIiJwOk1giIiIicjpMYomIiIjI6TCJJSIiIiJzP/0EtG0LrFkDvHihdTRmmMQSERERkbm1a4EFC4D33wcyZ5b/OxAmsURERERkbvduYNs2oFs3IEMGoHBhrSNS0SmKomgdhL2Eh4cjKCgIYWFhCAwM1DocIiIiIucQHw/odHKxsaTma142j4SIiIiInJuH4528d7yIiIiIiIhegUksERERETkdJrFERERE5HSYxBIRERGRePIEaNoUmDMHePRI62gSxSSWiIiIiMS6dcCffwIdOgBZsgDvvgs4aCErJrFEREREJFavTvh/fDzg42OXslopwSSWiIiIiKS17ObN6rEPPtAmliRgEktEREREksBGRiZc9/QEmjTRLp5XYBJLREREROqlBABQsyYQHKxNLEnAJJaIiIjI3cXEAH/9pR57/31tYkkiJrFERERE7m73buDpU/VYs2ZaRJJkTGKJiIiI3J3pUoKKFYGcObWJJYmYxBIRERG5s/h4YM0a9ZiDLyUAmMQSERERubdDh4C7d9VjTGKJiIiIyKEtX66+XqwYULSoNrEkA5NYIiIiIncVHw+sWKEea9FCm1iSiUksERERkbs6cAC4c0c91ry5NrEkE5NYIiIiIne1dav6eokScnECTGKJiIiI3NXQocDx48CAAUD+/E6zlAAAdIqiKFoHYS/h4eEICgpCWFgYAgMDtQ6HiIiIyHEoChAdDfj6ahpGUvM1zsQSEREREaDTaZ7AJgeTWCIiIiJyOkxiiYiIiMjpOFUSe+fOHbRq1QoZM2aEv78/3njjDRw9elTrsIiIiIjIzry0DiCp/vvvP1StWhW1a9fGxo0bkTlzZly+fBkZMmTQOjQiIiIi5xEXBzRpArzzjtSEzZ1b64hSxGmqE/Tv3x9///039u7dm+LnYHUCIiIicnu7dgG1aydcr1JF6sWmS6dZSMZcrjrBn3/+iQoVKqB58+YICQlB2bJlMXv27EQfExUVhfDwcNWFiIiIyK0tX66+HhnpMAlscjhNEnvt2jVMnz4dhQoVwubNm9G1a1f07NkT8+fPt/qYUaNGISgoyHDJlSuXHSMmIiIicjBxccAff6jHnKjBgTGnWU7g4+ODChUqYP/+/Yaxnj174siRIzhw4IDFx0RFRSEqKspwPTw8HLly5eJyAiIiInJPO3cCb7+tHrt0CShUSJt4LHC55QTZsmVD8eLFVWPFihXDzZs3rT7G19cXgYGBqgsRERGR2zJdSlC2rEMlsMnhNEls1apVcfHiRdXYpUuXkCdPHo0iIiIiInIi0dHAihXqMSddSgA4URLbu3dvHDx4ED/++COuXLmCxYsXY9asWejWrZvWoRERERE5vs2bgceP1WNMYm2vYsWKWL16NZYsWYKSJUtixIgRmDBhAlq2bKl1aERERESOb+FC9fW33gLy59cmllTgNM0OAKBx48Zo3Lix1mEQEREROZenT4E//1SPtW6tSSipxWlmYomIiIgohVauBIwqNsHb26mXEgBMYomIiIhcn+lSgkaNgOBgbWJJJUxiiYjIupgYKY5uTXi4dPshIsf177/Anj3qMSdfSgAwiSUiImORkcDu3cCIEUDdukD69MDBg9bvP3263Kd6dWDwYGDbNiAiwl7RElFS/P67+nr69DIT6+ScamMXERHZyJUrwPffSyF043VzALB3L1C1quXH7doltSf37ZPLDz8AXl5AjRpAr15A48aAB+dLiDR19ar6eosWgK+vNrGkIr6zEBG5s5s3gU6dgKJFZc2caQILmJ+G1IuNlcTV0viOHUDTpkCJEsBvv1l+XiKyj99+A65fl4PMIkVcYikBAOgURVG0DsJektqLl4jI5d27B/z4IzBrlsykJiYwEHjyBPD0VI+fPQuULg3Ex7/662XNKjOzXbrIqUwi0oY+7dPptI0jEUnN1zgTS0TkThQFmDgRKFAAmDLFcgLr4QGULw/07g2sXi2nIk0TWAAoWRL47z9gwwagXz+gcmXL9wOA0FBgwAD5ugsWJHyQEpF96XQOncAmB2diiYjcRWSkLB0w3eSh5+8P9OgBfP01kDlzyr7Gs2eS+P78s8zUWlO/vswC586dsq9DRC6LM7FERJTg9m2gWjXLCayPD9CzJ3DtGvDTTylPYAEgIABo0wY4fRrYuBF4+23L99u7V9bOEhGlEJNYIiJ34O8vp/5NdeoklQkmTpR1q6lFpwMaNAC2bweOHgWaNVPfPmKEU/dsJ3J4sbFJW6/uxJjEEhG5g4wZ5TS/v79cDwgA1q6VU/q5ctn2a5cvL1977Voge3agQgWZ+SUi21m4UA4UBw0CLlzQOhqb4JpYIiJ3snQpMGwYsGaNlNWyt7AwqXSQL5/l2xVFZo+sbRAjoqR56y3gwIGE6336AOPGaRdPMnBNLBERmfvkE+DUKW0SWAAICrKewALAtGmyDOHxY/vFRORqzpxRJ7CArIl3MUxiiYhcza1bid/uqJ169uwBvvpKWtdWqACcPKl1RETOadYs9fVs2aR7nothEktE5Eo2bgQKFpQasM7k9m2gefOEigU3bsjp0FWrNA2LyOm8eCHrYY19/jng7a1NPDbEJJaIyFUcOAB8+KE0MOjRAxg61HmaCjx4YL4ONjJSEtvZs7WJicgZLV8ua8/1dDqgY0ft4rEhJrFERK7gn3+ARo0k8dP7/nvgjz+0iyk5ypUDjh2T2Vdj8fFA585Sv9ZZEnIiLc2cqb5evz6QN68modgak1giImd3/75shjKtA9uyJfDBB9rElBLZsgE7d1qeNerfX1rbMpElsu70aeDgQfXYF19oE4sdMIklInJm8fFA27ayptTYu+8Cc+cCHk72Nu/jI5tSBg82v23sWKBDB3b6IrLG0oauRo20icUOnOzdjYiIVH75Bdi8WT1WpQqwYoXzbuTQ6WQpxIQJ5rfNnQt89BHw8qXdwyJyaBER5hu6OnRw3veBJGASS0TkrI4dAwYMUI9lywb8+SeQNq02MaWmXr2ABQvMN3ytXSuJbFSUNnEROaLly4Hw8ITrLryhS49JLBGRM3r+HPj0UyAmJmFMpwMWLQIyZ9YurtTWurV0F/PzU4+vXw+0aCGVGIjIfENXgwZAnjzaxGInTGKJiJxRz57A5cvqsW+/Bd5+W5t4bKlxY2DLFsC0/eSff0rSTuTuTp0CDh1Sj3XurE0sdsQklojI2SxdKmtDjVWqJOtIXVX16sCmTUC6dAljvXsD7dtrFxORo3CTDl2mmMQSETmT69fNS+YEBACLF7v0Bg4AwJtvSkeytGmBb74Bxo2TJRRE7u6jj6Scnn79eIcOgJeXtjHZget/h0REriI+HmjVSr15AwCmTwcKFNAmJnurVk1qYebLxwSWSK92bbncuydnaVq21Doiu+BMLBGRs4iOlmUDxrVfW7d2mw8sg/z5mcASWZItGzBwoMtv6NJjEktE5Cz8/KQu7LFjQOXKksxNnap1VI7lzh2+JkRugssJiIicTZkywP79wN27sh6WxJUrQN26wI0bMmvdu7fWERGRDXEmlojIGXl4ADlzah2F4zh1StbL3rgh1/v0AebN0zIiIrIxJrFEROT8jh8H7t9Xj3XoII0SiFxR795SWsuNWzAziSUicmQvXmgdgXNo3x746Sf1WHw88PHHwI4d2sREZCsXLwITJ0q5vTx5gBEjgGfPtI7K7pjEEhE5quvXgdy5JTlje9VX69dPLsaio4FmzYATJzQJicgmxo8HFEX+/+CB1Ex2Q0xiiYgckaIAPXoAjx8D/fsDZcsCu3drHZXjGz0a6NhRPfbsGfDuu8DVq9rERJSaHjwA5s9Xj3Xt6pabPJnEEhE5orVrgfXrE66fOwesWKFdPM5CpwNmzAA+/FA9fv8+UL+++bpZImczdSoQFZVw3dtbDnjdEJNYIiJHExUlu+uNZckC/PCDNvE4G09PYNEioFYt9fjVq0DDhm65dpBcxIsX5nWQW7YEsmfXJh6NMYklInI0U6bIelhjv/wCpE+vSThOyc9PKhOULq0eP35cesxzjTE5o/nzZYmRsb59tYnFATCJJSJyJI8fm8+4VqsGfPKJNvE4s6AgYONGIG9e9fi2bUC7dlK9gMhZREcDP/+sHnv3XaBkSW3icQBMYomIHMmIEcDTp+qxceNkrSclX7ZswJYtQObM6vElS4CxY7WJiSglfv3V/AzN119rE4uDYBJLROQoLl82X+/26adApUraxOMqChUCNmwA0qZNGKteHejcWbuYiJIjIkIOcI29+SZQu7Y28TgIJrFERI5iwAAgNjbhuo8P8OOP2sXjSipUAFavlp3czZoBmzdzjTE5j0mTgNBQ9djo0W5/hsZL6wCIiAjAvn3AH3+ox3r1Ml/PSSlXty6wZw9QsaJUMCByBv/9B4wZox5r0ACoUUObeBwIk1giIq0pivkO44wZgYEDtYnHlVWponUERMkzZoz5OnmeoQHA5QRERNpbvhw4fFg9NnQoT3fbW1SU+cYZIi09fgxMnKgea9FCOvgRk1giIk29fCltZY0VKgR88YU28birZ8+Axo1lw9e//2odDZHImBH46y9ZAgPIMhjTDV5ujMsJiIi0FBoKhIQAN24kjI0ZI5u6yD4ePJBOXseOyfU6dWSNcpYs2sZFBADvvAMcOiTNO86dAwoX1joih6FTFEXROgh7CQ8PR1BQEMLCwhAYGKh1OEREQlGAtWtlDWymTMDu3W6/69iuWreWNrXGSpUCdu0CMmTQJCQid5bUfI1JLBGRo4iLkzVwISFaR+JenjwBatYEzp5Vj7/5JrB1q7q+LBHZXFLzNa6JJSJyFJ6eTGC1EBwsXb3y51ePHzgAfPCBbPgiIofDJJaIiChbNmDbNvnX2JYtQKtWMktOZA9HjsiGT3olJrFEREQAkC+fLB8IDlaPr1wJdOoExMdrExe5j3v3pClHqVLAzp1aR+PwmMQSEdnbqFHA9u1aR0GWlCgBbNwIpEunHp87F+jShYks2dZXXwFhYcDly8DbbwPt26tbUZMKk1giIns6exYYNEjKONWtK6cOybFUqiTVInx91eOzZwPdukk1CaLUtn69ND4xFhMDeLEaqjVMYomI7Om77xKSoG3bZLYlPFzbmMjc228DK1YA3t7q8RkzgB49mMhS6nr+HPjyS/VYcDAwfrw28TgJJrFERPZy8KDM8Bnr0wdgyT/H1KSJzIyZzoRNnQqMHKlNTOSahgwBbt5Uj40bx2olr8AklojIHhQFGDBAPZYxI9C3rzbxUNI0awYsXSrlz/Ty5wfatNEsJHIxR48CEyeqx2rXBtq21SYeJ8IklojIHrZulQ5QxgYO5CysM/jwQ2DxYklkCxaUjmq5c2sdFbmCmBigc2f1hkFfX2DmTHbtSwKuFiYisjVFkYTVWM6c5mvgyHG1aCHJRYUKQI4cWkdDruKbb4ATJ9RjgwcDhQppE4+TYRJLRGRrq1YBx46px4YNA/z8NAmHUqhpU60jIFeydKn5MoISJSSxpSThcgIiIluKjZWKBMYKF+Z6N1cTGwv88IPsMid6lXPngI4d1WO+vsCCBYCPjzYxOSEmsUREtrRoEXDhgnpsxAjWfnQl8fGSkAweLPV/nzzROiJyZOHhwAcfABER6vGpU4Fy5bSJyUkxiSUispWoKGDoUPVY2bLARx9pEw+lPkUBevcG5s+X64cOATVqmJdLIgLk9+Xzz4GLF9XjHTrIhZKFSSwRka3MmmWezPz4I+DBt16Xce+eVC4w9s8/0vXr0CFtYiLHpSgy22r8HlCuHDBlinYxOTG+kxIR2cLz57JG0lj16kD9+trEQ7aRPTuwZ495xYL794GaNWXzDpGeh4dUKtm8GciUCciQAVi5kps8U4hJLBGRLUyfDjx4oB4bNYq1H11RsWLAvn1AkSLq8ago4NNPpRIF29SSsTp1pGLJmjVAvnxaR+O0nDaJHT16NHQ6Hb766iutQyEiMte1q8zE6psZNGoEVK2qbUxkO3nzAgcOSHJiavhw4LPPgMhIu4dFDix3blk/TSnmlEnskSNHMHPmTJQqVUrrUIiILEuXDhg0CLh+Hfj2W/OlBeR6MmQANmyQAxhTS5cC1aoBV67YPy7SjmkFAkpVTpfEPn/+HC1btsTs2bORIUMGrcMhIkpccDAwejRQpozWkZA9eHtLqaRJk8w38B0/Lpt4lizRJjayrwsXpCb0okVaR+KynC6J7datGxo1aoQ6lk7ZmIiKikJ4eLjqQkREZFM6HdCjB7BuHRAQoL7t2TNZWtChA5cXuLLLl4G33wbu3gXatAHmzdM6IpfkVEns0qVLcfz4cYwaNSpJ9x81ahSCgoIMl1y5ctk4QiIiov97911ZJ1u0qPlt58+z4YWr+ucfoHZtKb8GJNSGXbNG07BckdMksbdu3UKvXr3w+++/wy+JpSgGDBiAsLAww+XWrVs2jpKI3Nr9+1pHQI6mRAng6FGgXbuEsfTpZUmBt7dWUZGtrFgBVK4M3LmjHq9cWWZmKVU5TRJ77NgxPHjwAOXKlYOXlxe8vLywe/duTJo0CV5eXoiLizN7jK+vLwIDA1UXIiKbOHAAyJVLTiObltYi95Y2LTB3LrBwoWz4mzsXyJNH66goNcXGygbOFi3MN3NVqABs2pRQqYRSjU5RnKN43bNnz/Dvv/+qxtq3b4+iRYvi22+/RcmSJV/5HOHh4QgKCkJYWBgTWiJKPYoipXL27ZPr6dIBI0YALAFIph49kiL31hw/LvVm06a1X0z0eh49Aj75BNi+3fy2SpUkgeVG9GRJar7mNAtyAgICzBLVtGnTImPGjElKYImIbGbduoQEFpBuXWxqQJYklsA+eSLraP39pcJBw4b8PXJ0x44BH34ImEyyAZB1sFOnshuXDTnNcgIiIocUFwf0768ey5cP6NJFm3jIefXrJ0tR/v0XaNxY1lAePKh1VGRJeDjQp4+sdTVNYL29gRkzgF9/ZQJrY04zE2vJrl27tA6BiNzdggXAuXPqsR9+AHx9tYmHnNOuXcBvv5mPvfkm0LQpMHKkbBIjbSmKbMrr2xcIDTW/PXt2YOVK+bmRzXEmlogopSIjgSFD1GNlysj6OKLkyJ4dqFnT8m1r1wJvvCEVDi5ftmtYZEKnkyTWUgJbrZosL2ACazdMYomIUmrSJOD2bfXYTz+Zd2oiepXChYEdO6RygaWa5ooCzJ8vm74aNwa2bpUxsr+JE9VnWnx95WB2+3Yga1bt4nJDfKclIkqJ0FA5xWvsnXeAunW1iYecn4eHzLZeugSMHw9kzGh+H0UB1q8H6tUDSpYEZs4EXrywe6guT1GAly8t35Y/PzBggPz/3XeBs2eB4cMBHx/7xUcAmMQSEaXM4MHSQlRPp5NZWO4mp9fl5wf07g1cuwYMHSol2yw5d042EFavbt/4XNl//8mGrNKlge++s36/fv2kKsn69UDBgvaLj1SYxBIRJdfJk+abcNq2BcqX1yQcclGBgcCwYcDVq8A330inL0saNLBnVK7nyRNgzhyZVQ0JATp1As6cAWbNAsLCLD/G3x9o1IgHrRpjEktElByKIk0MjNcjpk1rvrSAKLWEhABjxsj66+nTgWLF1Ldb20gYHw907izrbNl2PUFcnDSVmDhRDgCyZAE6dJCmBLGxCfd79kxmZclhOU3HrtTAjl1E9NpWrwY++EA99sMPwKBB2sRD7kdRZGPXxInAzZvA6dOWZwRPnZJqGXpFisi67SpV5KxBkSKAp6fdwtbM06fyGv39N7BnD7B/v9R5TYq6dYEtW2waHplLar7GJJaIKKmiooDixWWtol7u3MCFC3J6kcjeIiOt/+6NGwd8/bX1x6ZNC5QtKwltmTJAoUJyyZzZtU6Th4QADx8m/f6+vjJD26GDLBlgtRG7c7m2s0REmouKkpmZ2bPlVC0gp3mZwJJWEvvd27o18cdGREi7ZOOWyYCsxS1VCti71/LjoqNlBtfWs7j6CgFhYcCjR5KIPniQcAkNlW5Z//4LvPce8PPPlp+nRAlpHJEYPz9ZE9u8uZQwCwhI9W+HUh+TWCKipAoMlHaSX34pu8dfvgRatNA6KiLLPv1UkrHt22XXfVKFh8speGuGDQNGjZKZ3KAguQQGShUFH5+Ei6+v/KvTSUKqKLKe95tvLD/v0KHAmjXy9fUX4zWqibl40fptJUtaTmILFgRq1JAD08aNrVeBIIfFJJaIKLlKlQK2bZMZIlc67UqupW1bucTFASdOyO/swYPA0aPAnTuJP7ZQIeu36btVRUTI5e7dpMf09tvWk9hbt2Ttakr8+6/120qWBLy8ZClQ9eqSuFavDmTLlrKvRQ6DSSwRUUrodNZLHhE5Ek9PoEIFuejdvy8tUvWXixdlrXd0tNyeWO3Te/dSHkti23CMu2Al140b1m9r0wZo357NCFwQk1giIiJ3kyUL0LChXPTi4mQ29MoVIHt264/Vz8SmRGJJrJ9f4o8NDpZNWpkzJ/ybKxeQNy+QJ488t6UzI1yz7rKYxBIRJebly1d/uBK5Ak9PSQjz5k38fuvXyxrbsDBZtxoWJpfISJnJjYqSf/X/ByS51OkSn+H99FOplKBfY6u/BAQAGTIA3t6p9Z2Si2ASS0Rkzf37UoKoe3cpVcTTkUQyS5vYTG1KVaokF6IkYvEzIiJrvv5a1v8NGiR1NF9VpoeIyMWcOSN7Ah0Rk1giIkt27AAWLUq4fv48W1ASkVvRdy6uW1dWe7zOnj5bYBJLRGQqKkpqwRoLCpIOSEREbuK336QqGwAsXQoULQocPqxtTMaYxBIRmfr5Z/Pi6aNGyY5uIiI38PAh8O236rEsWaRMtqNgEktEZOzqVWDkSPVYxYpyTo2IyE3062fe6G3qVMcq1sIklohIT1FkGcHLlwljHh7SatbWfeKJiBzE3r3AvHnqsY8/lrWxjoRJLBGR3m+/AVu2qMe6dwfKldMmHiIiO4uJMd8SEBAAjB+vTTyJYRJLRARI7/U+fdRj2bMDI0ZoEw8RkQYmTgTOnlWPjRhhm9LAr4tJLBGRogAdOgDPnqnHZ82SjkFERG7g1i1g2DD1WJkyQLduWkTzakxiiYhmzgS2b1ePtW8PNGqkTTxERBr46isgIiLhuk4HTJ8OeDlof1cmsUTk3q5fl85cxnLmBH75RZt4iIg0sGEDsGqVeqxTJ6BKFW3iSQomsUTk3r7+Wj31AMgGr6AgbeIhIrKzyEjZw2osUyYpj+3ImMQSkXubNg1o1izheufOQL16moVDRGRvI0fKSSljP/8MBAdrE09SMYklIveWJYucQ/v9d6BsWWDsWK0jIiKym/PngTFj1GPVqwNt22oTT3IwiSUi0umAzz4Djh2TgohERG5A398lJiZhzMtLNnPpdNrFlVRMYomI9JzhXZuIKJUsWgTs2qUe69sXKFFCk3CSjUksERERkZt58kQSVmN58gCDB2sTT0owiSUi93H7NtC8OXDvntaREBFpauBA4OFD9djkyUDatNrEkxJMYonIPcTGyrrXlSuB0qWBTZu0joiISBMHDkiPF2PNmgFNmmgSTooxiSUi9zBiBLB3r/z/4UPg3XelIgERkRuJjQW6dlWPpU0LTJyoTTyvg0ksEbm+nTsliTWWJw/QsKE28RARaWTyZODUKfXYsGFA7tyahPNamMQSkWsLDQVatpRaMnqensCSJUCGDNrFRURkZ7dumW/ceuMNoFcvbeJ5XUxiich1RUcDH31kvpFr5EjgzTe1iYmISCM9eph32Z4xA/D21iae18UklohcV69ewN9/q8fq1we++UabeIiINLJmDbB2rXqsUyfgrbc0CSdVMIklItc0a5ZMMRjLnRtYuBDw4FsfEbmPZ89kFtZYSAgwerQ28aQWvpMTkevZvx/o3l095u8vUxGZM2sSEhGRVoYMkTLZxsaPB4KDtYkntTCJJSLXcvcu8OGH6mbgAPDrr0DZstrERESkkePHgUmT1GN16kjZbGfHJJaIXEdUFPDBB1KRwNjXX7vGOzYRUTLExQFffAHExyeM+foC06cDOp12caUWJrFE5BoURSp4HzqkHq9bFxg1SpuYiIg0NG0acPSoeuy774CCBbWJJ7V5aR0AEVGqiIszX0KQPz+wdCngxbe61PbypUx4v3wp3X7SpZN/fXy0joyIAODOHWDQIPVY0aKuVZyF7+xE5Bq8vIAFCyRx/f57yajWrHH+nQsaio2V9XS7dgGnT0u5Xf3l6VPLj/H2lpc+Rw4pov7GG0DJkvJvnjwsDEFkLz16SFUCYzNnynICV8Eklohch04HDB8OFCgABAZK5kRJFh8PHDkiSeuuXcC+fcDz58l7jpgYSXCfPgX++UcmwvUCAoBq1YBGjeSSN2+qhU5ERlavloux9u2BGjW0icdWdIpi3IvRtYWHhyMoKAhhYWEIDAzUOhwiIofw6BEwZ46U1b1+3X5ft0QJSWYbNwaqVuUsLVFqCAsDiheXQi16mTMD588DGTNqF1dyJDVf40wsEZEbUhTg4EHZ+LFihRR2sLd//pHLmDFAvnzSPejzz4EsWewfC5GrGDBAncACwIQJzpPAJgdnYonI+bx8CfTsCQweDOTKpXU0Tmf9ennpTpxI2v2LFZPTkIUKAVmzAtmyJVzSpgVevJBlBxER8m9YGHDxInDmTMLF2hpaU15eQLNmUhbo7bc5O0uUHH//LUt2jNWvD2zc6FwltZKarzGJJSLnEhcHfPIJsHIlkD07sG4dmxgk0aVLQO/ewIYNid+vcGHgnXeAWrWAmjVff2ZUUWRmaO9eSaA3bgQeP3714woVAvr3B1q3lg1jRGRdVBRQrhxw7lzCWJo0wNmzcqbDmTCJtYBJLJGTUxRpJzttWsJY2rTAH3/IdANZ9OwZMHKktJk0rUKm5+MDtGgBfPklUKWKbWdt4uKAw4fl+GPtWllSkJj8+aW2ZatWTGaJrBkxQtrLGhs7FujbV5t4XgeTWAuYxBI5OUvv0gEBso2+VCltYnJgigIsXix1Ie/ds3yffPmALl1k53LmzPaND5AYDx2S0j/LlgGRkdbvy2SWyLILF4DSpYHo6ISxcuXkb8sZy2QnNV/jaiMicg4zZ5onsD4+UguWCayZ58+Bli0l4bOUwGbNCsyfD1y5AvTrp00CC8iMb5UqwNy5suRg0iSpWmDJtWuy8at4cZnFJSIpjffFF+oE1tMTmD3bORPY5GASS0SOb9UqOc9tTKcDFi2S3T+kcuECULkysGSJ+W3e3jIze+kS0KaNY22cSp9eCrSfOQNs2iTfgyVXrgBNmgANG8r3QeTOZs0C9uxRj/XuLTOxrs6B3r6IiCzYvRv47DOZbjA2ZQrQvLk2MTmwFSuAihXVmzv06teXBHHMGFmF4ah0Oon1wIHEk9mNG6UbWL9+QHi4fWMkcgS3bsnvv7F8+YBhwzQJx+6YxBKR4zp5EnjvPfMipoMHm8/MurmYGKBPH9mcZdplKyhIktuNG4EiRbSJLyWMk9mNGyU5NxUTA/z8s3xfixfLGlsid6AosozAUmvZtGm1icnemMQSkWO6dAmoV898iq1zZ2ktSwZPn0pJrF9+Mb+tVCng6FHgo4+cq06kMZ0OaNBAmjMsWCDreU2Fhsoa4GbNrG9iI3Ilv/8uB3fGPv8cqFtXm3i0wCSWiBzPrVvyTvzwoXr8/felvJazZmM28PixJLB795rf1ratzGIWLGj/uGzBw0Nqxl66JKdQLVUo+PNP2fg1fz5nZcl13b8P9OqlHsuaVUpquRMmsUTkWB4+lBnYmzfV47VqyfliT09NwnJEDx4AtWsDx4+rx318ZLPH3LlS7NzVBAQAP/0kRdzffdf89qdPgXbtgMaNgTt37B0dke317Ak8eaIemz4dyJBBm3i0wiSWiBzLiBGyvd5YhQoyxebnp01MDujuXemmdeaMejxbNmk92amT609YFy4sHcBWrgRCQsxv37BBZmUXLbJ/bES2smYNsHy5eqx5c1lK426YxBKRYxkzRjZz6RUrJgu/HHk7vZ3duiUJrGmunyuXlNqpUEGbuLSg0wEffihdvz77zPz28HBZgtCmjfkGGCJn899/QNeu6rHgYGDyZG3i0RqTWCJyLH5+0ka2dWsgTx5gyxYgUyato3IY168DNWpIrVRj+fJJAusq61+TK1Mm2eiyZo3ljV8LF0rdzKNH7R4aUarp21c2MRqbOBHIkkWbeLTGJJaIHI+XFzBvnmxHz5lT62gcxv370tvhxg31eKFCksDmzatFVI6laVOZlW3d2vy2K1eAt96SzS+mZYeJHN2GDbLO3di770pVDnfFJJaIHJOHh+UpNTf14oWssjBNYIsXl34QzPUTBAdLKa4lSwDTtusxMdKx7N13zYtfEDmq//6Tde7G0qWTmrCuvvY9MUxiiUg7c+dyoWISxMfLzOLhw+rxUqWAXbtkMxeZ++QT6ZdRpYr5bVu2yPKCQ4fsHhZRsn31lWzmNDZ+vKyDd2dOk8SOGjUKFStWREBAAEJCQtCsWTNcvHhR67CIKKXGjZPK3G+/DTx6pHU0Dq1fP2DVKvVY3rySiGXOrElITkO/VnjgQPMZq9u3gerVgRkzWFOWHNeff8qZBWP16wMdO2oTjyNxmiR29+7d6NatGw4ePIitW7ciJiYG9erVQ0REhNahEVFy/fYb8PXX8v+jR2WnEgt6WjR9uuT7xoKCpLSUu27mSC5vb2DkSGDbNvNZ65gY2e3drp0s2SByJI8fS2tZY0FBwK+/uvcyAj2dojjn8efDhw8REhKC3bt3o0aNGkl6THh4OIKCghAWFoZA04VSRGQff/wBtGhhvrNm4UKgVSttYnJQGzYATZqoXyovL2DzZpnApuS7fx/4+GNZR2yqVCmZ8S5QwP5xEVnSsqX0eDE2d64cdLmypOZrTjMTayosLAwAEBwcbPU+UVFRCA8PV12ISEPbtkkxT9MEdswYJrAmTp2SZMv0pfr1VyawryNLFvk11J8IMHb6tNTY3brV/nERmVq1yjyBbdRI2kmTcMokNj4+Hl999RWqVq2KkiVLWr3fqFGjEBQUZLjkcvcV0ERaOnxYWspER6vHBwyQ7eJk8PQp8MEHwPPn6vEhQ/gBlhq8vICffwZWrJAd3saePpXKBZMmcZ0saefhQ6BLF/VYhgzSTprLCBI4ZRLbrVs3nD17FkuXLk30fgMGDEBYWJjhcuvWLTtFSEQq588DDRsCpmvYu3SRxYpkoCiy3+3aNfV4q1bAsGGahOSyPvoIOHJEmsIZi4sDevWSkkZRUdrERu5LUWQdrGkJuMmTgezZtYnJUTldEtu9e3esW7cOO3fuRM5XFEb09fVFYGCg6kJEdnbzJlCvnuxQMNaiBTBlCqcVTEyaBKxerR57801u5LCVokXlJMEHH5jf9ttvwDvvAA8e2D8ucl/z5pm/BzRrZrmtsrtzmiRWURR0794dq1evxo4dO5AvXz6tQyKiV3n4UBLY27fV4/XqyUYuT09t4nJQhw6Zr9XMmBFYtgzw9dUmJneQLp0sLRg61Py2v/8GKlaUerNEtnbtGtCzp3osUyYpA8eDWHNOk8R269YNixYtwuLFixEQEIDQ0FCEhoYiMjJS69CIyJJnz2RxoWk958qVpUKBj482cTmoJ09kcjo2Vj2+cCELmtuDh4cs11ixAkiTRn3bzZtAtWrAunWahEZuIi4OaNPGfC38r7+ynJ41TpPETp8+HWFhYahVqxayZctmuCxbtkzr0IjIVHS0nJ89dkw9Xry4FDg13U3j5uLjZcPWzZvq8QED5DiA7Oejj2T21fTAISICaNoUmDiRG77INn76SX73jHXoIL93ZJnT1olNCdaJJbKTXr1kcaexPHnkHTpHDm1icmA//yxduYzVqAFs3y476cn+7t8HPvzQPKkAgG7dgAkT+LOh1HP0qKx9Nz4Tkz+/LGMJCNAsLM24fJ1YInJgffoARYokXM+cWXqkMoE1c/CgzLgay5wZWLKESZKWsmSRgwhL5YunTgXeew9g6XFKDS9eyO+ZcQLr4QEsWuSeCWxyMIklotSnn3WtUgVIm1aWEBQurHVUDiciQtbAxcUljOl0UuCcpXS05+srPeu//978to0bgapVzZeAECVXv37mWwcGDZKZWUock1giso2MGaU10vbtsr2bzPTvD1y+rB4bPBioU0ebeMicTic/k8WLzfcinj0rx2knTmgTGzm/detkZt9YxYryO0evxjWxREQa2L7dPFmtXBnYt4/LCBzV339Lvc5Hj9Tj6dIBy5dzEx4lz+3bQJky6hLa/v5yUGS8GssdcU0sEdnHvXvcrp1MYWFA+/bqMX9/YP58JrCOrGpVqeVbtKh6/PlzoEkTYPZsbeIi5xMbC7Rsad4DZvx4JrDJwSSWiFLuxg2gbFlpH2ta4JSs+uorwLQL9ujR/PByBvnzA/v3S/UIY3FxQOfOspaRx3T0Kj/8AOzZox778ENpN0tJxySWiFLmyROgQQOpRTRrlpxnjYjQOiqH9+ef0lbSWO3aQPfumoRDKZAhgxTb+PRT89t+/BFo3RqIirJ/XOQcdu0CRoxQj+XJIzP57MqVPExiiSj5Xr6UGkPGW2rXrzevFUUqjx7JbJ2xgABg7lwpqUPOw9dXSiBZ+pX//XdZH/v0qd3DIgf38KEsI4iPTxjz9JSSehkyaBeXs+LbJhElT1ycFDU0rQJfsqTlWkRk0LWrTFwbmzBBZmHI+Xh4yMzrjBnmByE7dwLVq5svGyH3pShAu3bA3bvq8ZEjWU4rpZjEElHSKYo0MvjjD/V4jhzAhg1A+vSahOUMVq8GVq5UjzVubL7Bi5zPF18Af/0lJZGNnT0rycmZM9rERY7ll1/kbdJY3brAN99oE48rYBJLREn3yy/m7WQDA6Xyu2mzeTIICzNf8xoczDVwrqRhQ2D3bun0ZezOHaBaNWDHDm3iIsewbx/w7bfqsSxZgIULuZTodfClI6KkWbYM6NtXPebtLVOMb7yhTUxOYtAg81OIkyYBWbNqEw/ZRvnywIED5lUmwsNlD+Tvv2sTF2nr3j2geXPzAi4LF5of9FDyMIklolfbs0f6o5qaOxd4+237x+NEDhwApk1Tj9WvD3z2mTbxkG3lyyfLxatWVY/HxMhS8lGjWILLncTEAB9/DISGqscHDZKlBPR6mMQSUeLOnQOaNgWio9XjP/4o22zJquhoqUZgnLT4+wPTp3MZgSvLmBHYulXqfpoaOBD48kuWVXYX334L7N2rHqtbFxg+XJt4XA2TWCKy7u5dy7WCunYF+vfXJCRnMnasbO4x9v33MltHrs3fX1rRfvWV+W0zZgAffMCyyq5u2TLZRmAsd25g8WIpq0WvT6co7nNiI6m9eIkIspCvRg3g1Cn1+HvvAatW8V34FS5flqXCxkXvy5QBjhxha1l388svspzc9NO2UiWpahASok1cZDvnzsnP1/hAxcdHlppUqKBdXM4iqfkaZ2KJyLLz54GrV9VjlStLVW4msIlSFCm7ZJzAenhIYzMmsO6nd2+ZlfP1VY8fPiwluC5f1iYuso3wcMsz7VOnMoFNbUxiiciyypVlQ5d+C33BgjJtlCaNtnE5gQULpNi9sZ49gYoVtYmHtNe8uayTNe3KdO2aJLKmvUPIOcXFyVYB42aGAPD550DHjtrE5MqYxBKRdWXLAgcPAjVrSi3YzJm1jsjhPXkCfP21eix3bvNe6eR+qleXZNW0Q9vjx8A778hsLTm3b78F1q1Tj5UrB0yZok08ro5JLBElLk8eYNcumYmlVxo8GHj0SD02bRqQLp028ZBjKVZMjgvLlVOPR0UBn3wC/PQTS3A5q99+A8aNU49lzCid+vz9tYnJ1TGJJSJKJSdOyM5zYx98ADRqpE085JiyZpXuXpZ+L/r3l+IfLMHlXHbvBrp0UY95e8seWFYjsR0msUQErF9vXpGfkiU+HujWTf7V8/c3L7FDBMjM/Jo1krCamjkTaNJENgiR47t6VQ5WTQ88ZsyQAi9kO0xiidzd/v2y66RbN2DIEJ7LTKEFC6Q7l7FBg2Q9LJElXl6yY33sWPPmF5s2SdevGzc0CY2S6OlToHFjWQtv7OuvZTMX2RbrxBK5s5MnpW3sf/8ljHXuLLOyLKOVZE+fAoULAw8fJowVLCiNDkzLKhFZ8scf0pb25Uv1eEiIzNi++aYmYVEiYmIkgd2yRT3epAmwejXfQl8H68QSUeLOngXq1FEnsIDsMPHgW0NyDBmiTmABYNIkJrCUdB9+KGXZTAuAPHgA1K4tXZ7IccTHAx06mCewpUoBv//OBNZe+ElF5I7On5eaPo8fq8cbNQJmzzY/t0lWnTolp4SNNW0q3XqJkqNKFeDQIaB4cfV4VJTUHh06lKt9HEW/fsDCheqxkBDgzz+BgABtYnJHTGKJ3M2lS7KE4MED9Xjt2tLs3dtbm7ickKIA3burN3P5+QETJmgWEjm5fPlkmXr9+ua3ff+9lOEy7QRF9vXzz+altPz9ZdmHaQ1gsi0mseQanj8Hjh+Xeibjx8thMpm7elUS2NBQ9Xj16uzGlQKLFwP79qnHBgwA8ubVJBxyEUFBUjC/e3fz25Yv54YvLc2bZ/7x4ukJrFjBdcta4MYucm5nz8oh8eLFQHS0+rawMMDSzzkuDihSRLpQff458NZb7nH6/N9/pd7LzZvq8SpVZGEXz4ElS0SE/BrduZMwli8f8M8/LGxOqWfKFKBXL/VsPwBkyiSJU61amoTllv76C3j/ffkIMTZ/PtCmjTYxuSpu7CLXpSjAtm1AgwbAG2/IobFpAgtI0mbJ1atymTMHqFYNKFpU2uTcu2fTsDX1zz/yvZomsBUqSDtZJrDJ9tNP6gQWACZOZAJLqat7dynjHBSkHn/0SPZlTpnCdbL2sG8f0KKFeQI7diwTWC0xiSXnoSjA0qVA2bJA3brA5s2J39/a+bZ//lFfv3RJ2uTkyiW1UXbtSo1oHcf+/bJc4PZt9XiZMvIapk+vRVRO7d9/ZV2csXr1pNwOUWpr0AA4fFiOt43FxQE9egAdO8rmL7KN/fuBhg3Ny5/16wf07atNTCSYxJJzCA8HPv4Y+PRT2Q5uiYeHVJavUUMOjU1r1eiZJrF6cXGyEK12bdm5v39/6sSupQ0bLJfRKlkS2LoVCA7WJi4n9+236g80T0/pzOUOq1JIG4ULAwcPynG2Kf1JJa6TTX379skmu2fP1OPt2wOjR2sTEyVgEkuO78wZoGJFWQBmSaZMwLBhslnp33+lifX8+bLW05LWreXdp3Bh619zxw7ZPdGwIXDs2Gt/C5qxNMtapYrMNmfKZO9oXMK+fcCyZeqxrl3NyyIRpbagINkB/9135rcdPQqUKyfH4ZQ6du+WWfDnz9XjTZoAs2bxoNURcGMXObZFi6SDVGSk+W2FCsm5nDZtUrYQUVFktnXOHMlKEqtb06wZ8OOPQLFiyf86WjPejdCokXyvadNqHZVTio8HKlVSH9dkyABcvgxkzKhdXOR+Vq4E2rYFXrwwv+3bb4EffpC2tpQyO3fK8iDT17dxY3nt2cjEtrixi1zDvXvmCWyGDLI29sIF4IsvUr6TRqeT2dbffpNZ3KlTgezZLd93zRrZRGa6ENIZNGkiDQzatZNeiExgU2z+fPOJ+eHDmcCS/X30kSwvsHRC6aefZEWUK+9VtaXt2+V43zSBbdqUCayj4UwsOTZFkVnEtWvleoUKsqzAVoU4IyOBmTOBUaPMmwEAUoqqbl3bfO3XpSiJn9961e2UqGfPJGEwLrFbrJgs0WZ/CNJKeDjQqZPUjzUVEiLFW9g9LunWrQOaNzffxPX++zJ34uOjTVzuhjOx5Bp0OnkXzp9fFh7u22fbSvL+/sBXXwHXrsl0hvHGpwYNHDOBVRTZVdSmTeK1dpjAvpZRo8x7RIwfzwSWtBUYKMnVpEnmv4sPHsiy/h49LK/IIrXp02W21TSB/egjWYXFBNbxvNZM7MuXL+Hn55ea8dgUZ2Kd2NOn2pSCCg8HxoyRPqIHDsiSAkvu3AGyZpVt6vb0+LFsk/3rL7k+c6asIaZUdf26zLoalzFq2FDqdxI5ikOHZBbx1i3z24oVk54wZcrYPSyHFx8v64jHjjW/rUUL2ZrBg1X7stlMbHx8PEaMGIEcOXIgXbp0uHbtGgBg8ODB+O2331IeMbm3mBjzLaDGtKplGhgoOyTu3LGewMbHS0ZTurQsmDJtrWMLL19Kp7IiRRISWEBa+5w5Y/uv72YGDFAnsF5eMgtL5EgqVwZOnJD1nKbOn5dNiT//bJ+3KGcRGSnVGy0lsG3aAL//zgTWkSU7if3hhx8wb948jBkzBj5Gc+slS5bEr7/+mqrBkZuIj5dNR3XqAE+eaB2NZabtcoytWgWcPi31Z5s3lzo3Cxda3jb8umJjgV9/lcoMX38tM7HGXr6UbbWUag4cMC+p1a2bHD8QOZqMGeW4dupUwPREaUyMFOivXVt6vLg7fdezlSvNbxs8WFayscKDg1OSqUCBAsq2bdsURVGUdOnSKVevXlUURVHOnz+vpE+fPrlPZ1dhYWEKACUsLEzrUEgvPl5RvvhCUWQ1p6KUKqUooaFaR5V0sbGKUrx4QvzGl8BARencWVEOHZLv83VERSnKsmWKUqSI5a8FKErmzIqyaVPqfF+kKIr82KpUUb/MGTIoyuPHWkdG9GrnzilK2bKW3y58fRVl5EhFiY7WOkptHD+uKAUKmL8uXl6KMmeO1tFRUvO1ZM/E3rlzBwULFjQbj4+PR0xMTCqk1eRWBg2SdZx6p0/LBipnOd8VGmr9UD08XCpiV64sSxFGjZImCqbds6y5e1fKf33wgUyvfPwxcPGi5fu+/75sk69fP2XfB1m0fLmUMTI2ZAgbnZFzKFZMfn+//dZ8X2dUlLz9li8vLW3dhaLILHWVKsDVq+rbAgOBjRtlmwE5h2QnscWLF8fevXvNxleuXImyZcumSlDkJlatksTOmI+PVAXwcJLCGTlyyCK05csTb9n0zz/AwIFSvDE4GMiXT5JTa6f+P/9cnrtjR6ntam29cO3a8im1ahWQLdvrfz9k8PIl0L+/eqxgQeDLL7WJhyglfHykQeHOnUCePOa3nzkjCd1XX5m3VnU1T59KpYHu3YHoaPVtuXJJ8Zs6dTQJjVIo2ZnCkCFD0L17d/z000+Ij4/HqlWr0KlTJ4wcORJDhgyxRYzkiq5eNT/c9fSUWjH16mkTU0p5eMha2NOnZXFVgwavLmd144Ykp6ZrWvUsnO1QKV9eatZu3y4zvZTqJk0y70U/ZgzL7JBzqlkTOHsW6N3bfI5AUYCJE+VtZ+ZMWXrvag4dAsqWleN9U5UqyVyAtb275MBSslZhz549Sp06dZTMmTMr/v7+StWqVZXNmzenaN2DPXFNrIOIjLS8UGvaNK0jSz03byrKiBGKkj+/9TWsgKJcuWL58SdOWL5/pUqKsnLl66+xpUQ9eCBLmo1f+urV+bKTazh8WLYfWHtbKlFCUTZu1DrK1PHypbwVe3lZ/l779pUtB+RYkpqvsWMX2V+XLup1sADQsqXs6He1gvzx8cCePbK9/cgROXenP48VFCTrYy19z4oC5MwJRETIzHSjRjLDmyWLfeN3U926AdOmqceOHJGGcUSuICZGqvQNG6YuH2esXj0pyVWqlF1DSzVbt8rSAUuVGIKDpY1048b2j4teLan5GpNYsq/ffwdatVKPFSsmOwvSpdMmJnuKiZGCjSdOAGFhQM+e1u97+bJ0J2ORQrs6f15OK8bFJYy1aiXHWESu5vJloE8fabdqTbNmsjmsShW7hfVabt+W72nFCsu3V6sGLFki8wTkmGyWxGbIkAE6CzNHOp0Ofn5+KFiwINq1a4f2Dri9j0msxs6fBypWlNlFvTRpZIorsU1RRHbUpIn6A93PT2ZycuXSLiYiW9uxA+jbFzh50vp9atWSzY716jnmSbPISKk8MGyY+mNGT6eTxiXDh7P+q6OzWceuIUOGwMPDA40aNcLw4cMxfPhwNGrUCB4eHujWrRsKFy6Mrl27Yvbs2a/1DZCLiYiQbaGm7ywzZjCBJYexY4f5jFTfvkxgyfW9/TZw9CgwZ471Qie7dsmqpnLlgLlzHaeawaNHwPffA7lzA998YzmBrVRJvr+RI5nAupJkz8R++OGHqFu3Lrp06aIanzlzJrZs2YI//vgDkydPxqxZs3DGwdpfciZWQ198ITVTjXXsCPBghxxEfLyseT1xImEsJAS4cgUICNAuLiJ7i4iQ9bK//CJlqazx95dKgW3aSPVAT0+7hQgAuHZN2j/PmSOzsJYEB0uJsQ4dnKdyI9lwOUG6dOlw8uRJs4YHV65cQZkyZfD8+XNcvXoVpUqVQoSlwyENMYnVyI4d8g5nrHRp6efp769NTEQmFi6UD2NjM2bI8ReRO3r2TOYexo+X3iuJyZ4d+Owzmal96y3bvbXfvStnS/78UxoTJNYXp2NHKUWeKZNtYiHbsdlyguDgYPz1119m43/99ReC/9/GJiIiAgGcuiA9T091le20aWXFPRNYchCRkdKLwlixYjJ7Q+SuAgJkOc21a8CvvwKFC1u/7927wNix0iwgfXpZPzt8OLB3r/VZ0qR4+RI4dkyWC1SoID1gvvgCWL/eegLboIHUfZ09mwmsq0v2ypDBgweja9eu2LlzJypVqgQAOHLkCDZs2IAZM2YAALZu3YqaNWumbqTkvPRVtgcOBKZMkXM7hQppHRWRwYQJsqPZ2M8/c+0cEQD4+soBXbt2MgM6Z47MghpX8DAWHQ3s3i2XYcNkLHt2IH/+hEuePOaFVxRF1rdeviybKS9fBm7elPFX8fICPv0U+Ppr5y0JRsmXohJbf//9N6ZMmYKL/+/jXqRIEfTo0QNvvfVWqgeYmricwAEcPw6UKcPFSeQwHjyQTkXGm1TefhvYts0xd2ATOYIHD6RM1fz56nXk9pYunczM9urFDZiuhHViLWASS0SmTBsb6HRy+rJsWe1iInImZ85IQrt9u1QASGydamoICgLefVfK4TVqJNfJtSQ1X3utk2UvX75EtL770P8xOSQiZ3HxonnzuFatmMASJccbb8gFkB4ue/cCO3fKnt5Tp5K2HOBVChaU7lrvvSfNCtgDhoAUzMS+ePEC/fr1w/Lly/H48WOz2+OsLZJxAJyJtZPHj6WuCc/FkoNr1gxYuzbhup+fJLa5c2sWEpFLef4cuH5dNocZX+7eVSe3+o+LNGkkYS1USDaSFSok17lX3L3YbCb2m2++wc6dOzF9+nS0bt0aU6dOxZ07dzBz5kyMHj36tYImFxAbK+1cgoOlNku+fFpHRGTR7t3qBBYAevdmAkuUmtKlU8/UEqWmZM/E5s6dGwsWLECtWrUQGBiI48ePo2DBgli4cCGWLFmCDRs22CrW18aZWDv45RdpWg3IIfWoUbLo0N5VsIkSER8PVK4s6/f0MmeWxgZ8ayAi0pbN6sQ+efIE+fPnByDrX588eQIAqFatGvbs2ZPCcMkl3L+fUE8FAF68kAWHsbGahURkyZIl6gQWAIYOZQJLRORMkp3E5s+fH9evXwcAFC1aFMuXLwcgzQ7Sp0+fqsGRkxkwAAgPV4/Nni1FBokchKXGBkWKAJ07axMPERGlTLKT2Pbt2+PUqVMAgP79+2Pq1Knw8/ND79698c0336R6gOQkDh8G5s5Vj7VuLf0HiRzIpElSQN3YmDHc7UxE5Gxeu07sv//+i2PHjqFgwYIo5eBtMrgm1kbi44EqVYAjRxLG0qWTlivZsmkXF5GJhw9lp7PxCYOaNaUcEItpEBE5BptVJ3j58iX8/PwM1/PkyYM8efKkLEpyDfPnqxNYABgyhAksOZzhw81XvIwbxwSWiMgZJTuJTZ8+PSpVqoSaNWuiVq1aeOutt+Dv72+L2MgZhIUB/furxwoXlh6ARA7k4kVgxgz1WKtWQPny2sRDRESvJ9lrYrdt24YGDRrg0KFDaNq0KTJkyIBq1aph0KBB2Lp1qy1iJEc2bJg00TY2cSLg46NJOETWfPstYNyLxc8PGDlSu3iIiOj1JDuJrVatGgYOHIgtW7bg6dOn2LlzJwoWLIgxY8agQYMGtohRZerUqcibNy/8/PxQuXJlHD582OZfk6w4dw6YPFk99t57gB1+D4iSg40NiIhcT7KXEwDApUuXsGvXLsMlKioKjRs3Rq1atVI5PLVly5ahT58+mDFjBipXrowJEyagfv36uHjxIkJCQmz6tcmEosiSAeOpLV9fYPx47WIisiA+HujbVz2WObP5KhgiInIuya5OkCNHDkRGRqJWrVqoVasWatasiVKlSkFnh50RlStXRsWKFTFlyhQAQHx8PHLlyoUePXqgfxI+kVidIBWtWwc0aaIeGzQI+OEHbeIhsmLRIqn2ZmzqVODLL7WJh4iIEmezjl2ZM2fGixcvEBoaitDQUNy/fx+RkZGvFWxSREdH49ixY6hTp45hzMPDA3Xq1MGBAwcsPiYqKgrh4eGqC6WC2FigXz/1WI4c0uyAyIG8eGH+a1m0KNCpkzbxEBFR6kl2Envy5EmEhoaif//+iIqKwsCBA5EpUya89dZbGDRokC1iBAA8evQIcXFxyJIli2o8S5YsCA0NtfiYUaNGISgoyHDJlSuXzeJzK9HRQKNG6s1bo0cDadNqFxORBWPHArdvq8fY2ICIyDW8VrODx48fY9euXVi7di2WLFmC+Ph4xBmvkUxFd+/eRY4cObB//368+eabhvF+/fph9+7dOHTokNljoqKiEBUVZbgeHh6OXLlycTlBarl+XZYQXL4MHDoEeCT7mIjIZu7eBQoVktlYvXfeAbZuZV1YIiJHZrNmB6tWrTJs6Dp37hyCg4NRrVo1jBs3DjVr1nytoBOTKVMmeHp64v79+6rx+/fvI2vWrBYf4+vrC19fX5vF5Pby5QMWL5Zm9ExgycEMGqROYD08ZN8hE1giIteQ7CS2S5cuqFGjBjp37oyaNWvijTfesEVcZnx8fFC+fHls374dzZo1AyAbu7Zv347u3bvbJQaygs0uyMEcPy6N5Ix16AA4eGdsIiL7UxTgyhU5TbV7t5xlvX0bWLhQTl85sGQnsQ9MC9vbUZ8+fdC2bVtUqFABlSpVwoQJExAREYH27dtrFhMRORZFAfr0kX/10qUDvv9eu5iIiBzK48eStG7bJv/evGl+n1u37B9XMqWoTqxWPv74Yzx8+BBDhgxBaGgoypQpg02bNplt9iIbUBSehyWnsGaNTCYYGzgQsLLqiIjIfZw5A4wbJ0sBY2ISv6/prlgH9Fobu5wN68S+hg8+AEqXlqrx6dJpHQ2RRVFRQIkSwNWrCWN58gAXLkibWSIit6MowPbtUq5l8+akP65LF2D6dNvFlQibbewiN7R5M7B6tVxmzABGjADatwc8PbWOjEhlyhR1AgtI9TcmsETkdhQFWLECGDkSOH361ffPmhWoWxd4800gVy6gWDHbx/iaOBNLiYuLA8qWlVMQetmzA5cusS4sOZSHD6WkVlhYwtibbwJ//82VMETkhsLCgMKFAWt7mXx9gTp15FK3LlC8uMO8WXImllLHokXqBBaQ1rJMYMnBDBqkTmABltQiIjcWFCSnp1q0UI9nygR07y69tzNn1ia2VJLsmdiIiAiMHj0a27dvx4MHDxAfH6+6/dq1a6kaYGriTGwyvXwpR3HGOxRLlgROnuRSAnIox48DFSqoKxJ89hnw++/axUREpDlFAT76CFi1Sk5V9e0LtGnj8KUxbTYT27FjR+zevRutW7dGtmzZoOM0h+uaMsW8xMZPPzGBJYeiKEDPnuoENm1aaS9LROTWdDpg6lTgk0+ADz90ucZEyU5iN27ciPXr16Nq1aq2iIccxX//AT/+qB6rVQt4911NwiGyZskSWfdqbOBAIEcObeIhIrKb+HigXz/Zq9Knj+X7ZM0KNG9u37jsJNlJbIYMGRAcHGyLWMiRjBoliayxMWO4wJAcSkSEvH8by5/f+ns5EZHLiIwEWrcG/vhDPpvz5JHZVjeS7HnlESNGYMiQIXhh3JScXMvNm8CkSeqx5s2BihW1iYfIilGjgDt31GPjx7OkFhG5uIcPgbfflgQWkPVUrVoBBw5oG5edJXsmdty4cbh69SqyZMmCvHnzwtvbW3X78ePHUy040sjQoVI1Xs/Ly3xpAZHGrl2T2t3G6tYF3ntPm3jIxPPn0vHn9m050rh9G7h3DwgPT7g8eyb/NmokRx+WTJ8uH8yZMiVc8ucHihaVNSM8O0Tu5tIloGFD86LYsbHAjRtSW9BNJDuJbdasmQ3CIIdx5gwwf7567IsvgIIFtYmHyIq+fdXHWp6ewIQJzGk0ER8vRxTnz8vl4kXg6dOkP/6tt6zftns3sGyZ5dvSpQOKFJGi7MWKyfNUqcKpeHJdBw/KQd+TJ+rxoCCZlX3nHW3i0kiyk9ihQ4faIg5yFP37q7d5p0sHDBmiXTxEFmzbBqxZox7r0UNqdZONKIr0WvfxMb/NwwP45RcgNDRlz53YjulHj6zf9vw5cOyYXPR8fCSRrVlTTrfWqpWymIgczenTsrna9AAxd25gwwbpue1mUtzs4NixYzh//jwAoESJEihbtmyqBUUa2bVL/hCMffMNEBKiSThElkRHS51uY5kzyyoYSkXx8cCpU8COHXI5cAAYPBjo3dvy/YsWtX8Sa0l0NLBnj1w2bACOHk1ZTESO5OpVoF498wS2XDlg3TogWzZNwtJaspPYBw8e4JNPPsGuXbuQPn16AMDTp09Ru3ZtLF26FJmdvPuDW9u0SX09SxZu8yaHM3asnK02NnIk8P+3I3od164BGzdK0rprl/kpyyNHrD+2WDF5jKngYCBnTlm/miMHkCEDEBgIBATIv4GB0lTFmi++kLgePZLL3buyJvD581d/PzVrvvo+RI7u7l1Z8H//vnq8QQNgxQo5Y+qmkp3E9ujRA8+ePcM///yDYsWKAQDOnTuHtm3bomfPnliyZEmqB0l2Mnq0/FH07SstkIYOdes/DnI8168DI0aoxypWBD7/XJt4nJ6iACdOyNqMtWvldGViEkti69SRjSXFismsbIECkrymSfN6MXbtaj6mKPLBfv48cOGC/HvokHwvxl0kE1tKMGaM3LdLFx4BkeN68gSoX1/e/IzVqCFduBy885atJbvtbFBQELZt24aKJuWWDh8+jHr16uFpchbz2xnbziZRfLz8cTRtCphUnyDS0nvvAX/9lXDdwwM4fBgoX167mJyOokgyumiRJK+mXfle5fFjmV11RGFh0vli1y5ZTrBxo8z8mgoPB3Llkn/TpQM6dQK++krWFhI5iogImYE1LZtVtiywc6ds5nJRNms7Gx8fb1ZWCwC8vb0Rb3wETM7Lw0N6LRM5kLVr1QksAHz5JRPYZPvyS2DGjKTfv1Ah2SBVu7bs/reUFDqKoCApPdSwYeL3mzVLElhAliX88ovUxv74Y2n35oYbZMjBREdL4wLTBLZQIVn658IJbHIku9nB22+/jV69euHu3buGsTt37qB37954x81KOxCRfUREAD17qseyZDFfWkBJ8Kr36cyZgTZtpNTezZuy/nTGDEnwcuVy/hpmsbFSi81UXByweDFQqpQk+g8f2j00IoOYGPNENUcOYMsWbrY2kuwkdsqUKQgPD0fevHlRoEABFChQAPny5UN4eDgmT55sixiJyM398IPkU8bGjeNSRqsS66jYpIn5bGqBAsDXXwN790pDgvnzJZHNlcu2cWrBy0uWUbRoYbkqQny8NFgoVEhmaKOj7R4iEdKmBZYulTc6T09ZwrNlC5A3r9aROZRkr4kFAEVRsG3bNly4cAEAUKxYMdSpUyfVg0ttXBNr4uJFqa/4ySeJl7gh0tC5c0Dp0jKBple7NrB9u/NPCqa6u3eByZNl5nTTJqByZcv3695d1ou2aSNLh4oXd88X89o1mZX97TfriX/hwpJINGrknq8RaW/3bmngYe3v2QUlNV9LURLrrJjEmmjWTBYaVqwodYtq1NA6IiIVRZGEdffuhDFvb9lEX7SodnE5nHv3gGHDgLlz5TQkADRvDixfbvn+ERFSNYBJmXj8GJg4Ud4HIyMt3+f994GZM2W5BRHZVFLzNU6/uavduyWBBWSncs2awMKF2sZEZGL+fHUCC0j/DSaw/xcRAXz/vZz6njUrIYEFpAXltWuWH5c2LRNYYxkzyut48SLw2WeW77N6NfDGG8DJk3YNjYisYxLrjuLjZf2bscyZpaQWkYO4d8+8OVSePMCgQdrE41Di4oA5cyR5HTpUkllT8fHA1q32j82Z5coF/P47sH+/nKEyFRgorzlRaps7V5YDUbIwiXVH8+aZt2IcPlzeoIkcgKIA3bqZd1icOvX1a+c7vX37pNVkhw6S6Zvy9QU6d5YmAF98Yf/4XMGbbwIHDwILFgCZMsmYp6ecrUqbVtvYyPWsWycdW8qVk/rGlGRcE+tu/vtPNioY9yMvWlQWGbKxATmIFStk87ixzz6TSTK39fIlMGSIrNu09Lat08kH4fffA9mz2z8+VxUaKs0QypWTg32i1PTvv7JzNSxMrnt6AuPHm9cUdDM2a3bg6emJe/fuIcSkTtnjx48REhKCuLi45EdL9jN4sDqBBWR3LhNYchCPH8vmeWOZM8u+G7d18iTQujVw9qzl2+vVA37+WWqcUurKmhX48091O1tTz59LhRe3P01AyRIXB7Rtm5DA6sfYOCrJkr2cwNrEbVRUFHx8fF47ILKhkyel/qGx99+XvsxEDuKrr4AHD9RjkycnnNV1O7/9BlSqZDmBLVlSSmlt3swE1pZ0OpkhsyQ+XkqVVa+e/Ba+5N7Gjzffufrxx0CvXtrE44SSPBM7adIkAIBOp8Ovv/6KdOnSGW6Li4vDnj17UJRbhh1XfLwsMjQ+wvP3l2LeRA5iwwZg0SL1WNOm5ksL3Erp0uYzMz4+0gGiTx/ryRXZxw8/SOUCAKhQQapCVKumbUzk+E6dMt+lmiePlHFj5ZAkS3IS+8v/kx1FUTBjxgx4Gr1x+vj4IG/evJiRnH7cZF8LF8qOW2MDB8ofDZEDCA8334cUFARMm+bm7+kVKsgyoGHD5HqZMrLh6I03tIyKAOn8NXRowvUHD4C33wamTJHNdUSWvHwJtGqlLomn08nftWmrWUpUkpPY69evAwBq166NVatWIYNp20JyXE+fSnFNY/o2k0QOol8/4PZt9dj48dyjBEAOODdtAt55RzZ3cemWYyhSREpuXb6cMBYTI0djd+7IgYdbH4GRRd99Z7486Jtv2HAoBVidwB307CmLCo2tXw80bKhNPEQmNmyQrp7G6taVpZ7MAf4vJoYbMB3R06fAp5/KQYapL7+U91629Sa9nTvlYNQ49SpdGjh0SMrjEQAbduz68MMP8dNPP5mNjxkzBs2bN0/u05GtnTolxTWNvfceE1hyGPfvA+3bq8fSppUGVG6TwEZFyYuwbJn1+zCBdUzp00udz379zG+bNg1o2RKIjrZ7WOSAnj6VagTGCayvr2wEYAKbIslOYvfs2YOGFhKgd999F3tYpNfx/PWXelOIn5+U1CJyAIoipU1NqxGMHQvkzatJSPYXHi4lsubNkw+4gwe1joiSy9MT+Okny5tyli6V3YmWuqqRe+nRw7yCxahRUmWEUiTZSezz588tltLy9vZGeHh4qgRFqei772R2JzhYrvfvD+TLp21MRP83bZosJTDWpIkbNZqKiJB1FPoJgKgoSXhu3NA0LEqhzp3l/dZ01nzTJjlQ+e8/beIi7a1bZ1565Z13WE7rNSU7iX3jjTewzMIpr6VLl6J48eKpEhSlshYtZBF5ly6WT3kRaeCff8z3FmbJImVR3WIZwcuXQLNm0kbWWESEeqMQOZfmzWXPgWl72v37gdq1pZsHuZfwcPn8NZY+vZx94Xrp15Lsjl2DBw/GBx98gKtXr+Ltt98GAGzfvh1LlizBihUrUj1ASiXZspk3OiDSSFSUtJF9+VI9Pm+edOdyedHRkuxs26YeDwmRWbuyZbWJi1JH3brA9u2y9+DJk4TxU6dkRnbbNoAVftyHosjPfe7chLFffgFy5tQuJheR7EOAJk2aYM2aNbhy5Qq+/PJL9O3bF7dv38a2bdvQrFkzG4RIRK5m4EDg9Gn1WK9eQIMG2sRjV7GxUiNy3Tr1eHAwsHUrE1hXUbmyLBMxrRF39qwks+Q+goKAOXOk3EqePHKQ07at1lG5BJbYcjWK4ibnYslZbdli3um4ZEngyBHZd+jS4uOlCsGCBerxwECZuatQQZu4yHauXwdq1pQNPX5+0iCBrb7d1/PncsmaVetIHJrNSmwBwNOnT/Hrr79i4MCBePL/UyXHjx/HnTt3UhYtpY4XL6R/98qVWkdCZNG//8oyAmO+vsDixW6QwCqK7E42TWDTpJHdbUxgXVO+fMCOHdIUYf16JrDuLl06JrCpKNlrYk+fPo06deogKCgIN27cQMeOHREcHIxVq1bh5s2bWGD6Bk32M2AA8PffcvnsM2l9yHVX5CAiI4H33zff1zJmjJt0UJ05U8oxGPP1lTJ4VatqExPZR8GCwLlzgFeyP3KJKBHJnont06cP2rVrh8uXL8PPaOqkYcOGrBOrpc2bgUmTEq4vXmxeQZ5II4oi1YdOnFCPN2kCdO+uTUx2deCAdM4z5u0NrFoF/H+DLLk4JrDuIyYGuHdP6yjcQrKT2CNHjuALC0Ucc+TIgdDQ0FQJipLp3Dkpo2XMzw8YPVqbeIhMTJ5sXiKxcGFg4UI3qDBz7x7w4YfywWZswQJ2ziOxYYO0qHWfLSqubexYoFgx2czFn6lNJfvjw9fX12JTg0uXLiGzW9TGcTAPHwKNG0sdOmOjRwNFi2oTE5GR3buBPn3UY+nSyf6WoCBNQrKvYcPMZ2W+/Rb45BNNwiEHs2CBtAKfPl2a0ZBzu3IF+P57ICwM6NBBSmtdv651VC4r2Unse++9h++//x4x/59V0Ol0uHnzJr799lt8+OGHqR4gJSIqCvjgA/M/kI8+kg0kRBq7dUvKocbFqccXLpSJCrfwyy9Ay5YJ1+vUAX74Qbt4yHFMmCCllvR/IGPGAOPHaxoSvQZFAbp2VRfA3rEDePRIu5hcXLKT2HHjxuH58+cICQlBZGQkatasiYIFCyIgIAAjR460RYxkiX6RoWm3n4oVgfnz3eAcLTm6yEg5xnr4UD0+eLA0qnIbadJI1j5hgmzwWbqU6yNJFCwIeHqqx/r2BX7/XZt46PUsXmzewKRHD/lcJptIcZ3Yv//+G6dOncLz589Rrlw51KlTJ7VjS3UuVSd21CipGG8sZ07g8GHpzkWkoZgYSWBN6/k3bCib8d32GOvlSzeoJUbJMm+e+SZcLy/542E5Lufx5Iks4TM+as+ZU/asBARoF5eTSmq+lqQkNjg4GJcuXUKmTJnw+eefY+LEiQhwwh+KyySxf/whSwaMpU0rs7JlymgSEpFefLycITXdyFWwoDQ0SJ9ek7CIHNdPP5mvh02TRhaUs36wc+jQQTZyGVuzBmjaVJNwnF2qNjuIjo42bOaaP38+Xpo2PCf7WbrUvFq8TienMZjAksYUBfjqK/MENihI3s+ZwBJZ0K+f/OEYe/FCNu3++68mIVEy7N5tnsC+/z4TWDtI0sKsN998E82aNUP58uWhKAp69uwJf39/i/edY/qDpNShKFJxwHQJAQD8/LPsbiXS2PffSzktY/7+cma0RAltYrKrPXukyciUKUBIiNbRkLPQ6YBx44AHD2RCQu/+faBRI2lg4xalPJxQVBRgWnY0IEBdt51sJkkzsYsWLULDhg3x/PlzAEBYWBj+++8/ixeykfBwYNYs8/FOnczrFxFpYPJkqSZlzMtLVr9Uq6ZJSPYVESFrG1eskIx9xQqtIyJn4uEBzJ1r3vzin39k+ZhpnWFyDKNHAxcvqsdGjpT1sGRzyd7YlS9fPhw9ehQZM2a0VUw24/RrYs+fB956C3j6VK5/+SUwcSJ3OpPmFi0CWrdWj+l0ssn600+1icnuevY0n4Zeu5ZnSSh5nj6V9/nz59Xjn38O/Pqr/GGRY7h4EShVCoiOThirWFE69JlWnaBkSdU1scHBwXj0/zpntWvXho+PT+pESclTrBiwerX0Wx83Tk5ZMoEljU2aBLRpYz4+ZYobJbC7d5snsG+9JaeCiZIjfXrp4GW6HGXOHKlKQ45BUYAuXdQJrKennDFlAms33NjliP6/bMOiWrWAq1dlCQGPyElD8fHAN98AvXqZd1YcMUJOFLiFiAiZJTPm5yenhvlhRimRN68sJDfdezJokKyPJe3Nmwfs2qUe++orbrC2M27sciRhYbJJa9Ik4NAh6y2NcuSwb1xEJqKigHbtpFiGqT595LPWbfTvD1y7ph778UegcGFt4iHXULGibPL64IOEo8ShQ2WGn7QVGWleEi1PHmD4cG3icWPJ3til0+m4sSs1RUbKjubhw4ECBWRB+LNnlqsQEDmAp0+lBrulBPaHH4CxY93oJIG+GoGxqlVlfSzR62rWTJaOeXvLzN+wYW70x+XA/P2BjRuB8uUTxqZNk3rtZFfc2GVrP/4I+PjIL32aNPKvogBHj8ppoePHre863b8fePNN+8RJlAQ3bkjpyn/+UY97ecmek7ZtNQlLG7Gx8iF2+nTCmL8/cOoUUKiQdnGRa1EU4PJlzuw7othYOYg9cwb47Teto3EpSc3Xkr0r6Pr1668VmFuJj0/5eVUPD9nhyCSWHICiSKWBbt2k2puxdOmkjFa9etrEpplff1UnsIActDKBpdSk0zGBdVReXuZNKsiuktzBvGHDhggLCzNcHz16NJ7qSz0BePz4MYoXL56qwTm9yMiUPe7994GzZ1n/lRzCf/9JlYHWrc0T2KxZ5Yy62yWw//0HfPedeqxUKaBHD23iIfdl+kdJ5EaSnMRu3rwZUVFRhus//vgjnjx5YrgeGxuLi6YFf91dUpNYDw/Z0ditG3DwILBqlfVNXUR2tHOn5GbLlpnfVrSonCwoW9b+cWlu+HDg8WP12IQJrEZA9nXypDTWmDZN60iINJHk5QSmS2eTuZTWPXl4yML8yEi5vHgh/0ZHy07GqlXlUrky4IzNF8hlhYVJC9lffjEvnwXI2tdJk9z01/bcOfPNXB9+CNSurU085J7WrgVatpQSbz17ysbg+vW1jso1xcUBCxYArVrJJjtyGKyUb0vBwdKcgMhJREcD06dLnVfTiUYAyJABmDkTaN7c/rE5BEWRNXBxcQljvr5SGo/IXi5ckNJb8fFyPS4OaNFCTo1wWV/qmzwZ6N1bzrbMmiUTT+QQkrycQKfTQWdS2sP0OhE5p/h4KZlVtKjkaJYS2HfekU24bpvAApLlm/ZE//prIF8+beIh91S0qHkZxvBwKR3y8KE2Mbmqq1cTXuvTp2Wz9ejR2sZEBslaTtCuXTv4+voCAF6+fIkuXbog7f/rohmvlyUi5xAbC6xfLzOvx45Zvo+Pj7xn9+olK2Tcmq+vtP/s2lVekH//NS96TmQPw4cDFy8CK1YkjF2/LkvYtm+XrnH0euLjgY4d1ftbFEWWAZJDSHKd2Pbt2yfpCefOnftaAdmSJnViiRzQrVtSIeq334A7d6zf7/33pV17kSL2i81pKApw86asbyfSQmSktCI/fFg93qqVrOHk2dLXM2OGHLAa695dlheQTSU1X0t2swNnxiSW3FlkJLB1KzB7NrBhQ8JyOkveegsYM4YTDkQOLzQUqFRJjkyNjRhhXgaOku7mTan88Px5wliePFL+Ml067eJyEzZrdkBEzkFR5Gzjpk1y2b0bePky8ccUKSJLB5o25SQOkVPImhVYt06OOI0TrsGDpUlCixbaxeasFAXo3Fn9egIyA8AE1qEwiSVyAYoC3L8v+w70lz17ZMlmUlSuDHTpImchvfiuQORcSpWSnZnvvac+xdK2LZA7N1ClinaxOaP584HNm9VjHToAdetqEw9Z5RTLCW7cuIERI0Zgx44dCA0NRfbs2dGqVSsMGjQIPj4+SX4eLieg5IiOlo2+jx5JKcaoKPUlJkZmKz09ZcOTp6dcvLxk/4/+4uenvu7rK5ulfH3lvtZmPOPj1eWFIyMllrt3gXv3Ei43b8oZruRuSg4IkDKTX3whvTbIilGjpCLBZ5+xmQE5tokTzdugZsokTXQKFNAkJKdz964sIzDqSIrs2YF//gHSp9cqKrfjUssJLly4gPj4eMycORMFCxbE2bNn0alTJ0RERGDs2LFah0dOKjoauHwZOH9e6tefPy/Lyh48kITQ+D3MVnQ6SYCN/9XpJIGNjk79r+fhIRVi2raVVrI8M/YK//4LDB0qRyw//ggMGyY1xty+TAM5pJ49ZQ3R9OkJY48eAe++C+zfLwktWadfRmD65j9zJhNYB+UUM7GW/Pzzz5g+fTquXbtm9T5RUVGq0l/h4eHIlSsXZ2Ld1K1bUnlmxw7ZzHvlirpmvavKkQNo0EAu77wjDQsoiTp1kjIOer6+UjcyRw7tYiJKTGws0KSJLIQ3NmCAHIiRddOmSft3Yy1bAosWaROPG3OpmVhLwsLCEBwcnOh9Ro0aheHDh9spInI0z54BGzdK0rp9uyStri5NGuCNN2SJXKlSUn2nRAlu0kqRK1cA05KBXbowgSXH5uUFLF8O1KgBnDwpY127Sh9psu7cOaBvX/VYlizSpYscllPOxF65cgXly5fH2LFj0alTJ6v340ys+4mPl134c+cCf/wh60lfl4eHnHY3Xdfq7S1nn+LjZUZXf4mJUa+dfflS7pca0qYFsmUzvxQqJElr/vw8051qWrdWz8D4+wPXrslucCJHd/eurB3q3l26yvFI1rqoKNndeuqUenzjRjmFRXbnFDOx/fv3x08//ZTofc6fP4+iRYsart+5cwcNGjRA8+bNE01gAcDX19fQYYxc27VrsqF0/vyk78jXy5EDKFZMLoULy8F3SIhcMmcGgoNfLzFUFElso6PlvVL/r/7/8fFyH31CrCjyeePvLzOrxv+ycoCdnDsH/P67eqxHDyaw5Dz0m5G48P3VYmKA0qXVSWzPnkxgnYCmM7EPHz7EY0tN2o3kz5/fUIHg7t27qFWrFqpUqYJ58+bBI5mZBasTuJ6zZ+Us2cqVSZvtDA4GateWS4UK0oI8KMj2cZKTadFC3c4zXTpp6cmNMUSua+lSWTKUMydw5IjMHJAmnGImNnPmzMicOXOS7nvnzh3Url0b5cuXx9y5c5OdwJJrOXMmIXlNjKcnUKeOlPd7+2052OavDiXq5El1AgsAvXszgSXXcvu2rPuuVUvrSBzHJ59Iu8KICCawTsIpTk7euXMHtWrVQp48eTB27Fg8NCqImZWn99zKmTPA8OGy3jUxJUsC7dvLxtIsWewTG7mIoUPV19OnB/r00SQUIpu4fFmO7B8+lF7Ub72ldUSOI3durSOgZHCKJHbr1q24cuUKrly5gpw5c6puc8J9aZQCYWHAoEFSAcXajzwoSPbitG8PlC3LfQyUAqdPA3/+qR77+mvWiCTXcfIkUL++FMQGgEaNZDdsqVKahkWUEk5xYrVdu3ZQFMXihVybogBLlsja1alTLSewQUEyO3vjBjB5MlCuHBNYSiHTjabBwbLBg8hVzJiRkMACUti/fn2pf+wu/v5bDljJ6TlFEkvu6dIlOeP12WdAaKj57enTJySvQ4Zwsoxe07VrsrHDWM+e0p+XyFVMniyzr8ZCQ+XN9u5dbWKyp5s3gWbNpPyY6dp3cjpMYsnhxMbKpq033pAmBab8/GTZIpNXSlU//yw1zvTSppWyWkSuxNtbmiFUq6Yev35ddr+6ciIbGQm8/7604n3xQqqQDBjgHq0bXRSTWHIoN24ANWtKkhodbX57o0ZSwnPYMJbGolQUGmreneuLL2Q5AZGrSZMG+OsvoEwZ9fjFi/IGfOuWJmHZlKIAnTsDx4+rx8+c4fozJ8YklhzGsmXynrp/v/ltOXMCq1bJ+26+fHYPjVxdUBAwbhyQJ49c9/ZmRQJybenTA5s2Sbs/Y1euSCJ744YWUdnOhAnqDnyAdLdZtIh1F50Yf3KkuefPpaLAJ59IFQJjnp6yOfz8eTkLxANmsgl/f6BbNyk9tHChnArIkUPrqIhsK0sWYOdOSeaMXb8O1KjhOpu9tm+XDxJjAQHAmjVcj+bkNO3YZW/s2OV4TpwAPv5YcgdT+fNLZYJKlewfFxGR2wgNBd55R9ZqGcueHdixAyhSRJu4UsOJE7LW9+lT9fjatcB772kSEr1aUvM1zsSSZpYtA6pWtZzAtm4t7z1MYImIbCxrVpmRfeMN9fjdu7K04ORJTcJ6bf/8I1UXTBPYYcOYwLoIJrFkd/HxwHffyfKByEj1bQEBskRpwQKAk+VERHYSEiKJbNmy6vH792XGwdlcuiSzy48fq8ebNgUGD9YmJkp1TGLJrp49Az74ABg50vy2SpVk9rVlS/vHRUTk9jJmlPWjxqfAGjcGfvhBu5hS4vp1SWDv31eP164ta9S4kctl8CdJdnP9urToXrvW/LbOnYG9e4ECBewfF7mpZcuANm3M1wESubMMGYCtW2VjV6lSwOLFssPWWdy+LQns7dvq8apVpaW0v782cZFNMIklu9i/H6hYETh7Vj3u6SntZGfMAHx8tImN3JCiAKNHSyWCEiWkg8+pU1pHReQYAgOBLVuAzZudq2PdixeSwF6/rh6vUAFYvx5Il06buMhmmMSSza1fD9SpY740KThY3ie//JKls8jOdu1Sb1ZZu9b8g4/Infn6yoYva9auBfbssV88SZEmDdCli3qsVClJxtkdxyUxiSWbWrBA1tGbbuAqXhw4fFgqnxDZ3fjx6usFC3K3MlFS3b0LtGsH1KoF9O1r/gavpd69gWnT5P/FisnSCHbec1lMYslmxo8H2rY1b0vdsCFw4ADXv5JGLl4E1q1Tj/Xuzc0eREmhKEDHjlK2SlHkjb5cOZmVcBRdu8qa923bpOoCuSy+a1OqUxSgf385QDfVpo00SWH5LNLMhAnq6xkyyNEWEb3a2rXAxo3qsQsXZNfu4MFAVJR94vjvv8Rvb9FCmjWQS2MSS6kqLk4O0n/6yfy2vn2BuXOlLT2RJh49AubPV4916QKkTatNPETOpkkTYOxYWTNrLC5OSnEVLAhMnmy7JQYvXgBjxsipvFmzbPM1yGkwiaVUExcnE1pz5pjfNmaMvO/xjC1pasYM9YertzfQvbt28RA5G09PmZE4fhwoX9789tu3gZ49pW/4uHFARETqfN2oKGDKFElev/1WZmK7d5e1aeS2mFJQqtAnsL//rh738JCk9ptvtImLyED/IWjsk094ypEoJYoXlwTy++8BLy/z20NDga+/BvLmBYYOlTWzphskkuL5c/kQKVwY6NFDnlcvJgb48EPg3r0Ufxvk3JjE0muzlsD6+gKrVgHt22sTF5HKkiXmHXx699YmFiJX4O0t62APHwZq1rR8n0ePJNGtXDlpZz0ePpSNE337Suew9OmBDh2Amzct3796dcDPL6XfATk5C4dPREmXWAK7Zg3QoIEmYRGp6XdRG6td27xPPBElX9myUnt5715gxAgpa2VJjRqWx/fvl79HX1/pTZ4U9evLGtwKFVIUMrkGzsRSijGBJaexfTtw5ox6zFL5DCJKuerVpYPNwYNAo0bmt7/zjuXHvXwJREcnLYGtXl2aLGzaxASWOBNLKRMfL8sELCWwa9fKQTKRwzCdhS1SBHj3XW1iIXJ1lStLLeYTJ4B582Rm1tfXes3WV5Xl8vICqlWT2o316rHFIxkwiaVkUxTZfLpwoXqcCSw5pHPnzOtasrkBke2VLZuwZCexKgWmSWyaNMCbb8qsa/XqkhSzDB5ZwCSWkm3oUGDqVPUYE1hyWH5+CacNoqOBjBmB1q21jorIvSSWhNaqBRw9KslsmjRAiRIsKE5JolMURdE6CHsJDw9HUFAQwsLCEMiWUSnyyy9Anz7qMW9v4M8/uQaWHNyDB8DMmfJhavpLTEREDiOp+RpnYinJ5s0z/+zX6WSCiwksObyQECkHRERELoGLwihJ1qyRUn2mZs4Emje3ezhERETk5pjE0ivt3Al8/LFUJDD2009Ap07axERERETujUksJersWaBZM9kPY+zbb4F+/TQJiYiIiIhJLFl39y7QsCEQHq4e79QJGDVKm5iIkuTECWDKFOm7TkRELolJLFn07Jk0XLl1Sz3+4YfA9OmsNU0O7uefgR49gJw5pTPXjRtaR0RERKmMSSyZiY0FWrQATp5Uj7/5pjQ48PTUJCyipLlzB1ixQv4fFibdupYt0zYmIiJKdUxiSUVRgC+/lLbUxgoWlFqw/v7axEWUZNOny5GYnr8/dyASEbkgJrGkMno0MHu2eixTJunamSmTNjERJVlkJDBjhnqsTRsgOFibeIiIyGaYxJLB8uXAwIHqMT8/mYEtWFCbmIiSZfFi4PFj9VjPntrEQkRENsUklgBI2+q2bdVjOh2waJGshSVyeIoCTJyoHqtXDyheXJt4iIjIppjEEu7eBZo2BV6+VI+PGyfVCIicwo4dwJkz6rFevbSJhYiIbI5JrJuLjJRmBnfvqse/+AL46istIiJKoV9+UV8vXBho0ECbWIiIyOaYxLoxRQE6dACOHFGP16oFTJ7MWrDkRC5dAtavV4/16gV48C2OiMhV8R3ejY0aBSxZoh7Lnx9YuRLw9tYmJqIUMV0LmyGD+SJvIiJyKUxi3dTq1cCgQeqxgADgr7+AjBm1iYkoRf77D5g3Tz3WuTOQNq0m4RARkX0wiXVDZ88CrVurx3Q6YOlSbuQmJzR7NvDiRcJ1T0+gWzft4iEiIrtgEutmnj4F3n8fiIhQj//8M9CwoSYhEaVcTIws4DbWvDmQK5c28RARkd0wiXUj8fEyA3vlinq8XTugTx9NQiJ6PaGhQI4c6rHevbWJhYiI7MpL6wDIfr7/Hli3Tj1WsaK0mmclAnJKuXIBBw/K5ZdfgHv3gEqVtI6KiIjsQKcoiqJ1EPYSHh6OoKAghIWFITAwUOtw7Oqvv4D33lOPZc4MHDvGM6/kQmJjAS8emxMRObOk5mtcTuAGLl0CWrVSj3l6AsuXM4ElF8MElojIbTCJdXHPnslGrvBw9fjYsdLUgIiIiMgZMYl1YfqOXOfOqcc/+4wt5YmIiMi5MYl1YZMnAytWqMdKl5aymtzIRU4rLAzYuFGO0oiIyG0xiXVRBw4AffuqxzJkAFatAtKk0SYmolQxc6YUNS5VCliwAIiO1joiIiLSAJNYF/ToEdCihWzUNrZoEZA/vzYxEaWKqChgwgT5/9mzQNu2QPfumoZERETaYBLrYuLigJYtgdu31eMDB7IjF7mA33+XWrDGOnXSJhYiItIUk1gXM3IksGWLeqx2bWD4cG3iIUo18fHSH9lYrVrSsYOIiNwOk1gXsnUrMGyYeixbNmDxYpbPJBewfj1w4YJ6rF8/bWIhIiLNMYl1EbdvS+ks4w3bnp7A0qVA1qzaxUWUasaMUV8vWRJo0ECbWIiISHNMYl1AbCzw6aeyocvYjz8CNWpoExNRqtq/H9i3Tz3Wrx9rxRERuTEmsS5g6FDzz/f33gO+/lqbeIhSnela2Fy5gE8+0SYWIiJyCExindyWLcCoUeqxvHmBefMAD/50yRVcuACsXase690b8PbWJh4iInIITHOc2L17QOvW6nWwXl6yDjZDBu3iIkpV48apf8nTpwc6dtQsHCIicgxMYp1UXBzQqhXw4IF6/KefgMqVtYmJKNXduydduYx9+SUQEKBNPERE5DCYxDqpkSOBHTvUY40by1lWIpcxdqy6rayvL9Cjh3bxEBGRw2AS64R27TJvXpAzp6yD5WZtchmhocD06eqxtm1ZM46IiAAwiXU6Dx9KW9n4+IQxT09gyRIgY0bt4iJKdTduqBNWLy+gf3/NwiEiIsfCJNaJKArQvj1w9656fMQIoFo1bWIispkqVYCLF4Fff5WSG+3aAfnyaR0VERE5CKdLYqOiolCmTBnodDqcPHlS63DsavJk6bxprF494NtvtYmHyOa8vYEOHYBLl8xrxRIRkVtzuiS2X79+yJ49u9Zh2N3Jk8A336jHsmSRjdusB0suz9tbSmsRERH9n1OlPxs3bsSWLVswduxYrUOxq4gIaU5kvEkbAObPl0SWiIiIyN14aR1AUt2/fx+dOnXCmjVrkCZNmiQ9JioqClFRUYbr4eHhtgrPpnr1kqWBxr7+GqhfX5t4iIiIiLTmFDOxiqKgXbt26NKlCypUqJDkx40aNQpBQUGGS65cuWwYpW0sWwb89pt6rHx5qRNL5HLmzwfOntU6CiIicgKaJrH9+/eHTqdL9HLhwgVMnjwZz549w4ABA5L1/AMGDEBYWJjhcuvWLRt9J7Zx/TrQubN6LF06Kafl46NNTEQ2c/cu8MUXQKlSwMcfA//8o3VERETkwHSKYtyU3L4ePnyIx48fJ3qf/Pnzo0WLFvjrr7+gM6rkHxcXB09PT7Rs2RLz589P0tcLDw9HUFAQwsLCEBgY+Fqx21psLFCjBnDggHp8wQKgdWttYiKyqV69gEmTEq77+gJ37rAAMhGRm0lqvqZpEptUN2/eVK1nvXv3LurXr4+VK1eicuXKyJkzZ5Kex5mS2KFDge+/V4+1agUsXKhNPEQ2deMGULQoYLSGHV98AcyYoVlIRESkjaTma06xsSt37tyq6+nSpQMAFChQIMkJrDPZtw/44Qf1WP78wNSp2sRDZHPffKNOYL29gYEDtYuHiIgcnlNs7HInT5/KjKtpW9nFiwEHnzwmSpndu4GVK9VjXbsCJgevRERExpxiJtZU3rx54QSrIJJNUYAvvwT+/Vc9Pnw4ULmyNjER2VRcHNC7t3osOFjW0xARESWCM7EOZNEiqTxgrEYNoH9/beIhsrl584ATJ9Rj338viSwREVEinGJjV2px5I1d164BZcoAz54ljKVPD5w6xbOq5KLCw4FChYAHDxLGSpSQHsteTnmSiIiIUkFS8zXOxDqAmBigZUt1AgsAs2YxgSUXNnKkOoEFgF9+YQJLRERJwiTWAfzwA3DwoHqsfXugeXNt4iGyuatXgQkT1GONGwN162oSDhEROR8msRrbv9+8nFbBguqa70Qu5+uvgejohOve3sC4cdrFQ0RETodJrIbCw83LaXl5Ab//Lu1liVzSjh3AmjXqsR49gMKFNQmHiIicE5NYDfXsCVy/rh4bNgyoVEmTcIjsY+5c9fVMmYDBg7WJhYiInBaTWI2sWAHMn68eq1aN5bTIDcydK2W0PD3l+g8/SCkOIiKiZGASq4Hbt6UtvLHAQGDhwoTPdSKX5eUlM6/798t6mo4dtY6IiIicEGvZ2Fl8PNC2LfDff+rxqVOBvHk1CYlIG5UqyZEbERFRCnAm1s7Gj5d9LcY++UTqxBIRERFR0jCJtaNTp4CBA9VjuXIB06YBOp02MRHZnPs0BSQiIjtiEmsnkZEy2xoTkzCm0wELFgAZMmgXF5FNXbwIVKgAHDumdSRERORimMTayYABwD//qMe++QaoVUuTcIhsLzpaNm4dPw5UqQKMGgXExWkdFRERuQgmsXawZQswcaJ6rEwZYMQITcIhsr24OKBNG+DoUbkeGytraUz/EIiIiFKISayNPX4MtGunHvPzk65cPj6ahERaio0Frl41n5Y3FhoK7NsHhIXZL67UFB8vZbOWLVOPFy0KdOmiTUxERORyWGLLhhQF6NwZuHdPPf7zz0Dx4trERHZ2/760WF2/Hjh/HrhxQxLZGjWA3bstP2brVpnF1OmAYsXkVHzlynIpUULqrDoqRZFWdPPmqcfTpAEWL5Z/iYiIUoEDfxo6v/nzgVWr1GMNGgDdumkTD9nJnTvA6tXAypXA3r0yM2nqyhXrj3/2TP5VFODcObnMmSNjgYFAkyZA8+ZA/foyre8oFEVazk2dqh739QX+/BMoW1abuIiIyCUxibWRa9eAHj3UYxkzSi7CclouSFGADRuAMWOAPXteff+7d4GICCBtWvPbnj+3/rjwcFmL8vvvQECAOqH19095/KlhxAj5/o15eQF//AG88442MRERkcvimlgbiI0FWrc2z0VmzwayZdMmJrKR+HiZbi9fHmjcOGkJLAAEBZmvM9Hz9AS8vV/9HM+eySn6998HsmaVXsYHD9q/LmtsLDB8ODB0qHrcw0Pia9TIvvEQEZFb0CmK+1QiDw8PR1BQEMLCwhAYGGizrzNyJPDdd+qxDh2AX3+12ZckLRw+LD/Ys2cTv1/atJLgNmwIFCkCFCwIBAcnPiX/8iVw8iRw6FDC5dq1V8eUJQtw61bSkuDUcPKkbOKyVAd23jzpsUxERJQMSc3XuJzABnx9JYfQNzYoUACYMEHTkMgWMmaUzVqWBAYC770HfPQRUK9e8k/1+/nJhq4qVRLG7t6VWd8VK2StraXjz9at7ZPAvnwJDBsGjB1rufbrtGlMYImIyKa4nMAGvv5azuoWLSpnhhctAtKl0zoqSnUFCgCffaYeK1xYZiAfPAAWLgSaNk29tarZswPdu0tVgzt3gClTgJo11TO6pvXcjPXvDyxdKmtxX5eHh1RcsJTAjh8PdO36+l+D6H/t3Xt0VOW9xvFnhoEwSBJCSCCQQEgrtyCXQkWEKrURikANihdUqhR6CuIpiqCwUBDtERUtSisaRVhKqfRI1FrlskoDLoJXridBuQikXARsCCREIObynj92ExhIEG323rOT72etWbovM/v3vk6nDy/vfjcAXADTCWx08qS0dq31t8ioo7Zvt9ZL69zZmkNy883Wn1yctG+f9fzinJzz12at9M9/SsnJ1r9HRFhzePv2PTPam5j43a/70UfSlVeeGRHu3Nma+N2v3/dqBgAA0sXnNUIscCEbN0oZGdKLL1qjj9X5+GPpxz+u+Xg4mDNHeuCBmo+3aSO1bWsF8AYNrFUFGjSwbj577DFrRLk6v/2t1TfTp1sjvRER9tQPAKg3mBML/CdKS6Xf/c66S6+8XLr8cusGpur06eNsbd9HTSO0lQ4etF7VWbmy5hD7P/9jPYWLp3cAABwWxkNHgEu2bbP+iv3RR8/M+XzwQSk/3926vi9jrEWLf/7z7zfVYdWqmpftiowkwAIAXEGIBSqVl1t32/fqJW3aFHqsoMC7S0z4fNZKAStWWEF8xQprTddBg6z1ar/N3r3S7t321wkAwHfAdAJAstZgvfNOKTv7/GOBgHXT1rRpztdV25o1s0Zkf/5za7uiQtqxQ9q82Vq1oKzMCvPl5da/N2gg9ehhzZcFACCMEGJRvxkjvfqq9dft1T3utUsX687/Xr2cr80Jfr+1qkDnzm5XAgDAd8J0AtRfBQXWklijR58fYH0+a8HfjRvrboAFAMDDGIlF/fSPf1jTB6q7Iz8lxRqd7d/f+boAAMBFYSQW9UtJiTXCmpZWfYAdM0baupUACwBAmGMkFvXPqlXn72ve3Hra1A03OF8PAAD4zhiJRf0SESEtXiw1bHhm37XXWo9sJcACAOAZhFjUPz16SLNmWYF27lzriVStW7tdFQAA+A6YToC6qbxc+vJLKSmp+uNTplgjrx07OlsXAACoFYzEou7ZskXq29eaJlBSUv05gQABFgAADyPEou74+mtr5YHevaVPP7WeRPXEE25XBQAAbECIRd3w3nvW07WeecaaSlDp8cetMAsAAOoUQiy8LSdHGjJEGjpU2rfv/OPt20snTjhfFwAAsBUhFt60f7/1uNju3aXly88/3qiRtQLB1q3W9AIAAFCnsDoBvOX4cWue63PPSadPV3/OT38qvfACN24BAFCHEWLhDUeOSPPmSfPnW0G2Oi1bWgH3zjsln8/R8gAAgLMIsfCGyZOlP/2p+mOXXCI98IA0aZLUtKmzdQEAAFcwJxbeMGnS+fsCAenuu6Xdu6UZMwiwAADUI4RYhI+DB6WjR6s/1rOnlJZ2ZnvECGnbNun5561pBAAAoF4hxMJdp05Jy5ZZy2S1bWvdkFWTqVOlMWOkzz+X3nhD6tDBuToBAEBY8RljjNtFOKWoqEjR0dEqLCxUVFSU2+XUX8XF0ooVVnh97z3rSVuVUlKkXbskP3++AgCgPrrYvMaNXXBGfr60cqX05ptWgK1peaw9e6T337eWyQIAAKgBIRb2MMZ6mta771qjrR99JFVUfPv7mja1giwhFgAAXAAhFrXv//7PmuN64MDFne/3S1ddZa3vetNN1pJZAAAAF0CIRe37wQ+kf/3rwuc0aCBdc421ykB6uhQf70hpAACgbiDE4rs7fNhaHeDKK6Vevc4/fsklVkBdsSJ0f5Mm0s9+Jg0fLv3iF1JsrDP1AgCAOocQi4tTUCBlZkpLl0pr11rzW//rv6SMjOrPHzrUCrHJydbUgqFDpQEDpMaNHSwaAADUVSyxhQvbtk2aO9d65GtJSeix5s2tUdmGDc9/39Gj1rEuXSSfz5laAQCA511sXmMxTpzPGOnvf5cGD5a6dpVeeeX8ACtZo7OrV1f/GbGxUmoqARYAANiC6QQ4wxjp9delJ56wlse6kKZNrRuyWrVypDQAAICzEWJh2bZNGj9eWreu5nMaNbJuyLr1Vum666Rg0Ln6AAAAzkKIre9On5ZmzpR+/3uprKz6c2Jjpbvvtl6MvAIAgDBAiK3vAgFp1arqA2zHjtKkSdKoUYy6AgCAsMKNXfVdICC98ELovuRk6c03pc8+s5bRIsACAIAwQ4iF1LevNHastVTW9OnW/Njhw63HwQIAAIQh1omF5ehR61GxnTq5XQkAAKjHWCcWod57T5oyxVpGqzqxsQRYAADgGdzYVdcZI82bZ92gVVEhJSZKEye6XRUAAMB/xFMjse+995769OmjYDComJgYpaenu11S+HvoIenee60AK0n33Se9+66rJQEAAPynPBNiMzMzNWrUKI0ePVpbt27V+vXrddttt7ldVnh78UXp8cdD9xlDiAUAAJ7niRu7ysrKlJycrFmzZmnMmDHf+3Pq1Y1dy5dLw4adGYGt9Oij1uisz+dOXQAAABdQp27s2rRpkw4ePCi/36+ePXsqISFBgwcPVm5u7gXfV1JSoqKiopBXvbB5s3TzzecH2Jdekh5+mAALAAA8zxMhds+ePZKkRx55RA899JDeffddxcTEaMCAASooKKjxfbNnz1Z0dHTVKykpyamS3bNvnzRkiPT116H7H3pI+vWv3akJAACglrkaYqdOnSqfz3fB1/bt21Xx7xHF6dOn68Ybb1SvXr20aNEi+Xw+vfHGGzV+/rRp01RYWFj12r9/v1NNc0dhoRVgDx0K3X/77dY0AgAAgDrC1SW27r//ft11110XPCclJUWH/h3KunTpUrU/IiJCKSkp2rdvX43vjYiIUERERK3UGvZKS6URI6Rzp1hcfbX0yitMIQAAAHWKqyE2Li5OcXFx33per169FBERoR07dqh///6SpNLSUuXl5aldu3Z2l+kNM2ZIq1eH7uvUSXrrLam+BHkAAFBveOJhB1FRURo3bpxmzpyppKQktWvXTnPmzJEk3XTTTS5XFwY+/VR66qnQffHx1goFMTHu1AQAAGAjT4RYSZozZ44CgYBGjRqlU6dOqU+fPsrKylJMfQ9pJSXS6NGhKxE0bCj97W9S+/bu1QUAAGAjz4TYhg0b6umnn9bTTz/tdinhpbzcmve6bduZfTNmSJdf7l5NAAAANvPEElu4gCZNpOefl7KypORkqWdP6cEH3a4KAADAVp4ZicW3+OlPpZwcKT/fmk4AAABQhxFi65KmTa0XAABAHcd0AgAAAHgOIdZrvvnGerQsAABAPUaI9ZqXX5ZSU6UXX5SMcbsaAAAAVxBiveTrr6XHHpOKi6Xx46W0NCkvz+2qAAAAHEeI9ZLnnpOOHDmznZXF1AIAAFAvEWK9oqDg/EfLDh4sXXWVO/UAAAC4iBDrFU8+KRUWhu57/HF3agEAAHAZIdYLDh6U5s0L3XfrrVKPHq6UAwAA4DZCrBc89ph0+vSZ7UDA2gcAAFBPEWLD3a5d0oIFofvGjpV++EN36gEAAAgDhNhw9/DDUnn5me1g0NoHAABQjxFiw9nmzdJf/hK677e/lVq3dqceAACAMEGIDWfTp4duN2smPfigK6UAAACEE0JsuNq6VVqxInTfAw9IMTHu1AMAABBGCLHh6tlnQ7fj4qypBAAAACDEhqWSEmn16tB9EyZIl1ziTj0AAABhhhAbjiIiziytlZpqbY8f73ZVAAAAYYMQG64aN5bGjJFycqRNm6T4eLcrAgAACBuE2HDn80ldurhdBQAAQFghxAIAAMBzCLEAAADwHEJsOMnPd7sCAAAATyDEhovPPrMeJztypPTJJ25XAwAAENYIseHi2Wel0lJp6VKpTx8pPd3tigAAAMIWITYc5OdLixeH7uva1Z1aAAAAPIAQGw4yMqTTp89sN2xoPaELAAAA1SLEuq2iQnr55dB9t94qJSS4Uw8AAIAHEGLdlpUl/fOfofv++7/dqQUAAMAjCLFuW7gwdLtbN6l3b3dqAQAA8AhCrJuOHZPefDN035gx1qNmAQAAUCNCrJuWLJFKSs5sN2ok3X67e/UAAAB4BCHWTedOJUhPl2JjXSkFAADASwixbtm82Xqd7Ve/cqcWAAAAjyHEuuXcUdikJCktzZ1aAAAAPIYQ64bTp635sGe76y6pQQNXygEAAPAaQqwb3n7bWpngbKNHu1IKAACAFxFi3dCtmzR+vBQdbW1fc43Uvr27NQEAAHiIzxhj3C7CKUVFRYqOjlZhYaGioqLcLkc6dcpaJ7ZVK+lnP3O7GgAAANddbF4LOFgTzhUMsi4sAADA98B0AgAAAHgOIRYAAACeQ4gFAACA5xBinVJSIr36qlRY6HYlAAAAnkeIdcqKFdYDDVq2lG64QcrMdLsiAAAAz2J1Aqe8/rr1z5IS6a23pIIC6cYb3a0JAADAoxiJdUJxsfS3v4Xuu/VWd2oBAACoAwixTvjrX60HG1QKBKQRI9yrBwAAwOMIsU6onEpQ6dprpRYt3KkFAACgDiDE2q2gQFq1KnTfyJHu1AIAAFBHEGLtlpkplZWd2W7cWLr+evfqAQAAqAMIsXY7dyrBkCFSVJQ7tQAAANQRhFg7ffmltHZt6D6mEgAAAPzHCLF2+t//lYw5sx0ZKV13nXv1AAAA1BGEWDudO5Vg+HApGHSnFgAAgDqEEGuXPXukTz4J3ccDDgAAAGoFIdYuS5eGbsfGSmlp7tQCAABQxxBi7XLuVIKbbpIaNnSnFgAAgDqGEGsHY6QFC6SJE6VWrax9rEoAAABQa3zGnH37fN1WVFSk6OhoFRYWKsqptVrLy6V166SrrpL8/JkBAADgQi42rwUcrKl+atBAGjDA7SoAAADqFIYGAQAA4DmEWAAAAHgOIRYAAACe45kQu3PnTl1//fVq0aKFoqKi1L9/f61Zs8btsgAAAOACz4TYoUOHqqysTFlZWdq4caO6d++uoUOH6vDhw26XBgAAAId5IsTm5+dr165dmjp1qrp166ZLL71UTzzxhE6ePKnc3Fy3ywMAAIDDPBFiY2Nj1bFjR7322mv6+uuvVVZWpoyMDMXHx6tXr141vq+kpERFRUUhLwAAAHifJ9aJ9fl8Wr16tdLT0xUZGSm/36/4+HitXLlSMTExNb5v9uzZmjVrloOVAgAAwAmujsROnTpVPp/vgq/t27fLGKMJEyYoPj5e69at0yeffKL09HQNGzZMhw4dqvHzp02bpsLCwqrX/v37HWwdAAAA7OLqY2f/9a9/6ejRoxc8JyUlRevWrdPAgQN17NixkMePXXrppRozZoymTp16Uddz5bGzAAAAuGieeOxsXFyc4uLivvW8kydPSpL8/tCBY7/fr4qKCltqAwAAQPjyxI1dffv2VUxMjO68805t3bpVO3fu1JQpU7R3714NGTLE7fIAAADgME+E2BYtWmjlypUqLi7WNddco969eys7O1t//etf1b17d7fLAwAAgMNcnRPrNObEAgAAhLeLzWueGIkFAAAAzkaIBQAAgOcQYgEAAOA5hFgAAAB4DiEWAAAAnkOIBQAAgOcQYgEAAOA5hFgAAAB4DiEWAAAAnkOIBQAAgOcQYgEAAOA5AbcLcJIxRpL1TF4AAACEn8qcVpnbalKvQuyJEyckSUlJSS5XAgAAgAs5ceKEoqOjazzuM98Wc+uQiooKffnll4qMjJTP57P9ekVFRUpKStL+/fsVFRVl+/Vgod/dQb+7g353B/3uDvrdHU73uzFGJ06cUOvWreX31zzztV6NxPr9fiUmJjp+3aioKP7H5gL63R30uzvod3fQ7+6g393hZL9faAS2Ejd2AQAAwHMIsQAAAPAcQqyNIiIiNHPmTEVERLhdSr1Cv7uDfncH/e4O+t0d9Ls7wrXf69WNXQAAAKgbGIkFAACA5xBiAQAA4DmEWAAAAHgOIRYAAACeQ4i9gNmzZ+vHP/6xIiMjFR8fr/T0dO3YsSPknNOnT2vChAmKjY1V06ZNdeONN+rIkSMh5+zbt09DhgxRkyZNFB8frylTpqisrKzqeHZ2tvr166fY2FgFg0F16tRJc+fOdaSN4cipfj/b+vXrFQgE1KNHD7uaFfac6ve1a9fK5/Od9zp8+LAj7Qw3Tn7fS0pKNH36dLVr104RERFKTk7WwoULbW9jOHKq3++6665qv++pqamOtDPcOPl9X7Jkibp3764mTZooISFBv/rVr3T06FHb2xiOnOz3559/Xp07d1YwGFTHjh312muv2dcwgxoNGjTILFq0yOTm5potW7aY6667zrRt29YUFxdXnTNu3DiTlJRk/vGPf5gNGzaYK664wlx55ZVVx8vKykzXrl1NWlqa2bx5s1m+fLlp0aKFmTZtWtU5mzZtMn/+859Nbm6u2bt3r1m8eLFp0qSJycjIcLS94cKpfq907Ngxk5KSYgYOHGi6d+/uRBPDklP9vmbNGiPJ7Nixwxw6dKjqVV5e7mh7w4WT3/df/OIXpk+fPubvf/+72bt3r/nggw9Mdna2Y20NJ071+/Hjx0O+5/v37zfNmzc3M2fOdLK5YcOpfs/OzjZ+v98899xzZs+ePWbdunUmNTXVDB8+3NH2hgun+n3+/PkmMjLSLF261Ozevdu8/vrrpmnTpuadd96xpV2E2O/gq6++MpLM+++/b4yxfpwaNmxo3njjjapzPv/8cyPJfPjhh8YYY5YvX278fr85fPhw1TkvvPCCiYqKMiUlJTVea/jw4eaOO+6wqSXeYne/33LLLeahhx4yM2fOrNch9lx29XtliD127JhzjfEQu/p9xYoVJjo62hw9etTB1niHU7/vb731lvH5fCYvL8/G1niHXf0+Z84ck5KSEnKtefPmmTZt2tjdJE+wq9/79u1rJk+eHHKtSZMmmX79+tnSDqYTfAeFhYWSpObNm0uSNm7cqNLSUqWlpVWd06lTJ7Vt21YffvihJOnDDz/UZZddppYtW1adM2jQIBUVFWnbtm3VXmfz5s364IMPdPXVV9vVFE+xs98XLVqkPXv2aObMmU40xVPs/r736NFDCQkJuvbaa7V+/Xq7m+MZdvX7O++8o969e+upp55SmzZt1KFDB02ePFmnTp1yqmlhzanf91deeUVpaWlq166dXU3xFLv6vW/fvtq/f7+WL18uY4yOHDmiZcuW6brrrnOqaWHNrn4vKSlR48aNQ64VDAb1ySefqLS0tNbbQYi9SBUVFbr33nvVr18/de3aVZJ0+PBhNWrUSM2aNQs5t2XLllXz+w4fPhzyH7zyeOWxsyUmJioiIkK9e/fWhAkTNHbsWJta4x129vuuXbs0depU/elPf1IgELC5Jd5iZ78nJCToxRdfVGZmpjIzM5WUlKQBAwZo06ZNNrcq/NnZ73v27FF2drZyc3P11ltv6dlnn9WyZct0991329yq8OfE77skffnll1qxYgW/7f9mZ7/369dPS5Ys0S233KJGjRqpVatWio6O1vPPP29zq8Kfnf0+aNAgLViwQBs3bpQxRhs2bNCCBQtUWlqq/Pz8Wm8L/899kSZMmKDc3FxlZ2fbdo1169apuLhYH330kaZOnaof/vCHGjlypG3X8wK7+r28vFy33XabZs2apQ4dOtTqZ9cFdn7fO3bsqI4dO1ZtX3nlldq9e7fmzp2rxYsX1/r1vMTOfq+oqJDP59OSJUsUHR0tSfr973+vESNGaP78+QoGg7V+Ta9w4vddkl599VU1a9ZM6enptl7HK+zs988++0wTJ07UjBkzNGjQIB06dEhTpkzRuHHj9Morr9T69bzEzn5/+OGHdfjwYV1xxRUyxqhly5a688479dRTT8nvr/1xU0ZiL8I999yjd999V2vWrFFiYmLV/latWumbb77R8ePHQ84/cuSIWrVqVXXOuXf3VW5XnlOpffv2uuyyy/TrX/9a9913nx555JHab4yH2NnvJ06c0IYNG3TPPfcoEAgoEAjo0Ucf1datWxUIBJSVlWVv48KYU9/3s11++eX64osvaqkF3mR3vyckJKhNmzZVAVaSOnfuLGOMDhw4YEeTPMGp77sxRgsXLtSoUaPUqFEjG1riLXb3++zZs9WvXz9NmTJF3bp106BBgzR//nwtXLhQhw4dsrFl4c3ufg8Gg1q4cKFOnjypvLw87du3T8nJyYqMjFRcXFztN8iWmbZ1REVFhZkwYYJp3bq12blz53nHKydCL1u2rGrf9u3bq50IfeTIkapzMjIyTFRUlDl9+nSN1541a5Zp165d7TXGQ5zo9/LycpOTkxPyGj9+vOnYsaPJyckJuWOzvnDz+56WllZv7xp2qt8zMjJMMBg0J06cqDrn7bffNn6/35w8edKu5oUtp7/vlTc05uTk2NQib3Cq32+44QZz8803h3z2Bx98YCSZgwcP2tG0sObm7/tVV11lRo4cWYutOYMQewHjx4830dHRZu3atSFLpJz9gz9u3DjTtm1bk5WVZTZs2GD69u1r+vbtW3W8ckmKgQMHmi1btpiVK1eauLi4kCUp/vjHP5p33nnH7Ny50+zcudMsWLDAREZGmunTpzva3nDhVL+fq76vTuBUv8+dO9e8/fbbZteuXSYnJ8dMnDjR+P1+s3r1akfbGy6c6vcTJ06YxMREM2LECLNt2zbz/vvvm0svvdSMHTvW0faGC6d/Z+644w7Tp08fR9oWzpzq90WLFplAIGDmz59vdu/ebbKzs03v3r3N5Zdf7mh7w4VT/b5jxw6zePFis3PnTvPxxx+bW265xTRv3tzs3bvXlnYRYi9AUrWvRYsWVZ1z6tQpc/fdd5uYmBjTpEkTM3z4cHPo0KGQz8nLyzODBw82wWDQtGjRwtx///2mtLS06vi8efNMamqqadKkiYmKijI9e/Y08+fPr7frZjrV7+eq7yHWqX5/8sknzQ9+8APTuHFj07x5czNgwACTlZXlVDPDjpPf988//9ykpaWZYDBoEhMTzaRJk+rlKKwxzvb78ePHTTAYNC+99JITTQtrTvb7vHnzTJcuXUwwGDQJCQnm9ttvNwcOHHCimWHHqX7/7LPPTI8ePUwwGDRRUVHm+uuvN9u3b7etXb5/Nw4AAADwDG7sAgAAgOcQYgEAAOA5hFgAAAB4DiEWAAAAnkOIBQAAgOcQYgEAAOA5hFgAAAB4DiEWAAAAnkOIBQAAgOcQYgEgDBhjlJaWpkGDBp13bP78+WrWrJkOHDjgQmUAEJ4IsQAQBnw+nxYtWqSPP/5YGRkZVfv37t2rBx54QH/4wx+UmJhYq9csLS2t1c8DACcRYgEgTCQlJem5557T5MmTtXfvXhljNGbMGA0cOFA9e/bU4MGD1bRpU7Vs2VKjRo1Sfn5+1XtXrlyp/v37q1mzZoqNjdXQoUO1e/fuquN5eXny+Xz6y1/+oquvvlqNGzfWkiVL3GgmANQKnzHGuF0EAOCM9PR0FRYW6oYbbtBjjz2mbdu2KTU1VWPHjtUvf/lLnTp1Sg8++KDKysqUlZUlScrMzJTP51O3bt1UXFysGTNmKC8vT1u2bJHf71deXp7at2+v5ORkPfPMM+rZs6caN26shIQEl1sLAN8PIRYAwsxXX32l1NRUFRQUKDMzU7m5uVq3bp1WrVpVdc6BAweUlJSkHTt2qEOHDud9Rn5+vuLi4pSTk6OuXbtWhdhnn31WEydOdLI5AGALphMAQJiJj4/Xb37zG3Xu3Fnp6enaunWr1qxZo6ZNm1a9OnXqJElVUwZ27dqlkSNHKiUlRVFRUUpOTpYk7du3L+Sze/fu7WhbAMAuAbcLAACcLxAIKBCwfqKLi4s1bNgwPfnkk+edVzkdYNiwYWrXrp1efvlltW7dWhUVFeratau++eabkPMvueQS+4sHAAcQYgEgzP3oRz9SZmamkpOTq4Lt2Y4ePaodO3bo5Zdf1k9+8hNJUnZ2ttNlAoCjmE4AAGFuwoQJKigo0MiRI/Xpp59q9+7dWrVqlUaPHq3y8nLFxMQoNjZWL730kr744gtlZWVp0qRJbpcNALYixAJAmGvdurXWr1+v8vJyDRw4UJdddpnuvfdeNWvWTH6/X36/X0uXLtXGjRvVtWtX3XfffZozZ47bZQOArVidAAAAAJ7DSCwAAAA8hxALAAAAzyHEAgAAwHMIsQAAAPAcQiwAAAA8hxALAAAAzyHEAgAAwHMIsQAAAPAcQiwAAAA8hxALAAAAzyHEAgAAwHP+H7x7PA6acDRWAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "d01c2c26",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.384846Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.384459Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.418050Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.415940Z"
+        },
+        "id": "d01c2c26",
+        "outputId": "02854850-5131-47d8-a283-93750688686b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 288
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               coef  std err      t  P>|t|\n",
+              "intercept   60.4937    9.460  6.394  0.000\n",
+              "bs(age)[0]   3.9805   12.538  0.317  0.751\n",
+              "bs(age)[1]  44.6310    9.626  4.636  0.000\n",
+              "bs(age)[2]  62.8388   10.755  5.843  0.000\n",
+              "bs(age)[3]  55.9908   10.706  5.230  0.000\n",
+              "bs(age)[4]  50.6881   14.402  3.520  0.000\n",
+              "bs(age)[5]  16.6061   19.126  0.868  0.385"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3bcd0ea9-c493-48c3-977e-58075f3491b6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>coef</th>\n",
+              "      <th>std err</th>\n",
+              "      <th>t</th>\n",
+              "      <th>P&gt;|t|</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>60.4937</td>\n",
+              "      <td>9.460</td>\n",
+              "      <td>6.394</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age)[0]</th>\n",
+              "      <td>3.9805</td>\n",
+              "      <td>12.538</td>\n",
+              "      <td>0.317</td>\n",
+              "      <td>0.751</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age)[1]</th>\n",
+              "      <td>44.6310</td>\n",
+              "      <td>9.626</td>\n",
+              "      <td>4.636</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age)[2]</th>\n",
+              "      <td>62.8388</td>\n",
+              "      <td>10.755</td>\n",
+              "      <td>5.843</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age)[3]</th>\n",
+              "      <td>55.9908</td>\n",
+              "      <td>10.706</td>\n",
+              "      <td>5.230</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age)[4]</th>\n",
+              "      <td>50.6881</td>\n",
+              "      <td>14.402</td>\n",
+              "      <td>3.520</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bs(age)[5]</th>\n",
+              "      <td>16.6061</td>\n",
+              "      <td>19.126</td>\n",
+              "      <td>0.868</td>\n",
+              "      <td>0.385</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3bcd0ea9-c493-48c3-977e-58075f3491b6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
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+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
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+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"summarize(M)\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"coef\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.893001159576716,\n        \"min\": 3.9805,\n        \"max\": 62.8388,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          60.4937,\n          3.9805,\n          50.6881\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std err\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.4442092810148126,\n        \"min\": 9.46,\n        \"max\": 19.126,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          9.46,\n          12.538,\n          14.402\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"t\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3960597317461096,\n        \"min\": 0.317,\n        \"max\": 6.394,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6.394,\n          0.317,\n          3.52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P>|t|\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2966112575328828,\n        \"min\": 0.0,\n        \"max\": 0.751,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          0.751,\n          0.385\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "bs_age = MS([bs('age',\n",
+        "                internal_knots=[25,40,60],\n",
+        "                name='bs(age)')])\n",
+        "Xbs = bs_age.fit_transform(Wage)\n",
+        "M = sm.OLS(y, Xbs).fit()\n",
+        "summarize(M)\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "year_grid = np.linspace(2003, 2009, 100)\n",
-    "year_grid = np.linspace(Wage['year'].min(),\n",
-    "                        Wage['year'].max(),\n",
-    "                        100)\n",
-    "X_year_bh = X_bh.copy()[:100]\n",
-    "X_year_bh[:] = X_bh[:].mean(0)[None,:]\n",
-    "X_year_bh[:,4:9] = ns_year.transform(year_grid)\n",
-    "preds = gam_bh.get_prediction(X_year_bh)\n",
-    "bounds_year = preds.conf_int(alpha=0.05)\n",
-    "partial_year = preds.predicted_mean\n",
-    "center = partial_year.mean()\n",
-    "partial_year -= center\n",
-    "bounds_year -= center\n",
-    "fig, ax = subplots(figsize=(8,8))\n",
-    "ax.plot(year_grid, partial_year, 'b', linewidth=3)\n",
-    "ax.plot(year_grid, bounds_year[:,0], 'r--', linewidth=3)\n",
-    "ax.plot(year_grid, bounds_year[:,1], 'r--', linewidth=3)\n",
-    "ax.set_xlabel('Year')\n",
-    "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of year on wage', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1cd84b43",
-   "metadata": {},
-   "source": [
-    "We now fit the model (\\ref{Ch7:nsmod})  using smoothing splines rather\n",
-    "than natural splines.  All of the\n",
-    "terms in  (\\ref{Ch7:nsmod})  are fit simultaneously, taking each other\n",
-    "into account to explain the response. The `pygam` package only works with matrices, so we must convert\n",
-    "the categorical series `education` to its array representation, which can be found\n",
-    "with the `cat.codes` attribute of `education`. As `year` only has 7 unique values, we\n",
-    "use only seven basis functions for it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "88ff3def",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:37.759390Z",
-     "iopub.status.busy": "2024-06-04T23:19:37.759130Z",
-     "iopub.status.idle": "2024-06-04T23:19:37.788899Z",
-     "shell.execute_reply": "2024-06-04T23:19:37.788370Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "gam_full = LinearGAM(s_gam(0) +\n",
-    "                     s_gam(1, n_splines=7) +\n",
-    "                     f_gam(2, lam=0))\n",
-    "Xgam = np.column_stack([age,\n",
-    "                        Wage['year'],\n",
-    "                        Wage['education'].cat.codes])\n",
-    "gam_full = gam_full.fit(Xgam, y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eedda235",
-   "metadata": {},
-   "source": [
-    "The two `s_gam()` terms result in smoothing spline fits, and use a default value for $\\lambda$  (`lam=0.6`), which is somewhat arbitrary. For the categorical term `education`, specified using a `f_gam()` term,  we specify `lam=0` to avoid any shrinkage.\n",
-    "We produce the partial dependence plot in `age` to see the effect of these choices.\n",
-    "\n",
-    "The values for the plot\n",
-    "are generated by the `pygam` package. We provide a `plot_gam()`\n",
-    "function for partial-dependence plots in `ISLP.pygam`, which makes this job easier than in our last example with natural splines."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "29dbc933",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:37.792129Z",
-     "iopub.status.busy": "2024-06-04T23:19:37.791875Z",
-     "iopub.status.idle": "2024-06-04T23:19:37.909465Z",
-     "shell.execute_reply": "2024-06-04T23:19:37.908764Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "4b7fab9b",
+      "metadata": {
+        "id": "4b7fab9b"
+      },
+      "source": [
+        "Notice that there are 6 spline coefficients rather than 7. This is because, by default,\n",
+        "`bs()` assumes `intercept=False`, since we typically have an overall intercept in the model.\n",
+        "So it generates the spline basis with the given knots,  and then discards one of the basis functions to account for the intercept.\n",
+        "\n",
+        "We could also use the `df` (degrees of freedom) option to\n",
+        "specify the complexity of the spline.  We see above that with 3 knots,\n",
+        "the spline basis has 6 columns or degrees of freedom.  When we specify\n",
+        "`df=6` rather than the actual knots, `bs()` will produce a\n",
+        "spline with 3 knots chosen at uniform quantiles of the training data.\n",
+        "We can see these chosen knots most easily using `Bspline()` directly:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = subplots(figsize=(8,8))\n",
-    "plot_gam(gam_full, 0, ax=ax)\n",
-    "ax.set_xlabel('Age')\n",
-    "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of age on wage - default lam=0.6', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bcaa9a68",
-   "metadata": {},
-   "source": [
-    "We see that the function is somewhat wiggly. It is more natural to specify the `df` than a value for `lam`. \n",
-    "We refit a GAM using four degrees of freedom each for\n",
-    "`age` and  `year`. Recall that the addition of one below takes into account the intercept\n",
-    "of the smoothing spline."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "cdb535b5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:37.912957Z",
-     "iopub.status.busy": "2024-06-04T23:19:37.912683Z",
-     "iopub.status.idle": "2024-06-04T23:19:37.946458Z",
-     "shell.execute_reply": "2024-06-04T23:19:37.945953Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "age_term = gam_full.terms[0]\n",
-    "age_term.lam = approx_lam(Xgam, age_term, df=4+1)\n",
-    "year_term = gam_full.terms[1]\n",
-    "year_term.lam = approx_lam(Xgam, year_term, df=4+1)\n",
-    "gam_full = gam_full.fit(Xgam, y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6a22de8",
-   "metadata": {},
-   "source": [
-    "Note that updating `age_term.lam` above updates it in `gam_full.terms[0]` as well! Likewise for `year_term.lam`.\n",
-    "\n",
-    "Repeating the plot for `age`, we see that it is much smoother.\n",
-    "We also produce the plot for `year`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "2acadc27",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:37.950913Z",
-     "iopub.status.busy": "2024-06-04T23:19:37.950529Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.082439Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.081725Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Partial dependence of year on wage')"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = subplots(figsize=(8,8))\n",
-    "plot_gam(gam_full,\n",
-    "         1,\n",
-    "         ax=ax)\n",
-    "ax.set_xlabel('Year')\n",
-    "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of year on wage', fontsize=20)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "16258b84",
-   "metadata": {},
-   "source": [
-    "Finally we plot `education`, which is categorical. The partial dependence plot is different, and more suitable for the set of fitted constants for each level of this variable. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "a2b8e481",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.085348Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.085138Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.186025Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.185091Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "672d44ae",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.421424Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.421207Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.429792Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.429086Z"
+        },
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+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([33.75, 42.  , 51.  ])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ],
+      "source": [
+        "BSpline(df=6).fit(age).internal_knots_\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax = plot_gam(gam_full, 2)\n",
-    "ax.set_xlabel('Education')\n",
-    "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of wage on education',\n",
-    "             fontsize=20);\n",
-    "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a21bb76",
-   "metadata": {},
-   "source": [
-    "### ANOVA Tests for Additive Models\n",
-    "In all of our models, the function of `year` looks rather linear. We can\n",
-    "perform a series of ANOVA tests in order to determine which of these\n",
-    "three models is best: a GAM that excludes `year` ($\\mathcal{M}_1$),\n",
-    "a GAM that uses a linear function of `year` ($\\mathcal{M}_2$), or a\n",
-    "GAM that uses a spline function of `year` ($\\mathcal{M}_3$)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "1a76b516",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.189290Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.188978Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.226707Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.226124Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LinearGAM(callbacks=[Deviance(), Diffs()], fit_intercept=True, \n",
-       "   max_iter=100, scale=None, terms=s(0) + l(1) + f(2) + intercept, \n",
-       "   tol=0.0001, verbose=False)"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gam_0 = LinearGAM(age_term + f_gam(2, lam=0))\n",
-    "gam_0.fit(Xgam, y)\n",
-    "gam_linear = LinearGAM(age_term +\n",
-    "                       l_gam(1, lam=0) +\n",
-    "                       f_gam(2, lam=0))\n",
-    "gam_linear.fit(Xgam, y)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0de32e6",
-   "metadata": {},
-   "source": [
-    "Notice our use of `age_term` in the expressions above. We do this because\n",
-    "earlier we set the value for `lam` in this term to achieve four degrees of freedom.\n",
-    "\n",
-    "To directly assess the effect of `year` we run an ANOVA on the\n",
-    "three models fit above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "7a68bf04",
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-     "shell.execute_reply": "2024-06-04T23:19:38.236171Z"
-    },
-    "lines_to_next_cell": 0
-   },
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+    },
+    {
+      "cell_type": "markdown",
+      "id": "57fe2029",
+      "metadata": {
+        "id": "57fe2029"
+      },
+      "source": [
+        " When asking for six degrees of freedom,\n",
+        "the transform chooses knots at ages 33.75, 42.0, and 51.0,\n",
+        "which correspond to the 25th, 50th, and 75th percentiles of\n",
+        "`age`.\n",
+        "\n",
+        "When using B-splines we need not limit ourselves to cubic polynomials\n",
+        "(i.e. `degree=3`). For instance, using `degree=0` results\n",
+        "in piecewise constant functions, as in our example with\n",
+        "`pd.qcut()` above.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "4a3f93ee",
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+          "data": {
+            "text/plain": [
+              "                               coef  std err       t  P>|t|\n",
+              "intercept                   94.1584    1.478  63.687    0.0\n",
+              "bs(age, df=3, degree=0)[0]  22.3490    2.152  10.388    0.0\n",
+              "bs(age, df=3, degree=0)[1]  24.8076    2.044  12.137    0.0\n",
+              "bs(age, df=3, degree=0)[2]  22.7814    2.087  10.917    0.0"
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+              "summary": "{\n  \"name\": \"summarize(sm\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"coef\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35.43907528910238,\n        \"min\": 22.349,\n        \"max\": 94.1584,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          22.349,\n          22.7814,\n          94.1584\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std err\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31134801000381135,\n        \"min\": 1.478,\n        \"max\": 2.152,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.152,\n          2.087,\n          1.478\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"t\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.280040022483476,\n        \"min\": 10.388,\n        \"max\": 63.687,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10.388,\n          10.917,\n          63.687\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P>|t|\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
       ],
-      "text/plain": [
-       "       deviance           df  deviance_diff   df_diff          F    pvalue\n",
-       "0  3.714362e+06  2991.004005            NaN       NaN        NaN       NaN\n",
-       "1  3.696746e+06  2990.005190   17616.542840  0.998815  14.265131  0.002314\n",
-       "2  3.693143e+06  2987.007254    3602.893655  2.997936   0.972007  0.435579"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "anova_gam(gam_0, gam_linear, gam_full)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed03248f",
-   "metadata": {},
-   "source": [
-    " We find that there is compelling evidence that a GAM with a linear\n",
-    "function in `year` is better than a GAM that does not include\n",
-    "`year` at all ($p$-value= 0.002). However, there is no\n",
-    "evidence that a non-linear function of `year` is needed\n",
-    "($p$-value=0.435).  In other words, based on the results of this\n",
-    "ANOVA, $\\mathcal{M}_2$ is preferred.\n",
-    "\n",
-    "We can repeat the same process for `age` as well. We see there is very clear evidence that\n",
-    "a non-linear term is required for `age`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "a05df53b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.239488Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.239277Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.264822Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.264344Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>deviance</th>\n",
-       "      <th>df</th>\n",
-       "      <th>deviance_diff</th>\n",
-       "      <th>df_diff</th>\n",
-       "      <th>F</th>\n",
-       "      <th>pvalue</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3.975443e+06</td>\n",
-       "      <td>2991.000589</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>3.850247e+06</td>\n",
-       "      <td>2990.000704</td>\n",
-       "      <td>125196.137317</td>\n",
-       "      <td>0.999884</td>\n",
-       "      <td>101.270106</td>\n",
-       "      <td>1.681120e-07</td>\n",
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-       "    <tr>\n",
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-       "      <td>3.693143e+06</td>\n",
-       "      <td>2987.007254</td>\n",
-       "      <td>157103.978302</td>\n",
-       "      <td>2.993450</td>\n",
-       "      <td>42.447812</td>\n",
-       "      <td>5.669414e-07</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "source": [
+        "bs_age0 = MS([bs('age',\n",
+        "                 df=3,\n",
+        "                 degree=0)]).fit(Wage)\n",
+        "Xbs0 = bs_age0.transform(Wage)\n",
+        "summarize(sm.OLS(y, Xbs0).fit())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ad6376f5",
+      "metadata": {
+        "id": "ad6376f5"
+      },
+      "source": [
+        "This fit should be compared with cell [15] where we use `qcut()`\n",
+        "to create four bins by cutting at the 25%, 50% and 75% quantiles of\n",
+        "`age`.  Since we specified `df=3` for degree-zero splines here, there will also be\n",
+        "knots at the same three quantiles. Although the coefficients appear different, we\n",
+        "see that this is a result of the different coding. For example, the\n",
+        "first coefficient is identical in both cases, and is the mean response\n",
+        "in the first bin. For the second coefficient, we have\n",
+        "$94.158 + 22.349 = 116.507 \\approx 116.611$, the latter being the mean\n",
+        "in the second bin in cell [15]. Here the intercept is coded by a column\n",
+        "of ones, so the second, third and fourth coefficients are increments\n",
+        "for those bins. Why is the sum not exactly the same? It turns out that\n",
+        "the `qcut()` uses $\\leq$, while `bs()` uses $<$ when\n",
+        "deciding bin membership.\n",
+        "\n",
+        "\n",
+        "    \n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "    \n",
+        ""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "99749b1e",
+      "metadata": {
+        "id": "99749b1e"
+      },
+      "source": [
+        "In order to fit a natural spline, we use the `NaturalSpline()`\n",
+        "transform with the corresponding helper `ns()`.  Here we fit a natural spline with five\n",
+        "degrees of freedom (excluding the intercept) and plot the results."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "bd2b4215",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.460525Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.460295Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.489739Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.489248Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "bd2b4215",
+        "outputId": "9c5d0d2f-5698-4753-b9d7-6e2d23ee3d12",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 255
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     coef  std err       t  P>|t|\n",
+              "intercept         60.4752    4.708  12.844  0.000\n",
+              "ns(age, df=5)[0]  61.5267    4.709  13.065  0.000\n",
+              "ns(age, df=5)[1]  55.6912    5.717   9.741  0.000\n",
+              "ns(age, df=5)[2]  46.8184    4.948   9.463  0.000\n",
+              "ns(age, df=5)[3]  83.2036   11.918   6.982  0.000\n",
+              "ns(age, df=5)[4]   6.8770    9.484   0.725  0.468"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-57135daf-564c-4bb2-bc0b-9d9b3d5e0089\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>coef</th>\n",
+              "      <th>std err</th>\n",
+              "      <th>t</th>\n",
+              "      <th>P&gt;|t|</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>intercept</th>\n",
+              "      <td>60.4752</td>\n",
+              "      <td>4.708</td>\n",
+              "      <td>12.844</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ns(age, df=5)[0]</th>\n",
+              "      <td>61.5267</td>\n",
+              "      <td>4.709</td>\n",
+              "      <td>13.065</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ns(age, df=5)[1]</th>\n",
+              "      <td>55.6912</td>\n",
+              "      <td>5.717</td>\n",
+              "      <td>9.741</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ns(age, df=5)[2]</th>\n",
+              "      <td>46.8184</td>\n",
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+              "      <td>9.463</td>\n",
+              "      <td>0.000</td>\n",
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+              "    <tr>\n",
+              "      <th>ns(age, df=5)[3]</th>\n",
+              "      <td>83.2036</td>\n",
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+              "      <td>6.982</td>\n",
+              "      <td>0.000</td>\n",
+              "    </tr>\n",
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+              "      <th>ns(age, df=5)[4]</th>\n",
+              "      <td>6.8770</td>\n",
+              "      <td>9.484</td>\n",
+              "      <td>0.725</td>\n",
+              "      <td>0.468</td>\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
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+              "\n",
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+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-57135daf-564c-4bb2-bc0b-9d9b3d5e0089');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"summarize(M_ns)\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"coef\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.344391551143353,\n        \"min\": 6.877,\n        \"max\": 83.2036,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          60.4752,\n          61.5267,\n          6.877\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std err\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0552034956774974,\n        \"min\": 4.708,\n        \"max\": 11.918,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.708,\n          4.709,\n          9.484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"t\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.569023819883921,\n        \"min\": 0.725,\n        \"max\": 13.065,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          12.844,\n          13.065,\n          0.725\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P>|t|\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1910601999370879,\n        \"min\": 0.0,\n        \"max\": 0.468,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.468,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
       ],
-      "text/plain": [
-       "       deviance           df  deviance_diff   df_diff           F  \\\n",
-       "0  3.975443e+06  2991.000589            NaN       NaN         NaN   \n",
-       "1  3.850247e+06  2990.000704  125196.137317  0.999884  101.270106   \n",
-       "2  3.693143e+06  2987.007254  157103.978302  2.993450   42.447812   \n",
-       "\n",
-       "         pvalue  \n",
-       "0           NaN  \n",
-       "1  1.681120e-07  \n",
-       "2  5.669414e-07  "
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gam_0 = LinearGAM(year_term +\n",
-    "                  f_gam(2, lam=0))\n",
-    "gam_linear = LinearGAM(l_gam(0, lam=0) +\n",
-    "                       year_term +\n",
-    "                       f_gam(2, lam=0))\n",
-    "gam_0.fit(Xgam, y)\n",
-    "gam_linear.fit(Xgam, y)\n",
-    "anova_gam(gam_0, gam_linear, gam_full)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0bf66bc5",
-   "metadata": {},
-   "source": [
-    "There is a (verbose) `summary()` method for the GAM fit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "ee9db82c",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.267342Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.267144Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.271357Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.270406Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LinearGAM                                                                                                 \n",
-      "=============================================== ==========================================================\n",
-      "Distribution:                        NormalDist Effective DoF:                                     12.9927\n",
-      "Link Function:                     IdentityLink Log Likelihood:                                 -24117.907\n",
-      "Number of Samples:                         3000 AIC:                                            48263.7995\n",
-      "                                                AICc:                                             48263.94\n",
-      "                                                GCV:                                             1246.1129\n",
-      "                                                Scale:                                           1236.4024\n",
-      "                                                Pseudo R-Squared:                                   0.2928\n",
-      "==========================================================================================================\n",
-      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
-      "================================= ==================== ============ ============ ============ ============\n",
-      "s(0)                              [465.0491]           20           5.1          1.11e-16     ***         \n",
-      "s(1)                              [2.1564]             7            4.0          8.10e-03     **          \n",
-      "f(2)                              [0]                  5            4.0          1.11e-16     ***         \n",
-      "intercept                                              1            0.0          1.11e-16     ***         \n",
-      "==========================================================================================================\n",
-      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
-      "\n",
-      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
-      "         which can cause p-values to appear significant when they are not.\n",
-      "\n",
-      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
-      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
-      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
-     ]
-    }
-   ],
-   "source": [
-    "gam_full.summary()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1fbf126f",
-   "metadata": {},
-   "source": [
-    "We can make predictions from `gam` objects, just like from\n",
-    "`lm` objects, using the `predict()`  method for the class\n",
-    "`gam`.  Here we make predictions on the training set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "cf0a024d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.274135Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.273728Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.283548Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.283095Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "Yhat = gam_full.predict(Xgam)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "70afdc16",
-   "metadata": {},
-   "source": [
-    "In order to fit a logistic regression GAM, we use `LogisticGAM()` \n",
-    "from `pygam`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "0fe9f031",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.286193Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.285994Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.372533Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.372014Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LogisticGAM(callbacks=[Deviance(), Diffs(), Accuracy()], \n",
-       "   fit_intercept=True, max_iter=100, \n",
-       "   terms=s(0) + l(1) + f(2) + intercept, tol=0.0001, verbose=False)"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gam_logit = LogisticGAM(age_term + \n",
-    "                        l_gam(1, lam=0) +\n",
-    "                        f_gam(2, lam=0))\n",
-    "gam_logit.fit(Xgam, high_earn)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "bdc73505",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.375490Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.375048Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.476110Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.475521Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax = plot_gam(gam_logit, 2)\n",
-    "ax.set_xlabel('Education')\n",
-    "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of wage on education',\n",
-    "             fontsize=20);\n",
-    "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9aed5a52",
-   "metadata": {},
-   "source": [
-    "The model seems to be very flat, with especially high error bars for the first category.\n",
-    "Let's look at the data a bit more closely."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "44f57a30",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.478694Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.478494Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.491764Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.491357Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>education</th>\n",
-       "      <th>1. &lt; HS Grad</th>\n",
-       "      <th>2. HS Grad</th>\n",
-       "      <th>3. Some College</th>\n",
-       "      <th>4. College Grad</th>\n",
-       "      <th>5. Advanced Degree</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>high_earn</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>False</th>\n",
-       "      <td>268</td>\n",
-       "      <td>966</td>\n",
-       "      <td>643</td>\n",
-       "      <td>663</td>\n",
-       "      <td>381</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>True</th>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>22</td>\n",
-       "      <td>45</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "source": [
+        "ns_age = MS([ns('age', df=5)]).fit(Wage)\n",
+        "M_ns = sm.OLS(y, ns_age.transform(Wage)).fit()\n",
+        "summarize(M_ns)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d5bc0531",
+      "metadata": {
+        "id": "d5bc0531"
+      },
+      "source": [
+        "We now plot the natural spline using our plotting function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "9162a058",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.493122Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.492786Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.668184Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.667651Z"
+        },
+        "id": "9162a058",
+        "outputId": "d225474f-137c-487b-80b6-ef9e19f84ee9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 740
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "education  1. < HS Grad  2. HS Grad  3. Some College  4. College Grad  \\\n",
-       "high_earn                                                               \n",
-       "False               268         966              643              663   \n",
-       "True                  0           5                7               22   \n",
-       "\n",
-       "education  5. Advanced Degree  \n",
-       "high_earn                      \n",
-       "False                     381  \n",
-       "True                       45  "
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.crosstab(Wage['high_earn'], Wage['education'])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1828779e",
-   "metadata": {},
-   "source": [
-    "We see that there are no high earners in the first category of\n",
-    "education, meaning that the model will have a hard time fitting.  We\n",
-    "will fit a logistic regression GAM excluding all observations falling into this\n",
-    "category. This provides more sensible results.\n",
-    "\n",
-    "To do so,\n",
-    "we could subset the model matrix, though this will not remove the\n",
-    "column from `Xgam`. While we can deduce which column corresponds\n",
-    "to this feature, for reproducibility’s sake we reform the model matrix\n",
-    "on this smaller subset.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "3b061ffd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.494311Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.494116Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.498822Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.498423Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "only_hs = Wage['education'] == '1. < HS Grad'\n",
-    "Wage_ = Wage.loc[~only_hs]\n",
-    "Xgam_ = np.column_stack([Wage_['age'],\n",
-    "                         Wage_['year'],\n",
-    "                         Wage_['education'].cat.codes-1])\n",
-    "high_earn_ = Wage_['high_earn']\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e3b8e93",
-   "metadata": {},
-   "source": [
-    "In the second-to-last line above, we subtract one  from the codes of the category, due to a bug in `pygam`. It just relabels\n",
-    "the education values and hence has no effect on the fit.\n",
-    " \n",
-    "We now fit the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "220e26ac",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.501236Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.501049Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.540965Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.540424Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LogisticGAM(callbacks=[Deviance(), Diffs(), Accuracy()], \n",
-       "   fit_intercept=True, max_iter=100, \n",
-       "   terms=s(0) + s(1) + f(2) + intercept, tol=0.0001, verbose=False)"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gam_logit_ = LogisticGAM(age_term +\n",
-    "                         year_term +\n",
-    "                         f_gam(2, lam=0))\n",
-    "gam_logit_.fit(Xgam_, high_earn_)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9838bbdc",
-   "metadata": {},
-   "source": [
-    "Let’s look at the effect of `education`, `year` and `age` on high earner status now that we’ve\n",
-    "removed those observations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "94209d95",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.543510Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.543313Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.635421Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.634852Z"
-    }
-   },
-   "outputs": [
+      "source": [
+        "plot_wage_fit(age_df,\n",
+        "              ns_age,\n",
+        "              'Natural spline, df=5');\n"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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2e2vatKn5+++/U3WTfAdLZgJCdmrNiKd+o7nd9nuiTViyZIn9PL2aLly4YI/ElitXLlWbfPXe9PSGk1lJe8gbN26cZvuf5OTJk6ney8r68NChQ+l+18YYc/r0aVO7dm0bVNKS0To1OU9sRCbtmMkoAKQ1bzIjaUM4X758ZvHixak+P3XqlKlRo4aRZLy8vNLccZP8iGyDBg3MmTNnUnUzZ84c283EiROvqVZPLLueWid64rvN7vost7ff3E1z8u24tm3bptk2JW9P//nPf6b6/Op25aOPPkrVTVxcnLn11luNdOXoiLv2Ii0eDQjnzp0zP/30k2ndunWKjRt3jXhaunfvbqQrewDSLPr/hu3l5ZVhI+fJgJAZTzzxhJFkbr311jQ/z25A6Ny5s5Gu7Jk+fPhwmt1cvnzZLhRpBYSzZ8/aPcPvvvuu2/G99957dgPk6kNUyQPCo48+mu4wPvvsM9vd1afDJO0R8Pf3z3BPSXh4eLphJWn4/v7+6R69iY2NtXsJhgwZkurzpIa9ePHi6a4Yz549a0JDQ9NtDA8cOGDy5ctnJJkFCxa4nZ5nnnnGbhhfLXlA+Pbbb9MdRlIQr1evXqrPhg8fbocxf/58t7VcbdWqVbbfn3/+2W23SXtCmjVrlqVxGGPMq6++asfj7nB68g25zz//PNXnOREQ3J0WN2zYMCNd2Xvq7ghQWr/zQYMG2XGkd+THGJPi8HNGASE7tWbEU7/RzPBk2++JNiFp4ymjU1J+/fXXdMNR8nXQbbfd5nY4mVGlSpV0681IVtaHmTF//nw7vD///DPV57kdEJKOciU/kuQpGzZssNMyaNCgdLuLjIy03T388MOpPk8eELZv357mMBITE03JkiWNJHPnnXdeU72eWHY9sU40xjPfbXbWZ5nlye03d9PcqVMnu12VVphJknSExtvb2xw5ciTFZ8l/y+kdtTXGmPfffz/D5S092bpIecyYMSku0ihQoIBat25tL3QqVqyY5s+fLz8/v3SHceLECe3du1c7d+60r9DQUEnS9u3b3Y6/efPmGV7MkpP++usv7d+/X7/88outPTg4WJL066+/6uLFix4d3+XLl+28bd++vUqWLJlmd15eXurdu3e6w1m5cqXOnDkjSbr77rvdjrNly5aSpIsXL2rz5s3pdpf84rer3XnnnXa+JN2vPsm3334rSWrVqpX93jOqZd26del2c/vtt6tYsWJpflawYEFVqVJFkvT777+n+CwmJsZeuPbPf/5T+fPnT3MYBQoU0D//+c90x//999/r8uXLyp8/vzp16pT+xOj/T8+RI0d08ODBNLsJDg7WHXfcke4wGjRoICn19EjSd999J0mqWLGiunbt6raWqyV9L9WqVVOtWrXcdps0HVFRUVm+GDZpeQgODlaPHj3S7a5fv36p+slpvXr1SvezpPlujNGBAweyNNyk+uvVq6eaNWum290DDzyQ6WHmVK2S53+jSXKr7b/WNiE2Nta2txm1k9WrV1fRokUluZ92d99TZoWFhUm6cnFjbt0UQ7pykXl0dHSK9V3yu4Zl9J3lhqR589lnn137hZnpSN7uPPTQQ+l217x5c1WvXj1VP1erVauWateuneZnLpdL9erVk5R2254RTyy7nlonekp21mdpye3ttySXLl2yFzu3b99eZcqUSbfb/v37237cPVsnM+2/lPVlKVvPQUhPhQoVdPfdd+upp55Ks2Fes2aN3nnnHS1btkynTp1KdzgZNX7p/bhy0o4dO/Tmm29q4cKFbp8SnZiYqL/++ivdFdO12L9/v230GjVq5Lbb5HeRutqmTZvs/5Ma1MxIb3p9fX1Vp06ddPvz8fFRvXr1tGLFCu3YsSPNWhYvXpzpO864m++33HKL235DQkIkSWfPnk3xfvK6MjNvJ0+enOZnSdPz999/y9s78z+vo0ePqmzZsqner1Kliry80s/x6U3PxYsXtXPnTklSRERElu/mkzQdu3fvznS/Fy9e1KlTp7K0zCfVWL9+fbe3KC1evLjKly+v6Oho209Oc7csJc13KfW8dycuLk779u2TlLLhTkvDhg0zPdycqDWJJ3+jTrT919ombN261d6h5Z577tE999yTqfG5a588sc7q3bu3Vq1apX379qly5crq0aOHbr/9drVo0UKlS5fO9vCT+/PPPzVx4kR99dVX2rt3r9u7VOVmWElP79699dJLL2nt2rWqUKGCevbsqbZt2yoiIiLDcJuRpHbH19dXdevWddtt48aN9dtvv2nv3r1KSEiQr69vqm6udbnMDE8su55aJ3pCdtdnSZzcfkvy+++/2+24xo0bu+02+efu1ns51f577EnKLpdL/v7+Klq0qIKCgtLtZ/To0SlufeaOu9tASleet5Cbpk2bpkGDBmV6L2lG9WdV8hVqRgtu8eLF0/3s+PHj1zT+9PbIhISEKF++fJmq5+qNgmupxd18TW8vR5Kkje3Lly+neP96nbeZnZ6rbzV36tQpuzLPSghM4unpSE/SfM9MQ1yiRAlFR0e73bD0JHfzPnlou3pZcuf06dP2/xltsGRlgyYnak3iqd+oU23/tbYJOfEb8MQ668EHH9T+/fs1YcIEnTlzRjNmzNCMGTMkSZUqVVK3bt30yCOPqGLFitkaz+bNm9WhQwedPHkyU917en13LV544QUdPnxYM2bM0PHjxzV58mS74VqzZk3dddddevjhh9224elJandCQkIy3PlTokQJSVeO2v31119pju9al8vM8MSy66l1oidkd30mOb/9liQr8zVpObq6v6vlVPvv8Scpu7N8+XK7gqhYsaKeeuopRUREqGzZsgoMDLQ/upEjR+qll17KcHgZbZR60q5du+zCVaxYMT399NO67bbbVL58eRUsWNDu/Zw+fbo9/Ohub0t2Zef+7skXki1btmT64VLp7Z3yRC2dOnXShAkTrnk4nuSJ6SlatKhWrFiR6f4qVKhwzePMCUnTUadOHc2ZMyfT/ZUqVeqaxpfd5xUg53jiN3ojtv3J28kpU6aoWbNmmerPXQjwVN1jx47VgAED9PHHH2v58uVav369/v77b+3fv18TJ07Uu+++q3feeUeDBg26puEnJCTon//8p06ePCkfHx/95z//Ubdu3VS1alUVLlzYnjb8+++/q1KlSpJydn2XWT4+Ppo2bZqGDh2quXPn6scff9SmTZuUkJCgX375Rb/88osmTpyoOXPmqFu3btc0jhuhrfL0snsjTLM719v2W5Lrfb7myClG6Zk6daqkKwvh+vXr091Dllt7CLNi5syZunTpkvLly6eVK1eme0gnJ2tP/uM9duyY227dfV6kSBH7/9DQ0Gwflj558qQuX77sduWXVE/yw11JtRw5ckQJCQlZCpue5ul5e/bsWVWvXj1XQ2xyISEh8vLyUmJiomJiYrLcf9J0nDt3Lke/l5CQEMXExGQ4z6X/f/j76mXoRpJ0jqt05Rx8dzL6PLd44jd6I7b9ydvJ/PnzO9o+paVcuXIaMWKERowYoYsXLyoqKkqff/65pkyZori4OD388MNq3LixPZc9K3788Ud7vvJ7772X4hqg5Dz5faV3NPRq58+fz3BYNWrU0EsvvaSXXnpJcXFxioyM1CeffKLZs2fr3Llzuueee7R///4s7Y1OandOnjypS5cuuT2KkNRWuVyuXD/TQfLMsuupdaKU/e82u+uz62H7LUny9VdG8y35aVBOrPdy7EnKafnll18kSW3atHF7+Dz5OfLZ5amEllR7nTp13J7v5cnar1apUiUFBARIunJBqDvuPk++wlizZk2260pISHB7gdqlS5e0bds2SUrVUCXVkrSXxynJL8T1xLyNj4/P0WUhIz4+PnZer169Ost7Q5JfIOfuXM3sSqpxy5Ytbg/9Hj9+3D6R/XrbUMsKf39/u8fV3UX/Us62JVnhid+oE21/dtWtW9euPzzRTuYkHx8fNWvWTG+99ZY++eQTSVf2gH755Zcpusvs+jDp+5Kkf/3rX+l2l9H3lZX1b8GCBSVduXjUncw8+Tc5f39/tWvXTtOnT9drr70m6crpI0kXvWZWUruTkJBg12fpSXqKdZUqVdK8/iCneWLZ9dQ6Ucr+d5vd9Zmntt88sT1ZsWJFe0rQhg0b3Hab/GnoTqz3cjUgJG0AuNsDsHXr1gxnWlb4+/vb/8fHx1/zcDJTe0xMjL3jR07w9vZW69atJUlLlixJN0knJiZq1qxZ6Q6nXbt2dgF95513PHIozd34vv76a9swtGvXLsVnSXcjSDqX1iklS5a0d5744osv0j3/8Pz58/r888/THU6XLl1sI/LWW295vM6s6NKliyTpwIED+uabb7LUb9L3YozR22+/7fHakiQtD6dPn9a8efPS7W7atGl2Ob16GbrRtG3bVtKVti75htjVZs+enVslueWJ36gTbX92hYaGqkmTJpKkTz755Lo5opORpOVLSn3hcGbXh8nDenrfWWJioj0ylJ6srH+TTrPcsmVLuuukX375RT///LPb4bjjbt5kJHm7M3369HS7W7dunb37j1NtlSeWXU+tEyXPfLfZWZ95avstaXnOzrakt7e3WrVqJUlaunSpDh06lG63H374oe0nadsvN+VqQEi6nVxkZKS9k0dyJ06c0P333+/RcSY/hLh///5rHk5S7Xv37tXatWtTff7333/r3nvvzfELtQYPHizpygI6cODANC86GT9+fKq7BSUXHBysRx99VJK0du1aDRkyxO2hv2PHjtkFNT3//e9/FRkZmer9o0eP6qmnnpJ05VDn1bdf7d27t73N11NPPaVVq1a5HU9kZKS9RZinJc3bo0ePaujQoWl2M2TIELcXgFWrVk09e/aUJH366aeaOHGi23EeOHBAc+fOvcaK3Xv00UcVGBgoSRo4cKDbuyBc3Ui1b9/e3gnrtddey3AFsGPHDi1YsCDLNfbt29eG1aFDh+rw4cOputm+fbvGjRsn6co1Dt27d8/yeK4nAwYMsCGyf//+abYZX331lb7++uvcLi1NnviNOtH2e8Lzzz8v6cptI+++++4UF5lfLT4+XpMnT1ZcXFyO1jRnzhy3R9uWLFli/3/1tU2ZXR8mfV/SldMz0jJ8+HBt2bLFba1ZWf8mbTQdOXIkzTbx7Nmzbm8veurUKS1YsMDtDi938yYj4eHh9s5iU6dO1fLly1N1c+bMGQ0cOFDSldNqktYpTvDEsuuJdaKU/e9Wyt76zFPbb0nL8++//56tHauPPPKIpCtHox566KE0b6k6ffp0u7z26NHjmi/OzpYsPTXBZO5JyulJ/nTQkiVLmnfeecesWbPGrFmzxrz22msmLCzMuFyuFI+HTktWxh8bG2sfW16/fn2zZMkSs3v3brN3716zd+/eFE8xdfdQl40bN9rPgoODzdixY83KlSvNhg0bzHvvvWcfXtO8eXO3D6LJ7oPSjDGmS5cudhyNGzc2n376qX2U/L/+9S8jyT4SXGk8KM2YK0/Ya9y4se2mTp06ZtKkSSYyMtJs3brV/Pjjj+bdd9813bp1M76+vqZBgwaphpH0UJPQ0FBTrlw54+/vb4YPH25Wr15tNm7caCZNmmQf9iLJvPHGG2lOz7p164yfn5+RrjyhslevXuaLL74wmzZtMhs3bjTffPONGTlypKlVq5ZROg93y+wy4W7+X7x40dSrV88Oq2PHjmb+/Plm8+bNZv78+aZ9+/ap5m16T6+tWLGi7aZly5bmww8/NOvWrTNbtmwxS5cuNa+//rpp166d8fLySvNBNpldTjJ6EFHyR9MHBASYxx57zCxcuNBs3brVrF692vz3v/81nTp1MhUrVkzV7759+0xISIjtv0uXLmbOnDlmw4YNZtOmTeaHH34wY8eOtQ9rGzp0qNta0zN58mQ7juLFi5s333zTbNiwwaxZs8aMGTPGPt3S5XKZ77//Ps1h5MSD0txJ3g6mtQxk9P0NGDDA9l+zZk0zc+ZMs2nTJvPjjz+aRx991OTLl88+dEySGT16dI7VmhnZ/Y060fZ7ok0wxpjHH3/cDqtEiRJm9OjRZtmyZWbr1q0mMjLSzJw50zz00EOmcOHCRpI5e/Zsiv49/XCypN/J4MGDzUcffWTWrl1rtmzZYhYuXGiefPJJExAQYCSZAgUKpHoIU2bXh+fOnTPFihWz3/fAgQPNokWLzKZNm8ynn35q2rZtm2p9l9Z6Zu/evfbz9u3bm5UrV5o9e/bY8SV/suvx48dNoUKFjHTl4XZjxowx69evt+vYypUrG39/f9tGX/1bP3DggJFkypcvb5588knz2WefmfXr15tNmzaZBQsWmAEDBtgndZcqVSrV95QZW7dutQ/W8/X1NUOHDjU//fSTiYqKMh988EGKdv+ZZ55JcxieeGhYZmV32fXUOjG7322Sa12feWr7berUqfbzJ554wmzatMkuy9HR0Sm6zeh77tmzpx1W/fr1zZw5c8ymTZvM0qVLzUMPPWRcLpeRZEJCQsyhQ4dS9Z/ZdiXpd5Heb9SdXA0IxhjTt29f2//Vr3z58pm33norwxVfVsef9MTatF7JF+aMxjtmzJh0h5O0gZTRl+aJgBAbG5tiQb76Va9ePbN58+YMF4rY2FjTo0cPt9OU9GrTpk2q/pM3YFFRUaZo0aLp9v/YY4+5naZ169aZMmXKZKqWWbNmperfUxsDhw8fNtWqVUt33O3btzeLFy922xgaY0xMTIxp0aJFpqanb9++Wa4zSWY2EmfOnGk3GtJ7pdcg7969O8VTud29xowZ47ZWd8aOHWtX3mm9/Pz80vzek9xoASE+Pt784x//SHd6K1SoYPbt22f/fuWVV3Ks1szK7m80t9t+T7UJiYmJZsyYMcbb2zvD6Q4MDEyx08mYnAkIGb2CgoLMwoUL0+w/s+vDRYsW2TCR1qt169Zm586d9u/01jNJT1pP63X1/Pj888/tk+ivfgUEBJgvvvgi3d968g0hd6+wsDCzadOma57/ixcvthu76b0eeeQRc/ny5TT7z82AkN1l1xjPrROz890md63rM09sv509ezZFCHQ3zoy+5wsXLpg777zTbU0lS5Y0W7duTbP/3AgIuXqKkXTlsMlHH32kFi1aqGDBgvLz81O5cuV0//33a+3atXr88cc9Ps5XXnlFU6dOVYsWLTJ1z/70jBw5Ut9//73at2+vwoULy9fXV6VLl1aPHj20ZMkSvf766x6uPG0FCxbUTz/9pHfffVeNGjVSgQIFVLBgQdWtW1fjx4/X2rVrM3XFe8GCBfXVV19p9erV6tevn6pVq6aCBQvK29tbISEhatSokR555BH98MMPWrp0qdthNWzYUFu2bNFjjz2mSpUqyd/fX0WKFFHHjh31ww8/ZHgee5MmTbR37169//77uuOOO1SyZEn5+vrK399fZcqUUfv27TV27Fjt2rUrS0+YzaqSJUtq69atevnll3XrrbcqICBAwcHBatKkid577z0tXLgwUxedlShRQqtWrdJ3332nXr162QuTfHx8FBoaqmbNmmno0KFauXKl2/NZPaF3797av3+/nnvuOTVo0EDBwcHKly+fChcurCZNmmjEiBFatGhRmv1WrVpV27Zt0yeffKK77rpLZcuWVUBAgHx9fRUWFqbWrVvr+eef1+bNmzVy5MhrrnHEiBHaunWr+vfvby/GDwwMVPXq1fX444/n+Pee23x9ffXtt99qxowZioiIUFBQkPLnz6/q1atrxIgR2rx5c4o7kbh7tkxuye5v1Im23xNcLpdGjhypPXv26JlnnlHDhg3teqRgwYKqUaOGevXqpVmzZikmJsbeSCKn7Ny5U6+++qq6dOmiGjVqqEiRIsqXL59tp0aNGqXdu3erY8eOafaf2fVhhw4dtGnTJt13330qWbKkbbtatWqlDz74QMuXL7enfLgzZ84cTZgwQeHh4QoKCnL74MeePXtq7dq1uvPOOxUaGipfX1+VKVNGvXv3VlRUlNunApcrV04bN27U6NGj1b59e1WrVk3BwcHy9vZW0aJF1bJlS7322mvatWtXhg8pdKd9+/bat2+fRowYobp166pQoULy8/NT2bJl1atXL61evVqTJk1yO525xRPLrqfWidn5bpO71vWZJ7bfChQoYNuq6tWrZ/gsC3f8/f01b948ffvtt+rRo4dtTwsXLqzGjRtr/Pjx2r17d4YP5ctJLmOug5sX44bUp08fzZo1S+XKlVN0dLTT5QA3lcjISLVo0UKStGzZshQXWAIAkJOcj7gAgFSSLubz8fHJ1h5PAACyioAAALnszz//dHtXkcWLF2vKlCmSrtxmNPkD1gAAyGm5+iRlAMCV88i7deumnj17ql27dqpUqZK8vLz0v//9T99++63mzJmjy5cvKyAgwN7iFQCA3EJAAAAHxMbGatq0aZo2bVqanxcqVEhffPGFqlatmsuVAQDyOgICAOSyhg0baubMmVq0aJG2b9+uEydO6PTp0ypUqJAqV66sjh076tFHH1VoaKjTpQIA8iDuYgQAAADA4gjCNUhMTNSRI0dUsGBBuVwup8sBAADAVYwxOnv2rEqWLHldPJviRkJAuAZHjhxRmTJlnC4DAAAAGfjjjz9UunRpp8u4oRAQrkHBggUlXVngChUq5HA1AAAAuFpsbKzKlCljt9uQeQSEa5B0WlGhQoUICAAAANcxTgfPOk7IAgAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgOXtdAEAAAA3g5iYGMXExOTa+MLCwhQWFpZr40PeQUAAAADwgClTpmjMmDG5Nr5Ro0Zp9OjRuTY+5B0EBAAAAA8YOHCgunbtmunuL1y4oIiICElSZGSkAgICsjQ+jh4gpxAQAAAAPCCrp/ycP3/e/r9u3boKDAzMibKALOMiZQAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFh5MiCsWrVKXbp0UcmSJeVyuTR//nynSwIAAACuC3kyIJw/f1516tTR5MmTnS4FAAAAuK54O12AEzp16qROnTo5XQYAAABw3cmTASGr4uPjFR8fb/+OjY11sBoAAAAg5+TJU4yyavz48QoKCrKvMmXKOF0SAAAAkCMICJkwfPhwnTlzxr7++OMPp0sCAAAAcgSnGGWCn5+f/Pz8nC4DAAAAyHEcQQAAAABg5ckjCOfOndO+ffvs3wcOHNC2bdsUEhKismXLOlgZAAAA4Kw8GRA2bdqkNm3a2L+ffPJJSVLv3r01c+ZMh6oCAABJyg/73ukSclxiQpz9f/UXFsnL19/BanJH9Ct3OF0CMiFPBoTWrVvLGON0GQAAAMB1h2sQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFjeThcAAABwM7h07pQunzuV6e7NxQT7/4Rjv8vl45ul8eUrECLvAiFZ6gfIDAICAACAB5zbtlBn1sy9pn6PffJMlvsJan6PgiN6XdP4AHcICAAAAB5QoG4nBVRunGvjy8fRA+QQAgIAAIAHeHPKD24SXKQMAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsb6cLAADAKTExMYqJicm18YWFhSksLCzXxgcA14KAAADIs6ZMmaIxY8bk2vhGjRql0aNH59r4AOBaEBAAAHnWwIED1bVr10x3f+HCBUVEREiSIiMjFRAQkKXxcfQAwI2AgAAAyLOyesrP+fPn7f/r1q2rwMDAnCgLABzFRcoAAAAALAICAAAAAIuAAAAAAMAiIAAAAACwCAgAAAAALAICAAAAAIuAAAAAAMAiIAAAAACwCAgAAAAALAICAAAAAIuAAAAAAMAiIAAAAACwCAgAAAAALAICAAAAAIuAAAAAAMAiIAAAAACw8nRAmDx5ssqXLy9/f381btxYGzdudLokAAAAwFF5NiB89tlnevLJJzVq1Cht2bJFderUUYcOHXT8+HGnSwMAAAAck2cDwsSJE9W/f3/17dtXNWrU0Pvvv6/8+fNr+vTpTpcGAAAAOCZPBoSEhARt3rxZ7dq1s+95eXmpXbt2WrduXaru4+PjFRsbm+IFAAAA3Iy8nS7ACX/++acuX76s4sWLp3i/ePHi2rVrV6rux48frzFjxuRWeekqP+x7p0tADoh+5Q5HxsvydPNyYpnKK8tTYkKc/X/1FxbJy9ffwWpyh1NtlFPjBZBHjyBk1fDhw3XmzBn7+uOPP5wuCQAAAMgRefIIQtGiRZUvXz4dO3YsxfvHjh1TiRIlUnXv5+cnPz+/3CoPAAAAcEyePILg6+urBg0aaPny5fa9xMRELV++XE2bNnWwMgAAAMBZefIIgiQ9+eST6t27txo2bKjw8HC99dZbOn/+vPr27et0aQAAAIBj8mxA+Ne//qUTJ05o5MiROnr0qOrWratFixalunAZAAAAyEvybECQpEcffVSPPvqo02UAAAAA1408eQ0CAAAAgLQREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAlrfTBQAAbnzRr9zhdAm54vz58yrw5pX///ZSRwUGBjpbEADkAI4gAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwCIgAAAAALAICAAAAAAsAgIAAAAAi4AAAAAAwLpuAkJcXJzTJQAAAAB5nqMBITExUS+99JJKlSqlAgUK6Pfff5ckvfDCC5o2bZqTpQEAAAB5kqMB4eWXX9bMmTM1YcIE+fr62vdvvfVWffjhhw5WBgAAAORNjgaE2bNn64MPPlCvXr2UL18++36dOnW0a9cuBysDAAAA8iZHA8Lhw4dVuXLlVO8nJibq4sWLDlQEAAAA5G2OBoQaNWpo9erVqd7/8ssvVa9ePQcqAgAAAPI2bydHPnLkSPXu3VuHDx9WYmKi5s2bp927d2v27Nn67rvvnCwNAAAAyJMcPYLQrVs3LViwQMuWLVNgYKBGjhyp3377TQsWLNDtt9/uZGkAAABAnuToEQRJatGihZYuXep0GQAAAAB0HT0oDQAAAIDzHD2CULhwYblcrlTvu1wu+fv7q3LlyurTp4/69u3rQHUAAABA3uP4Rcpjx45Vp06dFB4eLknauHGjFi1apEceeUQHDhzQ4MGDdenSJfXv39/JUgEAAIA8wdGAEBkZqZdfflmDBg1K8f6UKVO0ZMkSffXVV6pdu7beeecdAgIAAACQCxy9BmHx4sVq165dqvfbtm2rxYsXS5I6d+6s33//PbdLAwAAAPIkRwNCSEiIFixYkOr9BQsWKCQkRJJ0/vx5FSxYMLdLAwAAAPIkR08xeuGFFzR48GCtWLHCXoMQFRWlH374Qe+//74kaenSpWrVqpWTZQIAAAB5hqMBoX///qpRo4YmTZqkefPmSZKqVaumlStXqlmzZpKkoUOHOlkiAAAAkKc4/qC05s2bq3nz5k6XAQAAAEDXQUBIEhcXp4SEhBTvFSpUyKFqAAAAgLzJ0YuU//77bz366KMqVqyYAgMDVbhw4RQvAAAAALnL0YDw9NNP68cff9R///tf+fn56cMPP9SYMWNUsmRJzZ4928nSAAAAgDzJ0VOMFixYoNmzZ6t169bq27evWrRoocqVK6tcuXL6+OOP1atXLyfLAwAAAPIcR48gnDp1ShUrVpR05XqDU6dOSZIiIiK0atUqJ0sDAAAA8iRHA0LFihV14MABSdItt9yizz//XNKVIwvBwcEOVgYAAADkTY4GhL59+2r79u2SpGHDhmny5Mny9/fXkCFD9PTTTztZGgAAAJAnOXoNwpAhQ+z/27Vrp127dmnz5s2qXLmyateu7WBlAAAAQN7kaECIi4uTv7+//btcuXIqV66cgxUBAAAAeZujASE4OFjh4eFq1aqVWrdurWbNmikgIMDJkgAAAIA8zdFrEJYtW6aOHTtqw4YN6tatmwoXLqyIiAg999xzWrp0qZOlAQAAAHmSowEhIiJCI0aM0JIlS3T69GmtWLFClStX1oQJE9SxY0cnSwMAAADyJEdPMZKkPXv26KeffrKv+Ph4/eMf/1Dr1q2dLg0AAADIcxwNCKVKldKFCxfUunVrtW7dWs8++6xq164tl8vlZFkAAABAnuXoKUahoaH6+++/dfToUR09elTHjh3ThQsXnCwJAAAAyNMcDQjbtm3T0aNHNWzYMMXHx2vEiBEqWrSomjVrpueee87J0gAAAIA8yfFrEIKDg9W1a1c1b95czZo10zfffKO5c+dqw4YNGjt2rNPlAQAAAHmKowFh3rx59uLkX3/9VSEhIYqIiNAbb7yhVq1aOVkaAAAAkCc5GhAGDRqkli1basCAAWrVqpVq1arlZDkAAABAnudoQDh+/LiTowcAAABwFUcvUgYAAABwfXH8ImUAAJwSExOjmJiYTHef/Fbc27ZtU0BAQJbGFxYWprCwsCz1AwC5jYAA4IZx6dwpXT53KtfGl69AiLwLhOTa+JD7pkyZojFjxlxTvxEREVnuZ9SoURo9evQ1jQ8AcgsBAcAN49y2hTqzZm6ujS+o+T0KjuiVa+ND7hs4cKC6du2aa+Pj6AGAGwEBAcANo0DdTgqo3DjT3ZuLCTr2yTOSpOL3TpDLxzdL48vH0YObHqf8AEBqjgaE8+fP65VXXtHy5ct1/PhxJSYmpvj8999/d6gyANcj7yye8pOYEGf/71u8orx8/XOiLAAAbiqOBoR+/fpp5cqVuv/++xUWFiaXy+VkOQAAAECe52hAWLhwob7//ns1b97cyTIAAAAA/B9Hn4NQuHBhhYRwji8AAABwvXA0ILz00ksaOXKk/v77byfLAAAAAPB/HD3F6I033tD+/ftVvHhxlS9fXj4+Pik+37Jli0OVAQAAAHmTowGhe/fuTo4eAAAAwFUcDQijRo1ycvQAAAAArnJdPCht8+bN+u233yRJNWvWVL169RyuCAAAAMibHA0Ix48f17///W/99NNPCg4OliSdPn1abdq00aeffqrQ0FAnywMAAADyHEfvYvSf//xHZ8+e1S+//KJTp07p1KlT2rlzp2JjY/XYY485WRoAAACQJzl6BGHRokVatmyZqlevbt+rUaOGJk+erPbt2ztYGQAAAJA3OXoEITExMdWtTSXJx8dHiYmJDlQEAAAA5G2OBoTbbrtNjz/+uI4cOWLfO3z4sIYMGaK2bds6WBkAAACQNzkaECZNmqTY2FiVL19elSpVUqVKlVShQgXFxsbq3XffdbI0AAAAIE9y9BqEMmXKaMuWLVq2bJl27dolSapevbratWvnZFkAAABAnuX4cxBcLpduv/123X777U6XAgAAAOR5jp5iBAAAAOD6QkAAAAAAYBEQAAAAAFgEBAAAAACWowEhX758On78eKr3T548qXz58jlQEQAAAJC3ORoQjDFpvh8fHy9fX99crgYAAACAI7c5feeddyRducXphx9+qAIFCtjPLl++rFWrVumWW25xojQAAAAgT3MkILz55puSrhxBeP/991OcTuTr66vy5cvr/fffd6I0AAAAIE9zJCAcOHBAktSmTRvNmzdPhQsXdqIMAAAAAFdx9EnKK1ascHL0AAAAAK7i6EXKd911l1599dVU70+YMEE9e/Z0oCIAAAAgb3M0IKxatUqdO3dO9X6nTp20atUqByoCAAAA8jZHA8K5c+fSvJ2pj4+PYmNjHagIAAAAyNscDQi1atXSZ599lur9Tz/9VDVq1HCgIgAAACBvc/Qi5RdeeEE9evTQ/v37ddttt0mSli9frrlz5+qLL75wsjQAAAAgT3I0IHTp0kXz58/XuHHj9OWXXyogIEC1a9fWsmXL1KpVKydLAwAAAPIkRwOCJN1xxx264447nC4DAAAAgBy+BkGSTp8+rQ8//FAjRozQqVOnJElbtmzR4cOHHa4MAAAAyHscPYLw888/q127dgoKClJ0dLT69eunkJAQzZs3TwcPHtTs2bOdLA8AAADIcxw9gvDkk0+qT58+2rt3r/z9/e37nTt35jkIAAAAgAMcDQhRUVEaOHBgqvdLlSqlo0ePOlARAAAAkLc5GhD8/PzSfCDanj17FBoammPjHTt2rJo1a6b8+fMrODg4x8YDAAAA3GgcDQhdu3bViy++qIsXL0qSXC6XDh48qGeffVZ33XVXjo03ISFBPXv21ODBg3NsHAAAAMCNyNGA8MYbb+jcuXMqVqyYLly4oFatWqly5coqWLCgxo4dm2PjHTNmjIYMGaJatWplqvv4+HjFxsameAEAAAA3I0fvYhQUFKSlS5dqzZo12r59u86dO6f69eurXbt2TpaVyvjx4zVmzBinywAAAAByXK4HhJCQEO3Zs0dFixbVgw8+qLffflvNmzdX8+bNc7uUTBs+fLiefPJJ+3dsbKzKlCnjYEUAAABAzsj1U4wSEhLsKTqzZs1SXFycR4Y7bNgwuVwut69du3Zd07D9/PxUqFChFC8AAADgZpTrRxCaNm2q7t27q0GDBjLG6LHHHlNAQECa3U6fPj3Twx06dKj69OnjtpuKFStmpVQAAAAgz8n1gDBnzhy9+eab2r9/vyTpzJkzHjmKEBoamqO3RgUAAADyglwPCMWLF9crr7wiSapQoYI++ugjFSlSJFdrOHjwoE6dOqWDBw/q8uXL2rZtmySpcuXKKlCgQK7WAgAAAFxPHL1IuU2bNvL19c3tEjRy5EjNmjXL/l2vXj1J0ooVK9S6detcrwcAAAC4Xtw0FylnxcyZM2WMSfUiHAAAACCvu2kuUgYAAACQfY5epOxyuTx2kTIAAACA7MuTFykDAAAASFuuB4TkDhw44OToAQAAAFwl1y9SlqTOnTvrzJkz9u9XXnlFp0+ftn+fPHlSNWrUcKAyAAAAIG9zJCAsXrxY8fHx9u9x48bp1KlT9u9Lly5p9+7dTpQGAAAA5GmOBARjjNu/AQAAADjDkYAAAAAA4PrkSEBwuVxyuVyp3gMAAADgLEfuYmSMUZ8+feTn5ydJiouL06BBgxQYGChJKa5PAAAAAJB7HAkIvXv3TvH3fffdl6qbBx54ILfKAQAAAPB/HAkIM2bMcGK0AAAAADLARcoAAAAALAICAAAAAIuAAAAAAMAiIAAAAACwCAgAAAAALAICAAAAAIuAAAAAAMAiIAAAAACwCAgAAAAALAICAAAAAIuAAAAAAMAiIAAAAACwCAgAAAAALAICAAAAAIuAAAAAAMAiIAAAAACwCAgAAAAALAICAAAAAIuAAAAAAMAiIAAAAACwCAgAAAAALAICAAAAAIuAAAAAAMDydroAAM6IfuUOp0vIcefPn1eBN6/8/7eXOiowMNDZggAAuAFwBAEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYHGbU+SoS+dO6fK5U7k2vnwFQuRdICTXxgcAAHCzISAgR53btlBn1szNtfEFNb9HwRG9cm18AAAANxsCAnJUgbqdFFC5caa7NxcTdOyTZyRJxe+dIJePb5bGl4+jBwAAANlCQECO8s7iKT+JCXH2/77FK8rL1z8nygIAAEA6uEgZAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgOXtdAEAkFkxMTGKiYnJdPcXLlyw/9+2bZsCAgKyNL6wsDCFhYVlqR8AAG50BAQAN4wpU6ZozJgx19RvRERElvsZNWqURo8efU3jAwDgRkVAAHDDGDhwoLp27Zpr4+PoAQAgLyIgALhhcMoPAAA5j4uUAQAAAFh5LiBER0froYceUoUKFRQQEKBKlSpp1KhRSkhIcLo0AAAAwHF57hSjXbt2KTExUVOmTFHlypW1c+dO9e/fX+fPn9frr7/udHkAAACAo/JcQOjYsaM6duxo/65YsaJ2796t//73vwQEAAAA5Hl5LiCk5cyZMwoJCUn38/j4eMXHx9u/Y2Njc6MsAAAAINfluWsQrrZv3z69++67GjhwYLrdjB8/XkFBQfZVpkyZXKwQAAAAyD03TUAYNmyYXC6X29euXbtS9HP48GF17NhRPXv2VP/+/dMd9vDhw3XmzBn7+uOPP3J6cgAAAABH3DSnGA0dOlR9+vRx203FihXt/48cOaI2bdqoWbNm+uCDD9z25+fnJz8/P0+UCQAAAFzXbpqAEBoaqtDQ0Ex1e/jwYbVp00YNGjTQjBkz5OV10xxIAQAAALLlpgkImXX48GG1bt1a5cqV0+uvv64TJ07Yz0qUKOFgZQAAAIDz8lxAWLp0qfbt26d9+/apdOnSKT4zxjhUFQAAAHB9yHPn1vTp00fGmDRfAAAAQF6X5wICAAAAgPQREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYLsP9PbMsNjZWQUFBOnPmjAoVKuR0OTeV8+fPq0CBApKkc+fOKTAw0OGKAADAjYjttWvHEQQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREAAAAABY3k4XgJtbTEyMYmJiMt39hQsX7P+3bdumgICALI0vLCxMYWFhWeoHAAAA/x8BATlqypQpGjNmzDX1GxERkeV+Ro0apdGjR1/T+AAAAEBAQA4bOHCgunbtmmvj4+gBAABA9hAQkKM45QcAAODGwkXKAAAAACwCAgAAAACLgAAAAADAIiAAAAAAsAgIAAAAACwCAgAAAACLgAAAAADAIiAAAAAAsAgIAAAAACwCAgAAAACLgAAAAADAIiAAAAAAsAgIAAAAACwCAgAAAACLgAAAAADAIiAAAAAAsAgIAAAAAKw8GRC6du2qsmXLyt/fX2FhYbr//vt15MgRp8sCAAAAHJcnA0KbNm30+eefa/fu3frqq6+0f/9+3X333U6XBQAAADjOZYwxThfhtG+//Vbdu3dXfHy8fHx8Muw+NjZWQUFBOnPmjAoVKpQLFQIAACAr2F67dt5OF+C0U6dO6eOPP1azZs3SDQfx8fGKj4+3f8fGxuZWeQAAAECuypOnGEnSs88+q8DAQBUpUkQHDx7UN998k26348ePV1BQkH2VKVMmFysFAAAAcs9NExCGDRsml8vl9rVr1y7b/dNPP62tW7dqyZIlypcvnx544AGld7bV8OHDdebMGfv6448/cmuyAAAAgFx101yDcOLECZ08edJtNxUrVpSvr2+q9w8dOqQyZcpo7dq1atq0aYbj4pw2AACA6xvba9fuprkGITQ0VKGhodfUb2JioiSluM7AnaRMxbUIAAAA16ek7bSbZF94rrppAkJmbdiwQVFRUYqIiFDhwoW1f/9+vfDCC6pUqVKmjh5I0tmzZyWJaxEAAACuc2fPnlVQUJDTZdxQbppTjDJrx44devzxx7V9+3adP39eYWFh6tixo55//nmVKlUqU8NITEzUkSNHVLBgQblcrhyuOO+JjY1VmTJl9Mcff3BIENnG8gRPY5mCJ7E85RxjjM6ePauSJUvKy+umuew2V+S5gIDrH+cMwpNYnuBpLFPwJJYnXI+IUwAAAAAsAgIAAAAAi4CA646fn59GjRolPz8/p0vBTYDlCZ7GMgVPYnnC9YhrEAAAAABYHEEAAAAAYBEQAAAAAFgEBAAAAAAWAQFuxcXFqXv37qpatarq1Kmj22+/Xfv27ctUv3369NFbb72V4r3Ro0friSeekCRdvHhRjz32mGrWrKk6deqoRo0amjhxYrrD+/TTT9WoUSNVqVJFDRs2VIsWLfTVV19d66Sl8NRTT2n06NEeGVZe0L59e9WuXVt169ZVixYttHXr1kz1t3LlSjVt2lR169ZVjRo11Lx5cx07diyHq03fZ599poYNG6patWpq0KCBunTpoh07drjt56efflLdunUlSdHR0QoODs75QvOYGTNmyOVyaf78+Znu50b8LpcuXaqWLVuqYsWKatiwocLDw/XBBx94ZNiTJk1Snz59PDKs60H58uVVrVo11a1bV3Xr1tVnn32W6X6NMapQoYLatm3rtrsbYZ7dfffdmjlzZpqfuVwu1apVS7Vr11bVqlV1zz336Ndff83dAnHT8Ha6AFz/BgwYoE6dOsnlcmnSpEnq16+ffvrpp2wP9+2339aRI0e0fft2eXt7Ky4uTvv370+z2w8//FCvv/665s2bpxo1akiSdu/erW+//TbN7i9duiRvbxbvnPL555/bjamvv/5affr00fbt2932c+nSJd15551atmyZ6tevL+nKdxgYGJjT5aZpxowZGj9+vObPn2+Xqc2bN+vIkSOqVauWIzXhyob61KlT1aRJk0z3cyN+l0uWLFGfPn305ZdfqlmzZpKkQ4cOaerUqWl2T5t2JQQmBbqsWL58uYKDg/Xzzz/rwIEDqlChgueLu06sXr1awcHBSkxM1AcffKDmzZtry5YtHptmlsO8gyMIcMvf31+dO3eWy+WSJDVp0kTR0dEeGfahQ4dUrFgx29j4+/urZs2aaXY7evRovfXWW3blL0nVqlXT008/Len/7/179tlnVb9+fU2aNEnLly9X06ZNVa9ePdWsWVPTpk2z/cbExKhDhw6qUaOG2rVrp0OHDnlkmvKK5Htaz5w5Y5cPd86ePavY2FiVKFHCvletWjUVKFBAkrRv3z61a9fOHplIvvfY5XJp7Nixaty4scqXL6/58+dr/PjxatiwoapUqZIisC5evFgRERFq0KCBwsPDtWLFijTrGTVqVKplqkGDBurQoYMdTv369VW7dm21atUqU3vioqKidNttt6lhw4aqV6+evvjiC/vZlClTVLVqVdWvX18vvfRSinnmrr+8JDExUf369dO7776bpVs+3ojf5YsvvqiRI0facCBJpUuX1pgxY+zfLpdLo0aNUqNGjTR8+HDt2LFDERERql+/vmrUqKGXX37Zdnv27Fn961//UrVq1RQREZHh0ZO8ZNq0aerfv7/uvfdeTZ8+3b7vbp5VrVpVmzZtsn/PnDlTd955pyRp4sSJatSokerWratGjRpp3bp1trvy5ctr5MiRatq0qSpUqJDiOzp8+LDuvvtuu5f/hRdesHX0799f4eHhql27tgYMGKCEhARJ0q5du9SsWTPVrFlT3bt3V2xsbKam2cvLS4MGDVKHDh303nvvZWo8TZs2Vc2aNdWjRw+1b9/eHqno06ePHnzwQbVs2VK33nqrJOmjjz5S48aNVb9+fbVs2TLFDqLXX39d4eHhql+/vjp27Kj//e9/maoZ1xkDZMF9991nHnvssUx127t3b1OyZElTp04d+ypevLh5/PHHjTHG7Ny505QuXdrccsstpl+/fmbu3Lnm0qVLqYZz7NgxI8mcOnUq3XEdOHDASDKzZs2y7506dcoO7+TJk6Zs2bLmjz/+MMYYc/fdd5vnn3/eGGPMoUOHTNGiRc2oUaMyNV244v777zelS5c2pUuXNj///HOm+nn88cdNgQIFTKdOncyLL75odu/ebT8LDw8377//vjHGmD179piQkBATHR1tjDFGknnrrbeMMcYsW7bMBAYGmhkzZhhjjPn8889Nw4YNjTHG7N+/3zRp0sScOXPGGGPM3r17TYkSJUxcXFyKOjJapo4dO2ZCQkLsdM2ZM8dUr17dJCYmmhUrVpg6deoYY64sd0FBQcYYY/766y9Tt25dc+TIEWOMMSdOnDBlypQxhw4dMjt27DAlSpQwMTExxhhjRo4caZKaX3f95TWvvfaaGTlypDHGmFatWpmvv/46w35u1O8yICDAbNmyxe20STJjxoyxf8fGxtpl+e+//zZ169Y169atM8YY89RTT5n777/fJCYmmtOnT5tbbrnF9O7dO6PZd8MoV66cqVWrlrn11lvNgw8+aI4fP56p/k6ePGmCg4PNX3/9ZbZv325Kly5tLl++bIxxP8/Gjh1rHnnkETucli1bmm+//dYYY1KMe926daZatWop6vzPf/5jjLny/RcqVMh+/61btzbjxo2z3SYNp3///nbdlZiYaB566CEzYcIEY4wxDRs2NB9++KExxpiff/7Z+Pr62rbvapLMX3/9leK9iRMnmk6dOmVqPNOnTzfGGPPrr78aPz8/O57evXub2rVrm9jYWGOMMZGRkaZTp052WVy1apWpUaOGMcaYjz/+2PTr18+ue2fPnm06d+6cZr24vnGcCJk2btw47du3T8uXL890P08//bS95kC6ciTg9OnTkqSaNWtq//79ioyM1Nq1azVq1Ch99NFH+v777zMcbps2bfTnn38qISFBu3fvliT5+Pjovvvus92cPHlSDz30kPbs2SNvb2+dPHlSO3fuVOnSpbV8+XK9/vrrkqRSpUqpa9eumZ4mXDF79mxJ0qxZs/Tss8/qhx9+yLCft956S0OGDNGKFSu0fPly1atXT4sXL1adOnW0ZcsWrVmzRpJUpUoVRUREaPXq1SpXrpwk6V//+pckqWHDhjp//rz+/e9/S5LCw8O1d+9eSdKiRYu0b98+tWzZ0o7Ty8tLBw8eVJUqVTI9bRs2bFCtWrXs6Sm9evXSI488osOHD6fbz9q1a/X777+rU6dOKd7fvXu3du7cqY4dO9qjJ/3799eLL76YYX+lSpXKdM03up07d+qrr77SqlWrPDrcG+W77NWrl3755RcdPXpUe/bsUaFChSRJDz74oO3mwoULevjhh7Vt2zZ5eXnpjz/+0LZt29SkSRMtX75cb775plwul4KCgnTvvfeme8rmjWjVqlUqW7asLl68qOeff169e/fOVJvz8ccfq1OnTgoODlZwcLCKFy+uxYsXq1OnTm7n2QMPPKB69erpjTfe0OHDh7Vnzx77vW7dulVjx47VyZMn5e3trd27d+vChQsKCAiQJN17772SpKJFi6pixYo6cOCAgoKCFBkZqcWLF9vaQkNDJUnz58/XunXr7DV4Fy5cUL58+RQbG6tt27bZ6yJq1aqliIiILM03k+xRVxmN54EHHpAkVa9ePdV4evbsqYIFC0qSvvnmG23fvl2NGze2n586dUoXLlzQ/PnzFRUVpQYNGkiSLl++nKV6cf0gICBTks7/X7ZsmfLnz++x4fr6+uq2227Tbbfdpn79+iksLEynTp1SSEiI7aZYsWIqVaqUNm7caE8ZWLFihaKjo1Ocj5o/f355ef3/s+YGDRqkzp0766uvvpLL5VL9+vUVFxeXZh2ZOUUGaevdu7cGDRqkkydPqkiRIhl2X65cOfXp00d9+vRRYGCgPv/8c9WpUydVd1d/J/7+/pKkfPnypfr70qVLkq6sDG+//XZ98sknbmsoVqyYSpcurXXr1qlz584ZT2QmGGNUs2ZNrV27NtVnO3fuTPF38mlz119esnr1akVHR9sgd/ToUQ0YMEAxMTEaPHhwuv3dqN9lvXr1tHHjRtWrV0/SlQ3ZpOElJiba7pJOwZOkESNGqGjRotq6dau8vb3Vo0ePPNOmlS1bVtKVHUFPPPGEqlatmqn+pk2bpqNHj6p8+fKSrpxmM23atFQhTko5z0qXLq2GDRvqm2++0S+//KL77rtP3t7eSkhIUI8ePbRixQo1atRIsbGxCgoKUnx8vA0ISW2TlLJ9So8xRl999VWqaUrrdKKsfq9RUVH2tKDsjCf5cmiMUe/evTVu3LhU/RljNHz4cA0YMCBLdeL6wzUIyNDEiRM1d+5cLV261KN3+Vi1apViYmLs35s3b1ZISEia4xg5cqSGDBmiXbt22ffOnz/vdvh//fWXypUrJ5fLpVWrVqU4R7Jdu3b2XNSYmJh0L3ZGaqdPn9aRI0fs3/Pnz1eRIkVShLq0nDt3TgsXLrR7tC5cuKDffvtNlSpVUsGCBVW/fn3NmDFD0pXrESIjI1McCciMDh06aNmyZfr555/texs3bkyz29GjR+vJJ59MsUxt3bpVS5YsUZMmTbRjxw67Mfjpp5+qVKlSbvcCN2vWTAcOHNCyZcvse9u2bVNCQoLatGmjxYsX6/jx45KU4noYd/3lJYMHD1ZMTIyio6MVHR2tJk2a6IMPPnAbDpLciN/lCy+8oBdffFHr16+372WmTStdurTda7106VL7Wbt27TRjxgwZYxQbG6u5c+e6HdaN5Pz58/bIsyTNnTvXBit3Nm/erBMnTujIkSN2udq/f78WL16sEydOZDjP+vbtq+nTp2v27Nn2SE5cXJwSEhJsYHn33XczNQ0FChRQy5Yt9cYbb9j3Tpw4IUnq3r27Xn31VRsk/vrrL+3bt0+FChVSvXr17NHaX375RZGRkZkaX2JioqZOnapFixbZ35C78dSpU0dz5syRdOWIl7vxdO3aVXPmzNHBgwftuJKu1+jevbvef/99nTp1StKVuxVm9i53uL5wBAFuHTp0SEOHDlXFihXVpk0bSZKfn582bNgg6cqGe8mSJTVo0KAsD/vgwYN64oknFBcXJ19fXxUoUEDffPNNiqMASQYMGKDAwEDdd999OnPmjEJDQ+Xv76/JkyenO/xXXnlFDz/8sF566SXVrVs3xeHQt99+W3369FGNGjVUqlQp3XbbbVmuP686c+aMevbsqQsXLsjLy0uhoaH67rvv7B6nfv36qWvXrqlO2zLG6P3339fjjz+ugIAAXbx4UR07dtQjjzwi6coe1EGDBmnSpElyuVz68MMP7Uo4sypXrqxPPvlEAwcO1N9//62EhATVq1cvzSMKDz30kAICAtSrVy+dO3dO3t7eqlSpksaPH6/Q0FB9/PHHeuCBB3Tp0iUVLlxYX3zxhdu9d4ULF9b333+vp556SkOHDtXFixdVtmxZzZ8/X7Vq1dLzzz+v5s2bq2DBgurYsaOCgoIy7A//n7u25kb8Ljt27Khp06bp6aef1pEjRxQaGipfX1+9++679lSOqz3//PO6//77NWvWLFWqVClFu/XCCy+oX79+uuWWWxQaGqqIiAjFx8dncS5fn44dO6a77rpLly9fljFGFStWtBvNUvptzrRp0/Tvf/87xTolODhYt99+uz766KMM51m3bt00ePBgValSRdWrV5ckFSpUSC+//LLCw8NVtGhRe6pjZnz00Uf6z3/+o5o1a8rHx0fdunXTmDFj9Oabb2rYsGGqW7euvLy85O3trQkTJqhy5cqaPXu2+vbtqzfeeENVqlTJcKdJixYt5HK5FBcXp/r162vNmjX2DkYZjefBBx/Ua6+9psqVK6tRo0bp7hBs0aKFJkyYoDvvvFOXLl1SQkKC7rjjDjVs2FC9evXSyZMn7fbCpUuX9OCDD2Yq0OH64jLJT1ADAOSIs2fP2g2/t99+W4sWLdLChQsdrgrXgu8SN5tz584pMDBQLpdLBw4cUNOmTRUVFaUyZco4XRocwhEEAMgFw4YN05o1a3Tx4kWVLFlSU6ZMcbokXCO+S9xs1q5da28bfvnyZb355puEgzyOIwgAAAAALC5SBgAAAGAREAAAAABYBAQAAAAAFgEBAAAAgEVAAAAAAGAREADgBuVyua6LB6r16dNH3bt3d7oMAICHEBAA4DrQp08fuVyuVK+OHTs6XZoVHR0tl8ulbdu2pXj/7bff1syZMx2pCQDgeTwoDQCuEx07dtSMGTNSvOfn5+dQNZkXFBTkdAkAAA/iCAIAXCf8/PxUokSJFK/ChQtLkvbu3auWLVvK399fNWrU0NKlS1P0+9NPP8nlcun06dP2vW3btsnlcik6Otq+t2bNGrVu3Vr58+dX4cKF1aFDB/3111+SpEWLFikiIkLBwcEqUqSI/vGPf2j//v223woVKkiS6tWrJ5fLpdatW0tKfYpRfHy8HnvsMRUrVkz+/v6KiIhQVFRUqlqXL1+uhg0bKn/+/GrWrJl2797tidkIAMgmAgIAXOcSExPVo0cP+fr6asOGDXr//ff17LPPZnk427ZtU9u2bVWjRg2tW7dOkZGR6tKliy5fvixJOn/+vJ588klt2rRJy5cvl5eXl+68804lJiZKkjZu3ChJWrZsmWJiYjRv3rw0x/PMM8/oq6++0qxZs7RlyxZVrlxZHTp00KlTp1J099xzz+mNN97Qpk2b5O3trQcffDDL0wQA8DxOMQKA68R3332nAgUKpHhvxIgRatiwoXbt2qXFixerZMmSkqRx48apU6dOWRr+hAkT1LBhQ7333nv2vZo1a9r/33XXXSm6nz59ukJDQ/Xrr7/q1ltvVWhoqCSpSJEiKlGiRJrjOH/+vP773/9q5syZtr6pU6dq6dKlmjZtmp5++mnb7dixY9WqVStJ0rBhw3THHXcoLi5O/v7+WZouAIBncQQBAK4Tbdq00bZt21K8Bg0apN9++01lypSx4UCSmjZtmuXhJx1BSM/evXt1zz33qGLFiipUqJDKly8vSTp48GCmx7F//35dvHhRzZs3t+/5+PgoPDxcv/32W4pua9eubf8fFhYmSTp+/HimxwUAyBkcQQCA60RgYKAqV658Tf16eV3Z32OMse9dvHgxRTcBAQFuh9GlSxeVK1dOU6dOVcmSJZWYmKhbb71VCQkJ11RTRnx8fOz/XS6XJNnTmQAAzuEIAgBc56pXr64//vhDMTEx9r3169en6Cbp9J/k3Vx9O9LatWtr+fLlaY7j5MmT2r17t55//nm1bdtW1atXtxcvJ/H19ZUke81CWipVqiRfX1+tWbPGvnfx4kVFRUWpRo0abqYSAHC94AgCAFwn4uPjdfTo0RTveXt7q127dqpatap69+6t1157TbGxsXruuedSdFe5cmWVKVNGo0eP1tixY7Vnzx698cYbKboZPny4atWqpYcffliDBg2Sr6+vVqxYoZ49eyokJERFihTRBx98oLCwMB08eFDDhg1L0X+xYsUUEBCgRYsWqXTp0vL39091i9PAwEANHjxYTz/9tEJCQlS2bFlNmDBBf//9tx566CEPzi0AQE7hCAIAXCcWLVqksLCwFK+IiAh5eXnp66+/1oULFxQeHq5+/fpp7NixKfr18fHR3LlztWvXLtWuXVuvvvqqXn755RTdVK1aVUuWLNH27dsVHh6upk2b6ptvvpG3t7e8vLz06aefavPmzbr11ls1ZMgQvfbaayn69/b21jvvvKMpU6aoZMmS6tatW5rT8corr+iuu+7S/fffr/r162vfvn1avHixvWUrAOD65jLJT1gFAAAAkKdxBAEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFgEBAAAAAAWAQEAAACARUAAAAAAYBEQAAAAAFj/D5/1g4LUksHRAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "markdown",
+      "id": "fb85535d",
+      "metadata": {
+        "id": "fb85535d"
+      },
+      "source": [
+        "## Smoothing Splines and GAMs\n",
+        "A smoothing spline is a special case of a GAM with squared-error loss\n",
+        "and a single feature. To fit GAMs in `Python` we will use the\n",
+        "`pygam` package which can be installed via `pip install pygam`. The\n",
+        "estimator `LinearGAM()` uses squared-error loss.\n",
+        "The GAM is specified by associating each column\n",
+        "of a model matrix with a particular smoothing operation:\n",
+        "`s` for smoothing spline; `l` for linear, and `f` for factor or categorical variables.\n",
+        "The argument `0` passed to `s` below indicates that this smoother will\n",
+        "apply to the first column of a feature matrix. Below, we pass it a\n",
+        "matrix with a single column: `X_age`. The argument `lam` is the penalty parameter $\\lambda$ as discussed in Section~\\ref{Ch7:sec5.2}."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax = plot_gam(gam_logit_, 2)\n",
-    "ax.set_xlabel('Education')\n",
-    "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of high earner status on education', fontsize=20);\n",
-    "ax.set_xticklabels(Wage['education'].cat.categories[1:],\n",
-    "                   fontsize=8);\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "b86a257f",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.637945Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.637732Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.738536Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.737576Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
+      "cell_type": "code",
+      "execution_count": 28,
+      "id": "86ff9343",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.671284Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.671050Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.705757Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.705247Z"
+        },
+        "id": "86ff9343",
+        "outputId": "cc0afe0e-b97f-490e-f36a-7f221efb2bb5",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearGAM(callbacks=[Deviance(), Diffs()], fit_intercept=True, \n",
+              "   max_iter=100, scale=None, terms=s(0) + intercept, tol=0.0001, \n",
+              "   verbose=False)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ],
+      "source": [
+        "X_age = np.asarray(age).reshape((-1,1))\n",
+        "gam = LinearGAM(s_gam(0, lam=0.6))\n",
+        "gam.fit(X_age, y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "879ee931",
+      "metadata": {
+        "id": "879ee931"
+      },
+      "source": [
+        "The `pygam` library generally expects a matrix of features so we reshape `age` to be a matrix (a two-dimensional array) instead\n",
+        "of a vector (i.e. a one-dimensional array). The `-1` in the call to the `reshape()` method tells `numpy` to impute the\n",
+        "size of that dimension based on the remaining entries of the shape tuple.\n",
+        "\n",
+        "Let’s investigate how the fit changes with the smoothing parameter `lam`.\n",
+        "The function `np.logspace()` is similar to `np.linspace()` but spaces points\n",
+        "evenly on the log-scale. Below we vary `lam` from $10^{-2}$ to $10^6$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "44812968",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.708880Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.708673Z",
+          "iopub.status.idle": "2024-06-04T23:19:36.976882Z",
+          "shell.execute_reply": "2024-06-04T23:19:36.976289Z"
+        },
+        "id": "44812968",
+        "outputId": "5c1ad47b-d992-4f6b-f6b3-a2dfd2a1fde4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 796
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<>:11: SyntaxWarning: invalid escape sequence '\\l'\n",
+            "<>:11: SyntaxWarning: invalid escape sequence '\\l'\n",
+            "/tmp/ipython-input-1121239147.py:11: SyntaxWarning: invalid escape sequence '\\l'\n",
+            "  ax.legend(title='$\\lambda$');\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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CVn3o0CGsW7cu5rX169fj7rvvZj9v3boVL7zwArZt24Z58+Zh3759uOmmm2CxWHDVVVeNa5v33nvvmG2KAW9HRwesVmtMu4xGI5qamnDo0CF84QtfSHubUuVlUPyZz3wm5ucf/ehH+OUvf4nDhw+jpqYGv/nNb7Bjxw5cffXVAIDnnnsODQ0NOHz4MFauXIm//OUvOHHiBHbv3o2KigosW7YMjzzyCDZv3ozvf//70Gq1k7FbhJBxsNlsaGtrg91uRzgchlqthtlsRn19PSwWy2Q3jxBCcpbVakVFRUXMaxUVFXC5XPD5fFAqlXj00Uexe/durFq1CgAwZ84c7N+/H7/61a/GFRQn2qbVamXvi68lWmai5GVQHC0SieCll16Cx+PBqlWrcOTIEYRCoZhvGPX19aitrcWhQ4ewcuVKHDp0CEuWLIk54OvXr8cdd9yBjz76CJdcckncbQUCAQQCAfazy+WauB0jhKRks9nQ3NwMr9cLo9EIjUaDUCiEvr4+OJ1ONDU1UWBMCCHjdObMGXi9XnzqU5+KeT0YDMbESnq9nv07EokgEAjEvHbTTTdh27ZtE9/gDOVtUPzhhx9i1apV8Pv90Ov1ePnll7Fw4UK0trZCq9WOqT0/+ltIvG8g4nuJbN26FQ8//LC8O0IIGRdBENDW1gav1wuLxcKGS3AcB4vFwnqQzWYzDaUghJA4Kisrx8y56u/vh8FgQGFhIdxuNwBg586dmDFjRsxyHMexf7e2trJ/Nzc3Y/Pmzdi7dy97zWAwpNxmZWUle198raqqKmaZZcuWpb+TacjboHjBggVobW2F0+nEH//4R2zatAlvv/32hG5zy5YtMeNgXC4XZs6cOaHbJITE53Q6Ybfb444fVigUMBgMsNvtcDqdY74kE0KIZIWlF8b1Tub2J8iqVavGpEPbtWsXGyqxcOFCcByHrq6upEMl5s6dy/7d3d0NtVod89robb755psx45ajt1lXV4fKykq8+eabLAh2uVxobm7GHXfcMZ7dlCxvg2KtVssO+KWXXop3330XTz/9ND7/+c8jGAzC4XDE/CEc/S2kpaUlZn3itxZxmXg4jov5ZkQImTyBQADhcBgajSbu+1qtFiMjIzFDngghJG1K5YRNdJOb2+3GmTNn2M8dHR1obW1FaWkpamtrsWXLFvT09OD5558HANx+++34+c9/jvvvvx+33nor3nrrLfzhD3/Azp07AQDFxcX47ne/i3vuuQc8z2P16tVwOp04cOAADAYDNm3alHYbv/Od7+Cqq67Ck08+iY0bN+LFF1/Ee++9h2effRbAhU6Nu+++Gz/84Q8xb9481NXV4aGHHkJ1dTWuv/76zA9SEnmZki0enucRCARw6aWXQqPR4M0332Tvtbe3o6uri30LWbVqFT788EMMDAywZXbt2gWDwYCFCxdmve2EkMQEQYDD4UB/fz8cDgcEQQBw4UuqWq1GKBSK+3vBYBBqtZq+yBJCpo333nsPl1xyCRvve++99+KSSy7B9773PQBAX18furq62PJ1dXXYuXMndu3ahaVLl+LJJ5/Er3/9a6xfv54t88gjj+Chhx7C1q1b0dDQgGuvvRY7d+5EXV3duNp4+eWXY8eOHXj22WdZerdXXnkFixcvZsvcf//9+Na3voVvfOMbWLFiBdxuN15//XUUFBSMa5tSKQTxL0we2bJlCzZs2IDa2lqMjIxgx44deOyxx/DGG2/gU5/6FO644w689tpr2L59OwwGA771rW8BuJAwGrgwCHzZsmWorq7G448/DqvViq985Sv42te+llZKNpfLBaPRCKfTGTNehhAij2SZJcxmM/bv34++vr6YMcXAhUDaZrOhqqoKq1evpjHFhBDJ/H4/Ojo6UFdXN+FBGJFPsvMmNV7Ly+ETAwMDuPnmm9HX1wej0YiLL76YBcQA8NRTT0GpVOLGG29EIBDA+vXr8Ytf/IL9vkqlwquvvoo77rgDq1atQlFRETZt2oQf/OAHk7VLhJBRpGSWqK+vh9PphM1mg8FggFarRTAYhMvlgk6nQ319PQXEhBBCJMnLnuJcQT3FhEwMQRAk9wLb7XbKU0wIkQ31FOenadtTTAiZ2tLJLGGxWGA2m6miHSGEkIxQUEwIyTnpZpZQKBSUdo0QQkhGpkz2CULI1EGZJQghhGQbBcWEkJxjNBrZkIjR0x4EQYDL5YLZbIbRaJykFhJCCJlqKCgmhOQchUKB+vp66HQ62Gw2+P1+8DwPv98Pm81GmSUIIYTIjoJiQkhOslgsaGpqQlVVFXw+HwYHB+Hz+VBVVYWmpibKLEEIIURWNNGOEJKzKLMEIYSQbKGgmBCS0yizBCGEkGyg4ROEEEIIIVPAvn378JnPfAbV1dVQKBR45ZVXUv7O3r17sXz5cnAch7lz52L79u0T3s6XXnoJ9fX1KCgowJIlS/Daa6+x90KhEDZv3owlS5agqKgI1dXVuPnmm9Hb2zvh7aKgmBBCCCFkCvB4PFi6dCmeeeYZSct3dHRg48aNWLt2LVpbW3H33Xfja1/7Gt54441xt2Hv3r2YPXt2wvcPHjyIL37xi7jtttvw/vvv4/rrr8f111+P48ePAwC8Xi+OHj2Khx56CEePHsV///d/o729HX/zN38z7jZJRWWeM0BlngkhhJCpZXS5YF7g4Qg4Jq09Js4EpSL9PkyFQoGXX34Z119/fcJlNm/ejJ07d7KAFAC+8IUvwOFw4PXXXwcA8DyPxx57DM8++yysVivmz5+Phx56CJ/73OfirnPv3r245ZZb0NnZGff9z3/+8/B4PHj11VfZaytXrsSyZcuwbdu2uL/z7rvvorGxEefOnUNtbW3cZajMMyGEEELIBHIEHLjqP6+atO2//fm3UVpQOiHrPnToENatWxfz2vr163H33Xezn7du3YoXXngB27Ztw7x587Bv3z7cdNNNsFgsuOqq9I/LoUOHcO+9947ZZrKhHk6nMyvzSygoJoQQQgiZhqxWKyoqKmJeq6iogMvlgs/ng1KpxKOPPordu3dj1apVAIA5c+Zg//79+NWvfjWuoDjRNq1Wa9zl/X4/Nm/ejC9+8YsT/lSegmJCCCGEEDLGmTNn4PV68alPfSrm9WAwiEsuuYT9rNfr2b8jkQgCgUDMazfddFPCoRHJhEIh/P3f/z0EQcAvf/nLcexBeigoJoQQQgiZhiorK9Hf3x/zWn9/PwwGAwoLC+F2uwEAO3fuxIwZM2KW4ziO/bu1tZX9u7m5GZs3b8bevXvZa9E9vIm2WVlZGfOaGBCfO3cOb731VlbmblFQTAghhBCSgIkz4e3Pvz2p258oq1atikmHBgC7du1iQyUWLlwIjuPQ1dWVdKjE3Llz2b+7u7uhVqtjXhu9zTfffDNm3HL0NoG/BsSnT5/Gnj17UFZWNp7dSxsFxYQQQgghCSgVygmb6CY3t9uNM2fOsJ87OjrQ2tqK0tJS1NbWYsuWLejp6cHzzz8PALj99tvx85//HPfffz9uvfVWvPXWW/jDH/6AnTt3AgCKi4vx3e9+F/fccw94nsfq1avhdDpx4MABGAwGbNq0Ke02fuc738FVV12FJ598Ehs3bsSLL76I9957D88++yyACwHx5z73ORw9ehSvvvoqIpEIG29cWloKrVab6WFKiIJiQsi0IAgClYsmhExp7733HtauXct+FrM8bNq0Cdu3b0dfXx+6urrY+3V1ddi5cyfuuecePP3006ipqcGvf/1rrF+/ni3zyCOPwGKxYOvWrfj4449hMpmwfPlyPPDAA+Nq4+WXX44dO3bgwQcfxAMPPIB58+bhlVdeweLFiwEAPT09+N///V8AwLJly2J+d8+ePfjEJz4xru1KQXmKM0B5ignJDzabDW1tbbDb7QiHw1Cr1TCbzaivr4fFYpns5hFCckiyfLckd1GeYkIIScFms6G5uRlerxdGoxEajQahUAh9fX1wOp1oamqiwJgQQgiVeSaETF2CIKCtrQ1erxcWiwUcx0GpVILjOFgsFni9XrS1tYEemBFCCKGgmBAyZTmdTtjt9rjjhxUKBQwGA+x2O5xO5yS1kBBCSK6g4ROEkCkrEAggHA5Do9HEfV+r1WJkZASBQEDyOrM5YY/neXR3d8PtdkOv16OmpgZKZe73ZcjVbpocmRodI0LkQ0ExIWTK4jgOarUaoVAoJtG8KBgMQq1Wx30vnmxO2Gtvb0dLSwuGhoYQiUSgUqlQWlqKxsZGLFiwQNZtyUmudtPkyNToGBEiLwqKCSFTltFohNlsRl9fHywWS0wPmiAIcLlcqKqqgtFoTLmubE7Ya29vx+7duxEIBFBUVMS2ZbPZsHv3bgDIycBYrnbT5MjU6BgRIr/cfw5HCCHjpFAoUF9fD51OB5vNBr/fD57n4ff7YbPZoNPpUF9fn/JxczYn7PE8j5aWFgQCAZhMpphtmUwmBAIBtLS0gOf5jLclJ7naTZMjU6NjRMjEoKCYEDKlWSwWNDU1oaqqCj6fD4ODg/D5fKiqqpLcm5bNCXvd3d0YGhpCUVHRmHG4SqUSOp0OQ0ND6O7uznhbcpKr3TQ5MjU6RoRMDBo+QQiZ8iwWC8xm87gnJE3EhL1E3G43IpFI0m15vV643e6MtzVaJpO25Gp3No91vkr3GNFkPEKkoaCYEDItKBQKmEymcf2u3BP2ktHr9VCpVEm3pVKpoNfr01pvqsAo00lbcrU7m8c6X6VzjGgyHiHS0fAJQghJQZyw53Q6x4zTFCfsmc1mSRP2UqmpqUFpaSk8Hs+Y8bc8z8Pr9aK0tBQ1NTWS12mz2bB//37s2bMH+/btw549e7B//37YbDb2fnNzM/r6+qDT6VBWVgadToe+vj40Nzez5bLR7mwe63wl9RgFg8GMzyvJL/v27cNnPvMZVFdXQ6FQ4JVXXkn5O3v37sXy5cvBcRzmzp2L7du3T3g7X3rpJdTX16OgoABLlizBa6+9lnDZ22+/HQqFAj/96U8nvF0UFBNC0iIIAhwOB/r7++FwOCZ8Mg/P8+jq6sKJEyfQ1dUVd6KWlDZl0u7oCXsDAwNwOp0YGRmB0+nEwMDAmAl7mWxLqVSisbERHMdheHgYHo8Hfr8fHo8Hw8PD4DgOjY2NkvP+igFvb28vlEolCgoKoFQq0dvbi+bmZgwMDLBJW2azGYIgwOv1QhAEmM3mMZO2Eu2bXO1Od3LkRJ/7dMm5rUTrknKMFixYgPb2dpqMN814PB4sXboUzzzzjKTlOzo6sHHjRqxduxatra24++678bWvfQ1vvPHGuNuwd+9ezJ49O+H7Bw8exBe/+EXcdttteP/993H99dfj+uuvx/Hjx8cs+/LLL+Pw4cOorq4ed3vSQcMnCCGSZftRrJSct1LaJEe7LRYL5s2bh5aWFlit1pj2LFu2TNZtLViwAE6nE4cPH4bT6QTP81AqldDr9VixYoXkdGxiloLh4WHwPI+hoSG2Lp1Oh2AwiA8++AAjIyPQaDQ4f/48vF5vzDLRk7ZCoVDSfZOr3eLkSHFbIyMjUKvVqKqqSvu8ZvOalXNbqdaV6hhpNBrJk/HGO6xouhB4HhGHY9K2rzKZoJD4JXjDhg3YsGGD5HVv27YNdXV1ePLJJwEADQ0N2L9/P5566imsX78ewIWOicceewzPPvssrFYr5s+fj4ceegif+9zn0t8ZAE8//TSuvfZa3HfffQCARx55BLt27cLPf/5zbNu2jS3X09ODb33rW3jjjTewcePGcW0rXRQUE5LjpEySycZEmonIi5qs3dE5bzmOA8dxiEQiMTlvS0tLU7YJAFtGXI8gCGm322az4fTp0+A4DtXV1RAEAQqFAoIg4PTp0ygtLY3ZVibHyGazYWBgABUVFSgrK2MBuFqtxsDAAGw2G1tPsmPodDpZZTme58FxHFQqFSKRCNxuN5RKJXp6eiAIAoLBIMLh8Jhl/H4/ioqKYLVacebMmZTHemBgAJWVlezYiP8f3e5UUk2OlHI9pnM+Mr2H5Lw/pK4r2THq7+/P2QmL+TbxL+Jw4PTlV0za9ucdPAD1/32+yO3QoUNYt25dzGvr16/H3XffzX7eunUrXnjhBWzbtg3z5s3Dvn37cNNNN8FiseCqq64a1zbvvffeMduMHurB8zy+8pWv4L777sOiRYvS3sZ4UVBMSA7LlZ6w0XlRxT9g4qNYsQ1ms1nyH7dk7S4rK0NLSwt8Ph9UKhV7nK9QKKBWq+Hz+dDc3IxZs2YlbdPJkycBAMPDw4hEIhgcHGS9l4WFhQgEApLaHd3jGolE4PP54q5HHHqQyTFKta1gMMjWY7fbk557v9/PeomLiorYdtVqNVQqFTweD0ZGRtgj9OLi4jHLiO+fO3dO0rEevYy4T+O5RhJNjpRyPSZqT7zzkeo4piLn/ZHuuhIdo1ydsEgT/3KL1WpFRUVFzGsVFRVwuVzw+XxQKpV49NFHsXv3bqxatQoAMGfOHOzfvx+/+tWvxhUUJ9qm1WplPz/22GNQq9X49re/PY69Gj8KignJUXL3hGUinbyoUh7Fptq3mTNnwmazIRKJgOd5qNVq1uMYCoVYz6NCoUB5eXnCNvX19SEQCLDJX9G9oB6PB0qlEt3d3SnbnazHVVxPR0cHK1SRyTGSsq3u7m58/PHHOHnyZNJzHwwGEQqFUFBQELdN4hcMlUqVNFiLRCIYHh5GSUlJ0mMNIOP9l0LK9Si1PR0dHThx4kRG95Cc94dc65KzmqNcqApf/hGfDn3qU5+KeT0YDOKSSy5hP0dnlYlEIggEAjGv3XTTTTFDI5I5cuQInn76aRw9ejTrTxAoKCYkB8ndE5bpB4ucuWOl7Nvp06cRCASgVCqh0WjYMgqFAhqNBsFgEMFgEIFAIGmbAoEAHA4HlEplwp7S4eFh+P3+pG2W0uPqcDhgMpkyPkZStjU8PBwziSrRuZ87dy4LPLRa7ZjAKBwOQ6VSoaioCOFwmA0xEYNw8fG22OOYbN+CwSAAZOVxvZTrUUp7XC6XpOOY6h6S8/6Qa13iZDyn0wmbzQaDwcCOi8vlklzNUS4T8bSJZK6yshL9/f0xr/X398NgMKCwsJDlFd+5cydmzJgRs1z0U4bW1lb27+bmZmzevBl79+5lrxkMhpTbrKysBAC88847GBgYQG1tLXs/EongH//xH/HTn/4UnZ2d49pXKSgoJiQHydkTJkfPnJyPYqXsW29vL3iej9uDKT4yFgQBSqUyaZsUCgUikQg4jkvYUxoIBFgAlYjUHleFQpHxMZK6rcHBwaS95Ha7HbNmzUJJSQkcDkfcgFelUsFgMLCx1k6nE16vl30h0ev1MBgM7Pgk2zetVptyGbke10u5HqW0R+wxzfQekvP+kHNdUicsZoPcT5uySWUyYd7BA5O6/YmyatWqMenQdu3axYZKLFy4EBzHoaurK+lQiblz57J/d3d3Q61Wx7w2eptvvvlmzLjl6G1+5StfiTvO+Stf+Qq++tWvprV/6aKgmJAcJFdPmFw9c3I+ipWyb+KkMnHMYXQaL57nEQ6HodVqUVFRAafTmbBNZWVlGBkZSdpTqtVqWQCViFarTdnjWlBQgLKysqTtkXKMpGxLpVJBEISU516r1aKmpgbhcJiNTxYD3qKiIqhUKsyePRuCIMBqtWLmzJlswp1arYZWq4XdbkdVVRWAC2MBk+2blGXkeFwv9XpM1R6DwcAybyQ7jqnuITnvD7mHPWRazVEu+VypUKFUTthEN7m53W6cOXOG/dzR0YHW1laUlpaitrYWW7ZsQU9PD55//nkAF3IA//znP8f999+PW2+9FW+99Rb+8Ic/YOfOnQAuzDP47ne/i3vuuQc8z2P16tVwOp04cOAADAYDNm3alHYbv/Od7+Cqq67Ck08+iY0bN+LFF1/Ee++9h2effRYAUFZWhrKyspjf0Wg0qKyslJzBZrwoKCYkB8nVEyZXz5ycj2Kl7JsYYA4ODrIhEkqlEjzPIxQKQaVSwWKxoL6+HidPnkzYJjFFWLKeUpPJhIKCgqRtLigoSNnjajKZsGDBgqTtkXKMpGxL7N1Nde4LCgrYefN6vWxMsCAICAQCrE0A4HK5YLfbYTAYWLo2u90OnU6HhoYGtkyifZOyjFyP66Vcj1LaM3/+fBw7dizje0jO+2Mihj0kmoyXTbk68W+qee+997B27Vr2s5jlYdOmTdi+fTv6+vrQ1dXF3q+rq8POnTtxzz334Omnn0ZNTQ1+/etfs3RswIWUaRaLBVu3bsXHH38Mk8mE5cuX44EHHhhXGy+//HLs2LEDDz74IB544AHMmzcPr7zyChYvXjzOvZaPQqDM3ePmcrlgNBrhdDpjxssQkilBELB///6EvUU2m42Nv0rUE2az2VBVVYXVq1fL1iskx8zxdPbt9OnTGBkZgd/vZ9knCgoKUFxcjPnz52P16tVJMweYzWbs378fnZ2drKpadA5epVKJ2bNnpzxGYpulrEeOTAZStiX27ko593JlMcmVbCiiTNsjXh/JrsV07qFs5inON1Lue7k/r8bL7/ejo6MDdXV1Kb8wk9yR7LxJjdeop5iQHCRXT5jcE2nkeBQbvW8DAwOswppYkauoqChm3/R6PevdFP9fVFTE9i1Vm8RteTwemEymMdsaXR0t3nqi2+x2u9kwBrFnS6/XS26PiOd5lmVCr9ejpqYGSqUyZlvJ2iweH5vNxsZMj+4BFrcppU3ZXEaqVPlsLRYLysrK4h5Hqe1Jp1dWjvZIlSvDHuSSaxP/CImHeoozQD3FZKLlWs+cnOSqVieFXMfxrbfewrvvvotAIMCCdI7jsGLFClx99dVZ33cp68lX2bz287GXPF/lwzGinuL8JEdPMQXFGaCgmGRDrlS0k1N0vtJ4vZxyVhoTJVtPovypTqeTtaejowN79uxBMBiERqNh43zFCXFr165FY2NjynZEV+orKipi2/J4POA4DuvWrWMBrZQ2ezyeuL3t+ZzzVcr5AOLn6I5eJp39z/T6kLs9U1muf15RUJyfaPgEIdOAlEkyuTCRRqpE+UrF96RW7EpXovVIyZ964sQJtLa2IhQKoaioiD0O12g00Gq18Hq9OHz4MC699FKoVKqEbeB5Hi0tLQgEAmxYhLgtjUYDh8OBlpYWzJs3jw2lSNXm0WnZDAZDXud8lZrPVo7qgdEyuT4moj1TWT59XpHpJf2BToQQkoF08pXmSns+/vhjuN1uaLXaMeNDlUoltFot3G432trakm6ru7sbQ0NDMYF19Hp0Oh2GhobQ3d2dcZuzeQzlJDWPdV9fX1b2P9faQwiZOBQUE0KySkq+0nA4nLV8pVLaEwgEWLnpeNRqNXieh8vlSrott9uNSCSSdFuRSIRVkcqkzdk8hnKSmqNbHMaSaBm59j/X2kMImTgUFBNCsio6X2k82c5XKqU9HMdBqVQiHA7HXSYcDkOpVKacW6DX66FSqZJuS6VSQa/XZ9zmfM35KmXfxIIr2dj/XGsPIWTiUFBMCMkqsWKX0+nE6Hm+YsUus9ksS+UzudozZ84c6PV6BINB8DwfswzP8wgGgywtWzI1NTUoLS2Fx+OJux6v14vS0lLU1NRk3OZsHkM5Sdm36upqVFVVZWX/c609hJCJQxPtyJSR6zOaJ1KinLcTsR6px1lKzt9EeYrTzVeaybmXkj914cKFKCwsxJ49e+D1eqHValkZavGx+cqVK9kku0TtUSqVaGxsxO7du+FwOKDT6di2xEwcjY2NKc/dROV8zUamj1SkXh+A9BzdmWRwGX2sE+WETqc9mbZpIs7HdP78JEREQTGZEvIh9+VEkStXrZx5g1MtZ7FYMG/ePLS0tMBqtcZsb9myZbLnmE3FYrGgqamJrWdkZARqtRpVVVUxbQaAw4cPw+12IxgMsiETK1euZOnYUrVHPJbisfZ6vaxsdTrnTEqb05HNnL9S9k3K9SFl/+XILxzdnr6+vpj2LF26NK32yNUmOc/HdP78JCQa5SnOAOUpzg1ScohO1Q/2dHLeZrqe0tJSScc53ZyuqfIUJyP3uZfSWxaJRNDW1gaXywWDwYD6+nrWQ5xOe+Tq3Zejh0+u4zgR68kkj7Vc+YXFZaTkhE51PrKZ85jyK49PPucp3rdvH5544gkcOXIEfX19ePnll3H99dcn/Z29e/fi3nvvxUcffYSZM2fiwQcfxC233DKh7XzppZfw0EMPobOzE/PmzcNjjz2GT3/60zHLnDx5Eps3b8bbb7+NcDiMhQsX4r/+679QW1sbd52Up5hMe1JziE7F/KDp5rzNZD3Nzc2YNWtWyuNcVlY27pyuQPw8xYlMxLmXkj9VpVJh0aJFGbdHqVQm/HBPR6Y5X+U6jhO9HvE9qXms5cwvLC4jJSd0svORzZzHUrZ18uRJAJiWn59TlcfjwdKlS3HrrbfihhtuSLl8R0cHNm7ciNtvvx2///3v8eabb+JrX/saqqqqsH79+nG1Ye/evbjlllvQ2dkZ9/2DBw/ii1/8IrZu3YrrrrsOO3bswPXXX4+jR49i8eLFAICzZ89i9erVuO222/Dwww/DYDDgo48+mvAvKRQUk7yWTr7WqZYsPp2ct8mCLynrGRwchEKhGBMUALHHubu7W1JOVwAwmUwZnbNcO/e51h6p5Gp3Pq5HyrUo1/UqZ5vk2v++vj7Z9m0qE3gBfk/8zCLZUFCkgUIp7UvJhg0bsGHDBsnr3rZtG+rq6vDkk08CABoaGrB//3489dRTLCjmeR6PPfYYnn32WVitVsyfPx8PPfQQPve5z6W/MwCefvppXHvttbjvvvsAAI888gh27dqFn//859i2bRsA4J//+Z/x6U9/Go8//jj7vYsuumhc20sHBcUkr0nJIToyMjIl84NKyXnr9XpT5ryVsp6RkZGUeVhHRkbgdrsl5XQFkPE5y7Vzn2vtkUqudufjeqRci3Jdr3K2KZv7n4vXbLb5PSH89r79k7b9W59YjcJi7YSs+9ChQ1i3bl3Ma+vXr8fdd9/Nft66dSteeOEFbNu2DfPmzcO+fftw0003wWKx4KqrrhrXNu+9994x23zllVcAXAjCd+7cifvvvx/r16/H+++/j7q6OmzZsiXlUJBMUUo2ktemcr7WVOTKeStlPWq1WlIeVr1en7Wcrrl27nOtPVLJ1e58XI+Ua1HOHMTZzHlM+ZWJFFarFRUVFTGvVVRUwOVywefzIRAI4NFHH8Vvf/tbrF+/HnPmzMEtt9yCm266Cb/61a9k3abVagUADAwMwO1248c//jGuvfZa/OUvf8FnP/tZ3HDDDXj77bfHt6MSUVBM8tpUzteailw5b6Wsp6ysDLW1tSmPc01NTdZyuubauc+19kglV7vzcT1SrkU5cxBnM+exlG1VVVVRfmWS1JkzZ+D1evGpT30Ker2e/ff888/j7NmzbLno9zZs2ICurq6Y126//XbJ2xT/Dv3t3/4t7rnnHixbtgz/9E//hOuuu44Nr5goNHyC5LWJyteaD+TKeStlPU1NTSgtLcXIyEjS46xUKiWdDyC9nK7x5Nq5z7X2SCVXu/N1PUDqa1HKMlLOq5xtkmP/GxoaZNs3kp8qKyvR398f81p/fz8MBgMKCwvZ8LudO3dixowZMctFP0FobW1l/25ubsbmzZuxd+9e9lp0xodE26ysrAQAmM1mqNVqLFy4MGYZcbzzRKKUbBmglGy5Yzrn2czHPMXprCuVXDv3udYeqXLtfORanl45z2uu3R/5es1OlNGpvfJpol00hUKRMiXb5s2b8dprr+HDDz9kr33pS1/C0NAQXn/9dYyMjMBiseDf//3f8ZWvfEXSdlNln/j85z8Pr9eLP/3pT+y1yy+/HBdffDHrCb788stx0UUX4T/+4z/YMp/97GdRWFiIHTt2xF2vHCnZKCjOAAXFuWU6V2TKp4p241lXKrl27nOtPVLl2vnItYpucp7XXLs/8vWanQj5nKfY7XbjzJkzAIBLLrkEP/nJT7B27VqUlpaitrYWW7ZsQU9PD55//nkAF1KyLV68GHfeeSduvfVWvPXWW/j2t7+NnTt3suwTDz74ILZt24Ynn3wSq1evhtPpxIEDB2AwGLBp06YxbZCSku2qq67Cj3/8Y2zcuBEvvvgiHn300ZiUbC+//DI+//nP45lnnsHatWvx+uuv4+6778bevXuxevXquOuVIyiGQMbN6XQKAASn0znZTSGEEEKIDHw+n3DixAnB5/NNdlPStmfPHgHAmP82bdokCIIgbNq0SbjqqqvG/M6yZcsErVYrzJkzR3juuedi3ud5XvjpT38qLFiwQNBoNILFYhHWr18vvP322wnbMGvWrKTt/MMf/iDMnz9f0Gq1wqJFi4SdO3eOWeY3v/mNMHfuXKGgoEBYunSp8MorryRdZ7LzJjVeo57iDFBPMSGEEDK15HNP8XQmR08xZZ8ghBBCCCHTHgXFhBBCCCFk2qOgmBBCCCGETHsUFBNCCCGEkGmPgmJCCCGEEDLtUVBMCCGEEDIKJefKL3KcLwqKCSGEEEL+j0ajAQB4vd5JbglJh3i+xPM3Hmq5GkMIIYQQku9UKhVMJhMGBgYAADqdbtpW98sHgiDA6/ViYGAAJpMJKpVq3OuioJgQQgghJEplZSUAsMCY5D6TycTO23hRUEwIIWkQBAFOpxOBQAAcx8FoNFIvEkkbXUe5TaFQoKqqCuXl5QiFQpPdHJKCRqPJqIdYREExIYRIZLPZ0NbWBrvdjnA4DLVaDbPZjPr6elgslsluHskTdB3lD5VKJUuwRfIDBcWEECKBzWZDc3MzvF4vjEYjNBoNQqEQ+vr64HQ60dTURAENSYmuI0JyF2WfIISQFARBQFtbG7xeLywWCziOg1KpBMdxsFgs8Hq9aGtroxROJCm6jgjJbRQUE0JICk6nE3a7Pe64T4VCAYPBALvdDqfTOUktJPmAriNCchsFxYQQkkIgEEA4HE6Y/1Kr1SIcDiMQCGS5ZSSf0HVESG6joJgQQlLgOA5qtTrhLPRgMAi1Wg2O47LcMpJP6DoiJLdRUEwIISkYjUaYzWY4nc4x4z0FQYDL5YLZbIbRaJykFpJ8QNcRIbmNgmJCCElBoVCgvr4eOp0ONpsNfr8fPM/D7/fDZrNBp9Ohvr6e8sySpOg6IiS3KQSa5jpuLpcLRqMRTqcTBoNhsptDCJlglF+WyIGuI0KyS2q8RnmKCSFEIovFwh5/UyUyMl50HRGSmygoJoSQNCgUCphMpsluBslzdB0RkntoTDEhhBBCCJn28jIo3rp1K1asWIHi4mKUl5fj+uuvR3t7e8wyn/jEJ6BQKGL+u/3222OW6erqwsaNG6HT6VBeXo777rsP4XA4m7tCCCGEEEJyQF4On3j77bdx5513YsWKFQiHw3jggQdwzTXX4MSJEygqKmLLff3rX8cPfvAD9rNOp2P/jkQi2LhxIyorK3Hw4EH09fXh5ptvhkajwaOPPprV/SGEEEIIIZNrSmSfsNlsKC8vx9tvv401a9YAuNBTvGzZMvz0pz+N+zt//vOfcd1116G3txcVFRUAgG3btmHz5s2w2WzQarUpt0vZJwghhBBCcpvUeC0vh0+MJtaJLy0tjXn997//PcxmMxYvXowtW7bA6/Wy9w4dOoQlS5awgBgA1q9fD5fLhY8++ijudgKBAFwuV8x/hBBCCCEk/+Xl8IloPM/j7rvvxhVXXIHFixez17/0pS9h1qxZqK6uxrFjx7B582a0t7fjv//7vwEAVqs1JiAGwH62Wq1xt7V161Y8/PDDE7QnhBBCCCFksuR9UHznnXfi+PHj2L9/f8zr3/jGN9i/lyxZgqqqKnzyk5/E2bNncdFFF41rW1u2bMG9997Lfna5XJg5c+b4Gk4IIYQQQnJGXg+fuOuuu/Dqq69iz549qKmpSbpsU1MTAODMmTMAgMrKSvT398csI/5cWVkZdx0cx8FgMMT8RwghhBBC8l9eBsWCIOCuu+7Cyy+/jLfeegt1dXUpf6e1tRUAUFVVBQBYtWoVPvzwQwwMDLBldu3aBYPBgIULF05IuwkhhBBCSG7Ky+ETd955J3bs2IH/+Z//QXFxMRsDbDQaUVhYiLNnz2LHjh349Kc/jbKyMhw7dgz33HMP1qxZg4svvhgAcM0112DhwoX4yle+gscffxxWqxUPPvgg7rzzTnAcN5m7RwghhBBCsiwvU7Ilqg//3HPP4ZZbbsH58+dx00034fjx4/B4PJg5cyY++9nP4sEHH4wZ8nDu3Dnccccd2Lt3L4qKirBp0yb8+Mc/hlot7bsCpWQjhBBCCMltUuO1vAyKcwUFxYQQQgghuW1a5SkmhBBCCCEkExQUE0IIIYSQaY+CYkIIIYQQMu1RUEwIIYQQQqY9CooJIYQQQsi0R0ExIYQQQgiZ9igoJoQQQggh0x4FxYQQQgghZNqjoJgQQgghhEx7FBQTQgghhJBpj4JiQgghhBAy7VFQTAghhBBCpj0KigkhhBBCyLRHQTEhhBBCCJn2KCgmhBBCCCHTHgXFhBBCCCFk2qOgmBBCCCGETHsUFBNCCCGEkGmPgmJCCCGEEDLtUVBMCCGEEEKmPQqKCSGEEELItEdBMSGEEEIImfYoKCaEEEIIIdMeBcWEEEIIIWTao6CYEEIIIYRMexQUE0IIIYSQaY+CYkIIIYQQMu1RUEwIIYQQQqY9CooJIYQQQsi0R0ExIYQQQgiZ9tST3QBCCCFkqhAEAU6nE4FAABzHwWg0QqFQTHazCCESUFBMCCGEyMBms6GtrQ12ux3hcBhqtRpmsxn19fWwWCyT3TxCSAoUFBNCCCEZstlsaG5uhtfrhdFohEajQSgUQl9fH5xOJ5qamigwJiTH0ZhiQgghJAOCIKCtrQ1erxcWiwUcx0GpVILjOFgsFni9XrS1tUEQhMluKiEkCQqKCSGEkAw4nU7Y7fa444cVCgUMBgPsdjucTucktZAQIgUFxYQQQkgGAoEAwuEwNBpN3Pe1Wi3C4TACgUCWW0YISQcFxYQQQkgGOI6DWq1GKBSK+34wGIRarQbHcVluGSEkHRQUE0IIIRkwGo0wm81wOp1jxg0LggCXywWz2Qyj0ThJLSSESEFBMSGEEJIBhUKB+vp66HQ62Gw2+P1+8DwPv98Pm80GnU6H+vp6yldMSI6joJgQQgjJkMViQVNTE6qqquDz+TA4OAifz4eqqipKx0ZInqA8xYQQQogMLBYLG0ZBFe0IyT8UFBNCCCEyUSgUMJlMk90MQsg40PAJQgghhBAy7VFQTAghhBBCpj0KigkhhBBCyLRHQTEhhBBCCJn2KCgmhBBCCCHTHgXFhBBCCCFk2qOgmBBCCCGETHsUFBNCCCGEkGmPgmJCCCGEEDLtUVBMCCGEEEKmPQqKCSGEEELItEdBMSGEEEIImfYoKCaEEEIIIdMeBcWEEEIIIWTao6CYEEIIIYRMexQUE0IIIYSQaU892Q0ghBCSPwRBgNPpRCAQAMdxMBqNUCgUk90sQgjJGAXFhBBCJLHZbGhra4Pdbkc4HIZarYbZbEZ9fT0sFstkN48QQjJCQTEhhJCUbDYbmpub4fV6YTQaodFoEAqF0NfXB6fTiaamJgqMCSF5jcYUE0IISUoQBLS1tcHr9cJisYDjOCiVSnAcB4vFAq/Xi7a2NgiCMNlNJYSQcaOgmBBCSFJOpxN2uz3u+GGFQgGDwQC73Q6n0zlJLSSEkMxRUEwIISSpQCCAcDgMjUYT932tVotwOIxAIJDllhFCiHwoKCaEEJIUx3FQq9UIhUJx3w8Gg1Cr1eA4LsstI4QQ+VBQTAghJCmj0Qiz2Qyn0zlm3LAgCHC5XDCbzTAajZPUQkIIyRwFxYQQQpJSKBSor6+HTqeDzWaD3+8Hz/Pw+/2w2WzQ6XSor6+nfMWEkLxGQTEhhJCULBYLmpqaUFVVBZ/Ph8HBQfh8PlRVVVE6NkLIlEB5igkhhEhisVjYMAqqaEcImWooKCaEECKZQqGAyWSa7GYQQojsaPgEIYQQQgiZ9igoJoQQQggh0x4FxYQQQgghZNqjoJgQQgghhEx7FBQTQgghhJBpj4JiQgghhBAy7VFQTAghhBBCpj0KigkhhBBCyLRHQTEhhBBCCJn2KCgmhBBCCCHTHgXFhBBCCCFk2lNPdgMIySZBEOB0OhEIBMBxHIxGIxQKRd5vSy5S2ix1v6Qsx/M8uru74Xa7odfrUVNTA6Uyt7+ry9XmSCSCtrY2uFwuGAwG1NfXQ6VSTUCLL8jH61GKfL2nc+3an6rXByHpoKCYTBs2mw1tbW2w2+0Ih8NQq9Uwm82or6+HxWLJ223JRUqbpe6XlOXa29vR0tKCoaEhRCIRqFQqlJaWorGxEQsWLJiUY5CKXG1uaWnB4cOH4Xa7wfM8lEol3nzzTaxcuRKNjY2ytzsfr0cp8vWezrVrf6peH4SkK7e7ZBLYunUrVqxYgeLiYpSXl+P6669He3t7zDJ+vx933nknysrKoNfrceONN6K/vz9mma6uLmzcuBE6nQ7l5eW47777EA6Hs7krJEtsNhuam5vR19cHnU6HsrIy6HQ69PX1obm5GTabLS+3JRcpbZa6X1KWa29vx+7du2Gz2VBQUACTyYSCggLYbDbs3r17zP2cC+Rqc0tLC/bs2QOXywW1Wg2dTge1Wg2Xy4U9e/agpaVF1nbn4/UoRb7e07l27U/V64OQ8cjLoPjtt9/GnXfeicOHD2PXrl0IhUK45ppr4PF42DL33HMP/vSnP+Gll17C22+/jd7eXtxwww3s/Ugkgo0bNyIYDOLgwYP43e9+h+3bt+N73/veZOwSmUCCIKCtrQ1erxcWiwUcx0GpVILjOFgsFni9XrS1tUEQhLzallyktPnkyZM4efJkyv3ieV7SupqbmxEIBGAymWKWMZlMCAQCaGlpAc/zk31oGJ7n0dLSknGbI5EIDh8+jFAoBJ1OB61WC6VSCa1WC51Oh1AohMOHDyMSicjS7ny8HqXI13tarutILlP1+iBkvPIyKH799ddxyy23YNGiRVi6dCm2b9+Orq4uHDlyBADgdDrxm9/8Bj/5yU9w9dVX49JLL8Vzzz2HgwcP4vDhwwCAv/zlLzhx4gReeOEFLFu2DBs2bMAjjzyCZ555BsFgcDJ3j8jM6XTCbrfHHSOnUChgMBhgt9vhdDrzaltykdLmvr4+9PX1pdyv7u7ulOs6d+4cBgcHUVRUNGYMpVKphE6nw9DQELq7uwFc+MPtcDjQ398Ph8MR9w+0lGWkSLSe7u5uDA0NSW5zIm1tbXC73SwYHr0erVYLt9uNtra2cbV/tHy8HqXI13tarutILlP1+iBkvKbEmGLxhi0tLQUAHDlyBKFQCOvWrWPL1NfXo7a2FocOHcLKlStx6NAhLFmyBBUVFWyZ9evX44477sBHH32ESy65ZMx2AoEAAoEA+9nlck3ULhEZBQIBhMNhaDSauO9rtVqMjIzEnNt82JZcpLRZ/KKYar/cbrekdaVaxuv1wu12yzrOOZVk63G73YhEIpLanIzL5QLP81Cr43/0qtVqBINB2T5b8vF6lCJf72m5riO5TNXrg5Dxysue4mg8z+Puu+/GFVdcgcWLFwMArFYrtFotTCZTzLIVFRWwWq1smeiAWHxffC+erVu3wmg0sv9mzpwp896QicBxHNRqNUKhUNz3g8Eg1Go1OI7Lq23JRUqbtVottFptyv3S6/WS1pVqGZVKhXA4LNs451RSrSccDkOlUqVss16vT7odg8EApVKZcO5COByGUqmEwWCQ1O5U8vF6lCJf72m9Xi/LdSSXqXp9EDJeeR8U33nnnTh+/DhefPHFCd/Wli1b4HQ62X/nz5+f8G2SzBmNRpjNZjidzjGP1QVBgMvlgtlshtFozKttyUVKm6uqqlBVVZVyv2pqalKua9asWSgrK4PH4xkzdpLneXi9XpSUlGB4eFiWcc6phlJIGVfpcDhQUlKStM2lpaWoqalJuq36+nro9XoEg8G46wkGg9Dr9aivr0+6Hqny8XqUIl/v6ZqaGpSWlmZ8Hcllql4fhIxXXgfFd911F1599VXs2bMn5kOksrISwWAQDocjZvn+/n5UVlayZUZnoxB/FpcZjeM4GAyGmP9I7lMoFKivr4dOp4PNZoPf7wfP8/D7/bDZbNDpdKivr5clJ2c2txUtkzG1Utrc0NCAhoaGlPulVCrZugYGBuB0OjEyMgKn04mBgQG2rqamJnAcB4fDEbMuh8MBjuOwePFiDA0NyTLOOdV4SCnjKgcHB7F48WLWZo/HA7/fD4/Hw9rc2NiYMs+sSqXCypUrodFo2DpCoRBbl0ajwcqVK2XLVzxZ1+NEy9d7WqlUorGxMem1L+U6ijbR934+Xh+EjFdejikWBAHf+ta38PLLL2Pv3r2oq6uLef/SSy+FRqPBm2++iRtvvBHAhTQ4XV1dWLVqFQBg1apV+NGPfoSBgQGUl5cDAHbt2gWDwYCFCxdmd4fIhLNYLGhqamJjRkdGRqBWq1FVVSV7Ls5sbguQZ0yt1DZLWcZisWDevHloaWmB1WqNycO6bNkyWCwWtqyYq9Xr9UKlUsFisaCxsREmkwlnz56VZZxzqvGQUsdVzpgxAytWrMDhw4fhdDpZfmG9Xo8VK1ZIzi/b2NgIt9uNd999F4FAAIIgQKFQgOM4rFixQvY8xdm+HrMlnf3KtDCFnMdQvE4SXfvp5CnO5r1PyHSgEPIw18o//MM/YMeOHfif//mfmA8Qo9GIwsJCAMAdd9yB1157Ddu3b4fBYMC3vvUtAMDBgwcBXEiNtGzZMlRXV+Pxxx+H1WrFV77yFXzta1/Do48+KqkdLpcLRqMRTqeTeo3zRL5Wv0pEHAvr9XphNBqh0WgQCoXgdDqh0+nQ1NSU1h81OSraRbeJ4zgoFAoIgoBAIDCmTYmqejkcDuzZswc6nS7ueEa/3896gMXUVvGW8fl8WLt27Zj5BdGkbMvn82Hp0qU4ceIEPB4P2yfx/0VFRZKPtXh8PB4PwuEw+9KgVqvTWk+6pmrFMinXo1yFKXKpot1k3PuE5Cup8Vpe9hT/8pe/BAB84hOfiHn9ueeewy233AIAeOqpp6BUKnHjjTciEAhg/fr1+MUvfsGWValUePXVV3HHHXdg1apVKCoqwqZNm/CDH/wgW7tBJoFCoUgaIOXTtkaPhRX/gIljYcVgwGw2S/7jJqXNyZZJ1CbxvdFtUiqVqK2tHbMecaxjX19f3PWI45yBCxNjky2TajyklG1VVlaiu7sbXq8X5eXlKfcrkejjk8l6xiOb1342JduvRIFjX18fnE5n2oGjnMcw0bUvxWTd+4RMdXkZFEvp3C4oKMAzzzyDZ555JuEys2bNwmuvvSZn0wjJmnRyjGbrj51cbRLHOjqdTthsNhgMBjZkwuVysbHJwIUeAJvNFrdXWsp4SCnbmjFjBo4dO5bxfuXiOZuqJiJwzBV0HREyMfJ6oh0h05mUsbDhcDitHKOZFsGQs03iWMeqqir4fD4MDg7C5/OhqqqK9fCJ45cDgQA6Oztx5swZdHZ2IhAIYN68eWmPq0y0Lb1eL8t+TcQ5m8oyuR6ncmEKuo4ImRh52VNMCInNMRpvLGy6OUblGHspd5ssFgtLGRVvrKPNZsPp06eh1Woxe/ZsKJVKNnv+9OnTKC0tTSswTrQth8Mhy37JfXymskyvx6lcmIKuI0ImBvUUE5Kn5MwxKlcRjInIeyqOdayoqIDJZGIB8ejxuUajEcXFxTAajSgvL5ecp1jKtuTaL8oLK40c1+NULkxB1xEhE4OCYkLylFw5RqUUr5AaXGYz72k2H4/LtV+UFzY1ua7HqRw40nVEyMSg4ROE5DE5cozKPWknW3lPs/14XNyvkydPoq+vj5Wsrq6unjJ5YeVKy5VJar9sTtbM58Axl68jQvIVBcWE5LlU425TmYjgMtM2SZEr4yrHk+o9G8cnXXLl85WynmTL8Dwv2/U41QPHXLyOCMlnFBQTMgVkkmN0ooLLic57KjWXsVyPx6Nz3ppMJpbz1mq1wuVyTUjO22wVVJArn6+U9QBIuszChQuzOlkz31F+YULkQ0ExIdNctoNLuWTz8fhk5LyVsxJbMnLtm5T1nDx5EgCSLtPT04OysrKMi7JEo8CRECIFBcWETHMTFVxmo5czW4/Hs10sQe5KbMnItW/R6wEulMcWg3mO42AwGNDX1wcAMZk94m3r4osvZkVZptpYYBGVVZ6a6LzmNwqKCSGyB5fZ6uUU2z7Rj8ezOakv273Scu2buB5xSInX6wXP81AqlSytWjAYBICU29Lr9VN6LHA27w+SPXRe8x8FxYQQAPIFl9ns5RRN9OPxbE7qy3avtFz7xnEcwuEwzp8/D57nwXEcVCoVIpEI3G43vF4vioqKwHGcpG2ZTKYpORZ4Mu4PMvHovE4NlKeYEMIkKl4hlZw5j3NJNnPeZruEr1z7ZjAYEA6H4fP5UFhYCLVaDYVCAbVajcLCQvh8PgBAZWWl5G1lej3mmql6f0x3dF6nDgqKCSGyyWZBjWzKZrGEbFdik2vfXC5XTAAcDochCEJMoKzRaFBTUzNti05M1ftjuqPzOnVQUEzIBBEEAQ6HA/39/XA4HNOilyDdXs5sHiMp20q2jDjuuqqqCj6fD4ODg/D5fKiqqpL10ehkVGJLZ98SHaNAIAC1Wo2amhro9XqEQiF4vV6EQiHo9XrU1NRArVaz8cITfRxzUbafApDsoPM6ddCYYkImQLYnXOTKjOd0xqdm8xhlWlBCXCYbk/omqxKblH1LdozEc6/ValFbW8sCBfF8iz9P5fHCqeRKwRkiLzqvUwcFxYTILNsTLnJpxrPUnMfBYBAtLS1ZOUZyFJSIbk82ct5OViW2ZPuW6jg2NjbGnPuCggL2u/HyC0/H3MH5mhOcJEfndeqQNSg+ffo0nn/+eRw6dAhWqxU+nw9vvPEG5s6dy5Y5fvw4urq6UFRUhKuuukrOzRMy6bKdTivXZjxL6eVcsGAB2tvbs3KMpJ4PQRCyWphDCovFgrKyMnR3d8PtdrMhCEpl9ke9STmO7e3tWLBgQdZ7uPPJZD0FIBOLzuvUIUtQzPM87r//fjz99NPgeZ6NMVMoFCwvpairqwvXXXcd1Go1Ojo6MGPGDDmaQEhOyGY6rcmosiZFql5OjUaTtWMk5Xz09vYCSF1QQq4UaFLFewJw7ty5MT3F2Rg6I/W6XrJkyZTOLyyHyXoKQCYWndepQZag+Jvf/CZ++9vfQhAEzJgxA6tWrcIf//jHuMt++tOfRl1dHTo7O/HHP/4R3/nOd+RoAiE5IZtFHrKdzzYdycan9vf3Z+0YSTkfUgtKRE8OnOggVOoTgGwNnUnnuq6oqJiW44XTkY2x6ST76Lzmv4yD4jfffBO/+c1voFAo8MADD+Dhhx+GSqVK+ojv7/7u7/D444/jrbfeoqCYTCnZnHCRzQB8PBKNGc3mMZKyLa1WCwA5MzkwnSEf2RqXne45m47jhdNFx2hqovOa3zIenPbss88CuNAD/MMf/hAqlSrl7zQ2NgIAPvroo0w3T0hOyWY6rWzns5VLNo+RlG1VV1ejqqoqZXuCwSCam5vR19fHyhbrdDr09fWhubkZNpst4/YC0p4A2Gw2tLa2Zq1YwGSkiSOEkGzLOCg+dOgQFAoFbrvtNsm/U1NTAwCwWq2Zbp6QnJLNIg/5Gqhk8xhJ3VZDQ0PSZUZPDpzIIFTKEwCfz5fVYgHZPGeEEDJZMh4+MTAwAACYPXu25N8RP+zD4XCmmydkUiQbVypOuDh58iT6+vrYI/qqqio0NDTI9kg712c8SzlGck1KkWNbyc5ZupMDMxl3LGWogkKhgCAI0Gg0EARhTE7giRg6Ix7HEydOoKurix2j2tpaLFy4kCYSpSlXcosTQv4q46C4qKgIDocjrUeH3d3dAIDS0tJMN09I1uVSXuBcnfGczUIY2dhWOuO3M70+pOQ8tVgsGBkZgcvlgtPphNfrBc/zUCqV0Ol0MBgMEzJ0ZmhoCOfOncPg4CDbN0EQUFlZSUFxGnLpM4QQ8lcZB8Vz5szB0aNHceLECXzqU5+S9Dt//vOfAQCLFi3KdPOEZFW6hSBMJhNbxmq1wuVyyZ47ONdmPEcfI47jwHEcBEGYtMIcUopuRK8n3jlraGiQNNHM4/HgxIkTGU1+k/IEYOnSpTh69Cja29tZ8KtSqRCJROB2u+FwOLBgwQJZh860t7dj9+7dCAQCKCoqYvtmt9uxe/duAMCCBQtk295UlWu5xQkhf5VxUHzNNdfgyJEjeOaZZ/Ctb30rZWL5EydOYPv27VAoFPj0pz+d6eYJyRopWQFOnjwJAFnPHZwrM57FYzQ8PIxIJILBwUHWg1lYWIhAIMD23263Z9Rblm6e5kSPq6Wsp6enB2VlZbBarQl7bysrK9Hd3S3LuU/1BMBsNsdsf/RxkRvP82hpaUEgEIDJZGKf8xzHQaPRwOFwoKWlBfPmzZuU4iL5IldzixNCLsg4KP72t7+Nn/3sZzh79ixuv/12/OIXv4BaHX+1u3btwle/+lX4/X6UlZXh61//eqabJyRrpGQF6OvrA5B7hSCyxel0sgpsPM/H9GB6PB4olUp0d3fj448/xsmTJ+HxeFBQUICCggLwPI/e3l7JvWXp5GkOhUIJA3Ap44UHBwexdOlSuFyuhL23M2bMwLFjx2TLG53sCYDD4YDP50NtbS1cLhe8Xi8CgQCUSiWKi4thMBjg8/lku866u7sxNDSEoqKiMUGvOGRjaGgI3d3dqK2tzXh7oqk27jaXc4sTQmQIiisqKrBt2zbcfPPN+M1vfoM33ngDGzduZO8//fTTEAQBBw4cYLOzlUoltm/fDr1en+nmCcmaiSgEkY+SBSp+vx/Dw8PgeR5FRUXsdbVaDZVKBY/Hg+HhYbS3t7PlhoaGYsbDBoNBSb1lUsf5Wq1WnDlzJuHj6rlz50paT1FRUdLeW57nZc8bnegJgLjvZWVlMJlMYybaCYKAwcFB2a4zt9uNSCSSdN+8Xi/cbrcs2wOm5rjbXM8tTsh0J0tFuy9/+cvQaDT45je/ifPnz+NXv/oV+2P261//GsBfH+np9Xr87ne/iwmcCckliYI+uQtB5KNUgUowGEQoFEJBQUHcnjC1Wg2fz8cyPIzuTXa73aw3OVVvmZTzoVKp0NXVlfRx9fnz56FSqSSdM5PJlLT3drKKkhQUFMS8HwgEZL3O9Hp9ymOkUqlYR0emPbxTddxtNgvXEELSJ0tQDAB///d/j09+8pP4xS9+gT/96U9obW2NSbm2aNEi/M3f/A2+853voLy8XK7NEiKrZEGf2WxOmRWgqqoKAJKOPa2qqsq53MFSSAlUtFote12r1Y7Z/3A4DKVSCbfbDZVKlbA32eFwwO/3J22PlCwNJSUlGBkZSfq4emRkBHq9Hg6HQ9I5S9R7K6U9cp37bG4LuJBbvrS0FDabDRqNJmYIBc/z7EtHTU1Nxj28U3ncbbbPGyEkPbLOiCgrK8NDDz2ElpYW+P1+DAwMoK+vD4FAAB9++CF+9KMfUUBMcpYY9CWqWGa321MWMGhoaEhZCCIfixyMDlQSFa/gOA4lJSVQqVTwer0Ih8MsGPZ6vaw3ked5aDSahL3JwWCQDUVJREpBiZkzZ6Z87B+JRFBbW5vxOcvFoiRyXWdKpRKNjY3gOI59YRG353A4wHEcGhsbMTg4mHHVv3TG3eYbKoJCSG6Trad4NKVSGTNDmpBcJrV3avXq1ZILQUjJHczzPJuYptfrUVNTk5Oz96MDFeDC2OHoMaxioAJc6FUMh8MIh8NwuVyIRCJQqVQsd25ZWRkCgQBCoRA0Gg18Ph9bV2FhIRtzKQ5FSSa6UEpXVxd7XF9bW8uKbpw8eZL1XI8eeys+rq6srERZWZmkwhTJzlk6hVsyPfeJimnMmjUrbpEYKdtLtoyYbk0Mbp1OJ9RqNWvH/PnzsX//fni9XlYW2+v1sp5iMduI2MObaFvR4255nsfIyAjbt+Li4jHjbrM5GU+O+zWbhWsIoesjPRMWFBOST9LpnZKSF1jKMu3t7WhpacHQ0BALHEtLS9HY2Jhz+V7FQCUYDMJqtY4pFlFaWsrer6+vx7lz52C32xEKhSAIAhQKBYLBIKqqqrBgwQI4nU709vayyXYicX3V1dVjxskmMjQ0hM7OTtjtdnYceZ5HRUUF5s+fD7PZjM7OTvaYP7rdSqUSs2fPhtFoxKlTp1IWppDrnMm1nkTFNCoqKmKCKynbk9omQRDYHJHof4v3kEajwfnz5+MWFBHvof7+/oTbqqiogFqtRn9/PwYGBuDz+dg1VFhYiPLychQUFIDjuKxOxpPzfs1m4RoyfdH1kT4KiglB+rPCpeQFTrZMokIINpstJwshcByHcDiM7u7uuJPjxKIXHMehv78fQ0NDCIfDUKlUbB3hcBhDQ0MIhUIAAI/HwwIqMV8wz/PweDwAIGlcpZSCEuXl5Th27BhbhuM4hEIhDA4OguM4lJeX49SpUynXAyDlOSstLU1ZuGVoaEiWcy+1mIaUa03cN7/fzwLOSCSCgYGBuPtfWFjIzv/g4CB2796NFStWwOPxwOPxIBwOj7lG/H4/ioqKcOrUKbz77rsJ27Nu3ToAQGdnJwRBgFarhVKpZNdGZ2cn6uvrEQwG0dLSkpXJeBNxv2aaW3yqTkYk8qDrY3xkqWiXLoVCgYKCAhiNRsybNw8rV67E5z//eSr7TCZNNmeF52MhBIPBgHA4DJ/PF9NmtVoNpVIJh8MBvV4PvV6PP/3pTwiHw6ioqADP86y3UFyupaWFjS1VqVRQKpWsJ1Bc3mq1gud5qFSqhI//pBzH5uZmzJ49GwaDAZFIBD6fDz6fD0qlEqWlpVCpVOjv70dnZ2fS9Rw+fBgKhSLlOautrU1Z3CXVtqSce6nX0EUXXZRyOXHfvF4vNBoNvF4vOx/iz+IyPp+PjRcXlxEzihw/fpwNqykuLh4zgXJkZAQ8z+P48eMpz1kwGIQgCOz6UCgUUCqVMYF4tibj5eL9OpUnI5LM0fUxfhkHxZ2dnTE/iz0+8cR7r7m5GS+88AL+8R//EQ899BC2bNmSaZMISVs2Z4VPViGETLhcLjbm1+fzxfQCij2HarUa7e3tMfs2ev90Oh36+/sRCATYmOHo4RNi4R+fz4e2tjaUl5cnfPzn8/lSHke73Q6lUony8vK4Y4oDgQC6urpgt9uTrsdms0GhUECv1yfdFnChZzrREBwp25Jy7qVeQ0ePHpV0jHiehyAI7Muf+FktTna0Wq2st5bn+ZhlxCEyQ0NDYzKOjBYKheDz+VIea+DCPRkKhWKG4IhfXl0uF7q6usbcq9HHWq4iGLl4v1IREJIMXR/jl3FQvGnTJgDAsWPH8P7770MQBJSVlWHZsmWsa95ms6G1tRWDg4NQKBRYtmwZFi9eDJfLhePHj+Ps2bPw+/148MEH0dfXh5/97GeZNouQtIizwp1OZ8KKZXLNCp+MQgiZEvPezpw5E4ODgzEV1PR6PcrKyuDz+djEulTZHnieZwGUOC5VoVCwn71eL3p7e9HR0ZHw8Z/ZbE65LXHIi5jpYvQ4ZTFQltJmIHVRlmAwmHQZKduScu6jryFBENgxFXtTxfU4HI6U23M4HAiHwyydnniNiz3FoVCIZRHRaDRxlxGzhej1etbrPPqLk1hURDwfidojZpUoLCyETqcbs2+CIMDn86Vcj1xFMHLxfqUiICQZuj7GL+NnPc899xzWrl2Ljz76CHPmzMH//M//oL+/H7t27cKOHTuwY8cO7Nq1C/39/XjllVcwe/ZsfPTRR7jqqqvw8ssv4/Tp02hubsbSpUshCAKeeeYZHD58WI59IyQt4qzwqqoq+Hw+DA4OwufzoaqqStbxV9GFEOIZXQghF4g9dBqNBrW1tairq8OsWbNQV1eH2tpaqNVqqNVqGAwGSfumVCoRDodjHouLj8nF151OZ9IUcDabTdK2xDHEiZYRg7dU6xGH1yRaRq1WQ6vVyrKtVOdevIa8Xi+cTieGh4fhcDgwPDzMjptKpYLJZJK0PVG8XiXgr7354heX0cuIX2a0Wi0qKiqg1+sRCoXg9XoRCoWg1+tRXl6OoqIiScdRXEYcnqHValnvtNTzGj3cSRAEOBwO9Pf3w+FwJHyaGU8u3q/Rw70StYmKgExfdH2MX8Y9xe+//z6+/vWvo7KyEocPH06Yhk2pVOJv/uZvsGrVKlx66aW44447cPHFF+Oyyy7DihUrsHv3blx88cWwWq149tlnsXLlykybRkja5JoVnkw6hRByxejhJdE9rtHDS+rr63H06NGk+1ZRUYGhoSG43W42Jjl6mWAwyB5VR49NFYmP/zweD4qLi+FwOBJuy2w2o7a2NmkxldraWvA8zzInJDofCoUi5TK1tbXo7+/PeFupzn1NTQ30ej36+/uhUqlY763YEyse5+XLl6O9vT3p+TCZTHA4HCxFXryCK2KPkzj0ZPR6xIl1FRUV8Hg8mDlzJoLBIFteq9XCbrejtrYWgiCk3H8ASZeRcl7F4U6ZzsDPxfuVioCQZOj6GL+Me4p/8pOfIBwOY8uWLZLyElssFmzZsgWhUAg/+clP2OtlZWW44447IAgC9u/fn2mzCBk3cVZ4RUUFTCaT7BMRpBZCyJVJdoD0ogMqlSrlvjU1NeHyyy9nk7jEcs9iXluNRoOLL7445SNrnuexePHilNtKVUyloaEBTU1NSdezcuXKlMs0NjZi4cKFGW9LyrlXKBQoLS2NmZwoZu/geZ69L+V8LF++HBaLBSqVik2UE4Nhv98PpVLJviyqVCr2aFYMhgOBAFQqFQs0o8dX63Q6ABcCXJ1Oh4ULF6bc/6amJknLSCmSY7fbMy4mkov3KxUBIcnQ9TF+GfcU79u3DwBw2WWXSf6dFStWAMCY4Hf16tUALkzqINPDdE0sLqZvEvOeio+7LRZLTuYpBqQXHUhn3w4fPgy3241gMAilUgmDwYCVK1di/vz52LNnT8psIPPnz4fRaEy5rVTtFtsupc3Z3FYi4rjburo69Pf3x+Ty1el0rHKo0+lMeT7mz58Pr9eLSCSCkZER+P1+NnShsLAQxcXFmD9/PgRBwOnTp+F2u8dsT6/X46KLLsKcOXNgMBhy4libzWZWTCTTGfi5eL/KXQSETC10fYyPQkhncFUchYWFCAaD2LdvH6644gpJv3PgwAFceeWV4DgOPp+Pvf7BBx/gkksuQUFBAbxebybNygqXywWj0Qin0wmDwTDZzck7lFgciEQiaGtrg8vlgsFgYL2tuUzqFxkp1b/C4TCOHj0Kh8MBk8mE5cuXswIU+/fvT/j4z2azoaqqCqtXr4ZCoZB0HKW0W0qbs7mtRPr7+7Fv3z6UlZVBEAQMDg6ybZWVlUGhUGBwcBBr1qxBRUVFyu1F5zQVe5/FVHk6nQ5NTU0ALmQLcrvdY4YQ6PX6mHH3cu2/lGUSbcvhcGDPnj3Q6XQJM4/4fD6sXbtW8gx8OStQytUhkGvrIbmFzusFUuO1jHuKzWYzent7sWvXLslB8RtvvMF+N5rY+zFdAqLpjBKLx/9SMDQ0lPNfCqQWHVAqlUlTVI3ef4/Hg0AgwPZfajYQqcdRSrvTbfNEbisZcSKN0+mEy+WKqR7n8/lYSe3oXvZk2xvdq5ToS6qUZeTcfynLJNqWGASLxVNGV9grKytjwz+kyuScRZOzQyDTIiByt4fkFjmuj+kk46B47dq1eOGFF/Dkk0/iuuuuSzmM4t1338VPfvITKBQKrF27Nua9Y8eOAQCqqqoybRbJYZRYnL4USN3/VI//5D6OyXpV0tnWRPfOGI1GFBYWor29HSqVCgUFBSz92cjICBwOBxYsWJDWRBq5ypfnCrEK4/nz51NWYcymybj35bquCZnqMg6KN2/ejP/8z/+E1+vFmjVr8O1vfxs33XQTFi1axG46QRDw0Ucf4T/+4z/wb//2b/D7/dBqtbj//vtj1vXyyy9DoVBgzZo1mTaL5LDpnlh8un8pSGf/kwVhch/HZL1lZrNZ8rbsdntWe90SpVEb77oyKV8+GRIFfFKrMGZz6Ntk3PtyXddT8bOIkNEyDooXLVqE3/zmN/jqV78Kv9+PJ554Ak888QQ4jmNlm4eGhtgjKkEQoFQq8e///u9YvHgxW8/Zs2fx8ccfo7a2Fp/5zGcybRbJYdM9sfh0/1KQ7v4nCsLkPI6pessaGhokbaujowMnTpxgxSvEghVy97o5nU74fD7MmjWL5SWOLqZiMBjg8/mm7DUkShbwaTQaSVUYXS5X1o5Rtu998br2eDwoKChAQUEBeJ5Hb29vWtf1VL+OCBFlHBQDwE033YQ5c+bgzjvvxAcffAAA8Pv96O3tHbPsxRdfjGeeeWbM+OOLLroIHR0dcjSH5LjoxOLJMgtM1cTik/WlINsTLhJtT679l2s9UnrvTp06xfL4JtqWy+VCe3s7hoeHEYlEMDg4yMawFhYWIhAIpN3rluoYlpWVwWg0jplEFj35bqpK9UVm7ty5UKvVqKmpYRkjor84lJaWwu/3Z/UYZfPeF6/r4eFh8DyPoaGhmDHVwWBQ0nU9lTsoCBlNlqAYAC6//HK8//77aGlpwe7du3H8+HEMDw8DAEpKSrBo0SJ88pOfpKIcZNonFp+MLwXZnkiTbHty7b9c65HSeydOAk62LTErhsfjGTOG1ePxQKlUoru7W3KvWzrHcHT5arEs91T9Yinli0xXVxcreV1bWxs3+4RYdCRbsnnvO51Olikj3phqMbuIWBlwOnZQEDKabEGxqLGxEY2NjXKvlkwhYmJxKZkFpqKJ+FKQSxNpUm2vsbFRlv2X6zhK6b1TKBQoLi6G0+lMuC2DwYCOjg7wPI+ioiK2jFqthkqlgsfjwfDwMPx+f84cw3wl5YuMmDbN4XAkrcKYzWOUzQ4Bv9/PeokTXY9ut5uN2Z+O1xEho8keFBMixXROLC73l4JcmkgjpQevvb0dCxYsyHj/5TqOUnrvNBoNFixYgBMnTmBgYAAFBQVQKpWsSlRRURFmzJiBU6dOoaCgIG6gplarEQgEEAwGc+YY5iupwxBqa2sRDAZz5hhls0MgGAwiFAqlvB5ramrQ1dWVM8eIkMlEQTGZNPmU3klucn0pSNWjuHDhwqxOpJE6kWjJkiWy7L94HE+ePIm+vj4Eg0FotVpUVVWhoaFB0nqk9t7V1dUhFAqhpaUFVqsVkUgEKpUKpaWlWLZsWczxF3uXo9cTDoeh1Wqh1Wpz6hjmI6nDECorK1FWVpZTxyhbHQJarTbl9ajRaFBeXo6qqqqcOkaETJYJCYo7Oztht9tZKdBkKP3a9JZr6Z2yyWKxoKysbNwVsqT2KIoTaQRBGDOuUu6JNOlMJKqoqJC0/1ImCAqCwCZShcPhlJ870aJ77xL1AtfX18Nut+P06dPgOA6zZ89maeECgQBOnz6NhQsXoqSkBA6HI2bMplghTq1Ww2Qyscf4UiYi8jyPkZERFuwXFxePOYZyfrHMl+po0V9kzGYzgsEgu67FSY/iY3+FQpHRfTYRstEhUFBQwK5HMRtKdPYNlUrFrkeTyTRtOygIiSZbUNze3o5HH30U//u//wuXyyXpdxQKBcLhsFxNICSvxBv2cO7cOcm9M+lMEItX+Uyn08WtfJaJdCYSSdn/VBMEbTYb/vKXv6C3t5cFwwqFAna7Hd3d3bjmmmskHUuLxYJ58+Yl7AU2m83Yv3//mC8gwF/LTvf09GDGjBlwOp3weDwIhUKsPRqNBiaTCTU1NTAajZIm0fX392NgYIB1LigUChQWFqK8vBwFBQXs+Mr1xVKuyZjZmNQpfpHp7+9HW1sbeJ5n7ymVSpSXlyeteJjOfTZRJrpDwGg0oqamBuFwGJFIBD6fj2XfKCoqgkqlYtdjNtpDSD6QJSh+5ZVX8OUvfxl+vz+tHhpCpis5Jr9J6ZVVKpVQqVRsJr4clc+SkToUIRgMoqWlJen+A0g52ezAgQPo6uqCIAhsf3meRzAYRFdXF/bv34/rr78+ZY+XzWZL2gusVqslDWkoLS2F0+lkQyXE9oRCITidTnAcB7vdnnK/gAtP3Ebvl8fjQWdnJ+rr62Wd/BR9PWaSXznbkzrFvzfxnhxMRntySfQTEK/Xi5KSkpjrmsYLEzJWxkHx+fPncdNNN8Hn82HGjBm47777oNPp8I1vfAMKhQK7d+/G0NAQ3nvvPfzHf/wHent7sXr1anz/+9+HSqWSYx8IyStyVbWS0iurUqnYfSZn5bNEpEwkWrBgAdrb2+H1etmjb6/Xy3oU7XY7Tp48CQBJj1FrayvOnDkDQRDYkAfgQk+hUqmE3+/H2bNnMTw8zAoJxZPofIjvSc1T7HQ6cebMGZYGTOy5FnvmIpEI2tvb4fP5Up77oaEhCIIAlUoFpVIJhULBvuBEIhH2vpyTIzPNr5zNam3itgRBQH19/ZjhE1KvoVyv1pbpMJTR45fFY0TjhQmJL+Og+Gc/+xm8Xi+Ki4vR3NyM6upqfPTRR+z9tWvXAgBuvPFGfO9738Ntt92G//zP/8RvfvMb/P73v89084TkHbmqWknplS0pKcHIyEhWK5+lmkik0Whgt9uh0Whw/vz5uEM6+vr6AIBVtIt3jM6fP8+ChdHjQ5VKJTQaDesxThYUy5WnWAz8xXG/kUiE7ZdKpUIgEIDdbgcAlJeXJ9zWuXPn4HK5YDQaEQqFYoZhiF+E3G43uru7UVtbK/GsJJYsn206+ZWzWa0teltKpXJMnmap11AuV2uTaxjKdJ7QTEi6Mg6Kd+/eDYVCgX/4h39AdXV10mULCwvxwgsv4NSpU3jxxRdxww034MYbb8y0CYTkFbmqWknplZ05cyY+/PBDmEwmGAyGMZO2AKRd+Yzn+ZSTlpL9Ie7v74fH44HH42HFE6KLCvj9ftbjl2yCoDgfQZzIJv4n9hSLj4pDoVDS/Uk3T3GiiV06nQ6CIKQ8r2KKt0T7Ja7baDRCp9ONCa4FQWCT+QDpvYmJzpuUfLZS8itns1pb9LaSHUcAktuT7YqPycg97IPGCxMiTcZBcWdnJ4ALFe1E0R8k4geVSKlU4tvf/jZuueUW/Pa3v6WgmEw7cla1ktIre/LkSbhcLtZTnMlEu/b2drS0tGBoaChmMlpjYyMWLFgQs2yiP8RarRZutxvBYBDFxcWIRCIIhUKsTSMjIwiHw9Dr9UknCOp0OtYDKwgCeJ5nPapiUKxSqVBeXp50n9LJU3zkyJG4E7ssFgvmzp2L7u5ueL1ehMPhMRPt1Go1C6KT7ZdWq41pT/TnJwCWOUCv10vuTUx23goLCyXls02VXzmb1drEbSW7rsXUd+Od9DmRFR+TyeYwFEJIrIyDYo/HAwCYOXMme02n07F/O51OlJWVxfzOokWLAAAffPBBppsnJO/IXdUqWa+sIAgoLCxEe3s7CwCie2XTmWjX3t6O3bt3IxAIoKioiPVe2Ww27N69GwDGBMbJRCIROByOmKwRYgCo0WjAcRzOnTuXcILg/Pnz4XA4MDQ0BOBCACcey0gkwo519GdTPFLPh16vTxiEKBQKVFZWQq/Xo7+/HyqVChqNJmZik9frZWnoTp8+nXS/BEFgQ0yie+F5nmfBEsdxknoTU523Sy+9VJb8ytms1mY0GlNe1/Pnz0dRURGsVmvGkz6zGRhncxgKISRWxokaxQ+46Edr0UHw2bNnx/yOOD5PHF9HyHQiDnvQ6XSw2Wzw+/0sJ67NZhvXrHCxV7aioiLuGErgrzPyE/2cDM/zaGlpQSAQgMlkYuN4OY6DyWRCIBBAS0tLTA+q+Ji/v78fDoeDbU8cvhEKheD3+1kwrFAo4Pf7WXAmrivRBEFxqEKyQHV0L2ui5VKdD3FyIM/zqK+vx9y5czF79mzMnTsX9fX14Hkep06dYjP8xaEcYg82z/NQKBQoLS1l7U20XwqFAk1NTeA4Dg6HI6Y9DocDHMehsbERp06digmQxfNhsVjg9XrR1taGSCSS8rydOHECJpMJKpWK9XKLwbDX643JZ5vpcZyIbAeJrut0zmuq45jNrEpShqGEw2HZcosTQv4q46BY7Bn6+OOP2WvFxcWYNWsWAOAvf/nLmN/ZtWsXANC3XDJticMeqqqq4PP5MDg4CJ/Ph6qqKll7ppxOJ3w+H2pra1FcXIxQKASv14tQKITi4mLU1tayiXbJdHd3Y2hoCEVFRXEntel0OgwNDaG7uxvAhTGR+/fvx549e7Bv3z7s2bMH+/fvh81mY+M9NRoNCgsLWQAm9mprNBoWHM+aNQt6vT6m3Xq9HrW1teyYGY1GcBwXE1RyHAeDwYBAIMDalEyq8yFmNIie2KXX61nWC4PBgN7eXoRCIdTV1UGn07HAJRwOQ6fTYfbs2Wy8d7L98vl8qKiowLp162CxWBAIBOBwOBAIBGCxWLBu3TpUVFRI6k0UM1kkO2/i5EC9Xh+3TeL4Yyk9vLl2XWu1WsnnNVWvbLZED0OJR85hKISQWBkPn1i1ahUOHTqEw4cP40tf+hJ7/brrrsMzzzyDJ554AldccQXLQvGHP/wBTz/9NBQKBa644opMN09I3srGrHAxKCsrK2M9g9ETkgRBkDTRzu12IxKJJO298nq9cLvdKScJNTQ0ALgwkauoqGjMBDkxC0I4HIbFYoHRaIzb7qGhIbZvYgAsjpcVl4mekJZKqsmBqXrvxDG3M2bMQHl5edxJjT09PWxbifZLPB8LFizAvHnz4k6Oi25PskqFLpdL0nkThxd4PB6YTKa4Ff2kXpe5dl0nq/on5bzKWfFRimwOQyGExMo4KP70pz+NJ598Ev/93/+Np556iuVEve+++/Dcc8/B7XZj3bp1KC0thd/vh9frZfk377vvvox3gBBRNmePy7WtiZ4VHt3rFG9M6Ohep0T7pdfroVKpUuZELioqSjlJ6NSpUygqKmJBNAAWFIvLimNbk7U7ekLa6KBGEATWJr1eL/l48TyPnp4euFwuGAwGFBcXsyA7+ji6XC7WBoPBwP4NXJjYpVarWTDFcRzrgY1eRqPRsAls4pjd0edD7K0UC2pEH09xotnw8DD7gqBWq1FaWgqTyQS1Wg2DwcDOm5iiTvziIG5PpVKhuroaF110EU6ePIm+vj7WpqqqKjQ0NEzImFqp5bvjLTN6Ut/ooR2BQEBSb2q6kwPl/IxJtC4pWWWiv6TkUtYMOU3V/SK5LeOg+BOf+AT+5V/+BeFwGD09PSxvZm1tLV566SV8+ctfhsPhwODgIPsdjuPwy1/+EitXrsx084QAyE5p2cnYVqbEXqfOzk5W6jW6MINKpcLs2bNTlh6uqalBaWkpbDZb0slfxcXFknL+arVa+P1+1pMpUqlUqKiogNFoZJPWErW7trYWgiDAarWynuXoCXtKpRJVVVWoqamRdKxaWlpw+PBh1lOtVCrx5ptvYuXKlVixYgXMZjNOnTqFkZERVr1ToVCgoKAAxcXFbILcsWPH4HA4Yvars7MTJpMJS5cuhSAIOH36NNxu95gSznq9HvPnz095PsxmMwoLC9Ha2jqmF9PtdqO/vx/Lli1DfX09jh49ir6+vpTHKPozOhNS7o9MlzGbzZJ7U7OxnnTv+1TrSpVVJp3jmI+m6n6R3JdWUPyrX/0Ka9asYY8/gQt/6P7lX/4l7vIbNmzA6dOn8cc//hEfffQRwuEw5s2bh7//+7/HjBkzMms5If8nm6Vc861srEKhQHl5OY4dO8ZKuxYWFiIYDGJoaAgcx6G8vDxl6eGmpiY0NjZi9+7dcDgc0Ol0rPdKLA3c2NiIUCgkqfS0x+OB1WplT41EPM/DarVCr9dj0aJFOH36dMJ2V1ZWIhKJoKuri/V+qlQq8DzP0pbNmDFjzFjaeFpaWrBnzx7Wm6tWqxEOh+FyubBnzx4AYCWaRy/jdrsRCASwZMkSDAwMsGpz4vEXBIFVoXM6nex4j16PGGwvWbJEUilosXhJPIFAAOfPn4dSqcSMGTNw7ty5pMdocHCQbc9kMrHtWa1WuFwuWcs8A8nLd0tdRkpvqpTrWq71SL3vpX6GpBqGkm+fRVJN1f0i+SGtoPiOO+6AQqGA2WzG6tWrsWbNGqxZswbLli1L+FijrKwM3/zmN2VpLCGjTUZp2XzKHyoIAgYGBmAwGFi6KjEYKi0thUqlwsDAAPr7+1Pu1+rVqwGA5bsVMxNYLBaWp9jhcKR8HK1QKDA4OMh6LKODYkEQWO9vX18fDAYD64n2+XxQKpUoKytj42q7u7tZ9TjxP4VCAa1WC5VKhZ6enpihGfFEIhEcPnwYoVAIOp2OLSsGrF6vF4cOHWKT7MShEOFwmA0tCYVCOHHiBAv0o89/9GPuDz/8ENXV1TGloMX1pFMK+ujRo7BarUnPvdVqhc1mQ09PT9Jj1N3dzfYzk+tayv0hpfSy1PLMq1evTtqbajabsX///qytR8p9n+5nSKLhVfn4WSTFVN0vkj/SHj4hCAJsNhteeeUVvPLKKwAulNS8/PLLWZC8YsUKSamQCMnUZJWWzZf8oWKbi4uL4XA4xryv1+vR29sLQFo53GSTvwBpk4QUCgXr+RJ7UUViD7PX68XHH3+MGTNmQKvVjplIFQgEcO7cOQwODsJoNLJloifaBQIBlhFDHGoRr9etra0NbrebjWOOLgIiBo/ipDmxJzFeCef+/n72hUMM9KMD5EgkgmAwiP7+ftbm8ZaCPnv2bEz6u3h4nsf+/fsxNDSU9BjZ7XZWgCST61rK/SGl9HI65ZktFgvKysriXo8Oh0Py/ZqsVzad9aS67+X6DMnHzyIppup+kfyRVuS6fft2vPPOO3jnnXdw6tQp9rrT6cTrr7+O119/HQBQUFCApqYmFiSvWrUKhYWF8racEExeadmJ3pZcAoFATEnl6GIRHo+HTUhKVVI5er+USiWbOzCalElCHMeB53lWQU3sHRaDUEEQ4PF4WNo2cdxutOhyyMmWic6IkWgSmcvlYnmExaBRJAa4Yto4cVujv/RHl50Wq+mJx0OkVCoRiUQkXUPRpaBHT+oTczyPPu6i6Jy6IyMjLPtEomMknttMyyFLuT+klF5OpzxzvLGn586dY7mj07lfE/XKynnfy7WufPwskmKq7hfJH2kFxTfffDNuvvlmABfG/YgB8jvvvIMPPviA/THx+Xx4++238fbbbwO48OG2fPlyFiSvXr0aBoNB5l0h09FklJbNxrbkIpZUFnPOisGTWq2GSqViJZWLioqSlh5OZ79STRIaGBiAUqlkldJG9wiJJZ/F4C/RsR5dDjneMiqVCuFwGG+//TYGBgZielcHBwcxMDCAGTNmQKG4UDhETAsnEktQi8cs1bkPh8MJh2uI21ar1fD5fAiFQklLQff392NgYGDMZLzy8vKEQcNoRqMRDocj5THiOC7jcshS7g8ppZellmf2eDw4ceJEwrGnCxculOV+HZ15ZPSXxnTue7k+Q/Lxs0iKqbpfJH+Me4yDxWLBDTfcgBtuuAHAhRnPBw8eZEFyS0sLq3IXDAbR3NyM5uZmPPHEE1AqlViyZAmuuuoqPPXUU/LsCZmWsl1aNl/zhyaryCWWG05WUllqKWhRssfRpaWlePPNN+FyuVgGBBHP82yYwpw5czAwMJDwWM+aNStlOWSz2Yyenh709PSM2Te/34+enh6WzSIcDkOlUo0ZD8zzPBvTODQ0lHBbFRUVsFqtCAQCY4JrnucRiUSg1WpRUlICm82WtBS0TqfDqVOnIAgCG/sr9u53dnaybBHieY13fhUKBS677DKMjIwkzRpiNptRW1ubcTnkxsZGSfcHgJTbSrVMZWUluru7k4497enpQVlZWcptpbquozO4iMcs+kujUqlkGVxSkeszJJ8/i5KZqvtF8kfGFe1Eer0e11xzDR555BHs3bsXTqcT+/fvx9atW7FhwwYYDAb2mDQSiaC1tRU/+9nP5No8mabEx/XZKC2bzW3JJRgMQq/Xg+O4uCV8OY5jBTSAxKWHx0N8HD269LRKpcLKlSuh0Wjg9XoRDAZZMOz1eqHRaLBy5UosWrSIHWuxF1sclqHT6dDQ0JCyHPKiRYtw7tw5NpFNLCctFg5RKBTo7Oxk+XzD4TAb5ysOdVCpVCgrK8PSpUvZtjweD/x+PzweD9vWqlWr0NjYyPICi73G4XAYoVAIKpWKBY4KhYJtRywFLU6AKykpwfDwMARBiPnSoFQqoVarIQgCC2aTMZvN0Ov1aGxsTHqMmpqa0NDQkHE55Pb2dixYsCDpehoaGlJuS8oyYsaMVGNPZ8yYkfH9KmZwcblcGBwcZGkBlUolBgcH4XK54o7/TrQuOT5D8vGzSIqpul8kfyiELBV1DwaD2L59Ox5//HF0dHSwx4HR4/fyjcvlgtFohNPppOEgk4zyFMfncDhYSrFEQyPEMZwcx8HpdMZdBgDWrl0r6+SWeHmB9Xo9Vq5cicbGRgBAe3s7y3YhThArLS1l2S5SLaNSqfCnP/0Jer0+7pCDUCgEp9OJ0tJSaLVadHd3jxnSUFNTA47jsGbNGpw7dy5lm9966y28++67CAQCbD0cx2HFihVYvnw59uzZA7/fj4GBgTHHury8HJFIBAMDA2xIQ7whFuFwGBdddBHOnTuH4eHhmGEhSqUSJSUlWLp0KVavXg2FQiHpOCa7rjUaDfbs2cPGhI/m9/vh8/mwdu1ahEIh2fIUjx4HXl1dzcYL79u3j2UiGY3neQwODmLNmjVQKpUZ3a+CIGD//v0pc32Lx1oKuT5D8umzKB1Tdb/I5JEar01YiohAIIDDhw9j3759eOedd3D48GF4PB4AyR/lEjIe2SgtOxnbylT048iZM2eyyWniuFW73Q6DwYCRkREYDAb279HliaWUgk5XY2MjLr30UrS1tbEKcvX19Sxzg81mw+nTp6HVajF79uyY0sOnT59GaWkpLBZL0owYp0+fTtkOMS2cxWJBVVUV21eO41BWVoZQKASfzwePx4OBgQGUl5ejsrKS/T7P8xgYGIDNZoPFYsHVV1+NNWvW4OjRo3A4HDCZTFi+fDnUajUrK6zX69nxFINUsWDJwMAAIpEICgsLodPpxmSoEMtXi4+XzWYz/H4/q1pXUFAAvV4f06OWKmsIkHmZa3ECVLKyylK2lYz4tyOdsacmkymj+1XMiFBeXp4wE0q6GRHk+gzJp8+idEzV/SK5T7ag2Ol04sCBA3jnnXewb98+HDlyhE1QET/IVCoVlixZgtWrV2P16tW48sor5do8IQlnj+fytia6lGl0NggxANbpdAgGg7Db7ezR+AcffACXywWHwwG3280CNb1eD6PROGGTW8TiEmazmT2SB2LzlY5+NG0wGMbkKxUEASMjI3C5XGyYFgBW+c3v97OiFWKAqVQq4fP5UFRUhMrKSpaaq7y8nG0r3hhWs9mMoaEhds7En6Pbo1KpMH/+fLaMGOhzHIdwOIzu7m5EIhE24VGhULAKd+LyYsA3OtOFmPZNLM0stUctWdaQ0fsrBs4Gg4H1dKczASrT+yO6gEO8YiLR45fNZvOYL3ujx55m0p7ojAipMngA0u9pKW2Ssq5sfu5l01TdL5Lbxh0UW61WNqlu3759OH78+JhJHzqdDo2NjSwIXrVqFet5ImSqS/UHLVuPCFNlgxDLF584cQKRSCTmSY7P58PQ0BAWLlwo++SWVI/rpeYrPXXqVNLyzLNnz8aJEycwODg4ZpiBGLxecsklaGlpSZhGbsaMGTh27BjLnzx6SENlZSVrT7LhA2VlZax6nVhII7qIiUqlQlFREcrKyjA4OJi0pLbY0ytXj1qyIRbz58+XdQJUqtLLqQo4iOOX+/v70dbWNua8lpeXpz32NNH9ms4XgmyWgiaEyG/ceYrPnj3LXo/ulbniiitYL7D4yJCQ6SbVH7RslzJN9jhSEAT4fD42BlYsxSxOfguHw/D5fLK1BUhdynXu3LmSHtcfPXoU7777btLyzHV1dWhvb2dZIcR9FlPC1dXVoby8POkXh+ghEjzPs95dQRAQDAbR3d2NQCAAq9WKM2fOJE0T5vf72eRClUoFpVIJQRAQCoUQiUQQCASwfPlyHD58OGlJbTFYlqNHrb29Hbt370YgEEBRURFrt81mw+7duwGAPXEYGBhAQUFBzHCWoqKimCA02RfCVOe+oaFB0heiGTNmxJTTjpbuEL1UQbqULwSpsnOMtxQ0x3Gs0A2VOiZkYqUVsd56663sDwEAXHTRRawXePXq1WzCBiHTWao/+o2NjTEz+eP1hGWzlKnD4UB/fz97LBxdelgsutPf3w+Hw4GSkpKMtyellOv58+djhhCMJpaLPnbsWMryzMuWLUNpaSkMBgM8Hg/rBRWzUQwMDGD+/PlJvzgMDg6ynmYxg4UgCCzIDofDsNvtOHfuXMrzOjw8zHqYowuXaDQa8DwPh8OB6upqrFu3LmlJ7ejjmUlPMc/zaGlpQSAQgMlkYseR4zhoNBo4HA60tLTgy1/+MubNm4eWlhZYrdaY3uRly5ZJmkQnpRf41KlTbHx0POLwiPb2dgiCgPr6+rhj5aXeQ1K+oKYqSDM6O0cm97R4fwwPDyMSibDrTpzYFwgEqNQxIRNkXN24arUaf/d3f4fPfe5zWL16NX1jJeT/SAn4PvjgA4yMjGS1lGmyQMXhcMDn80Gv10OtVo+Z2BUOh+F2u2G322UJiqWUch0ZGYFer4+ZUCaKLhft9XpZz3Y0sfiH2+3Gxx9/jOrqakmTpBL1uvb19bFMOaOHmIhti0Qi6O3tRXV1dcL96uvrYwHf6KBYoVCwbBNDQ0NYtGhRyslxcjxi7+7uxtDQEEtRF12ARKVSQafTYWhoCMePH0dXVxc4jsPs2bNj8iuLEx8BJA0wFy5cmPLcO51OAMmLd4jXgBjEjx7nK/UeknK/trW1YfXq1UmfJKQz3EdKKWjxnIs5sqPzVCuVSnR3d1OpY0ImQFpBsZg/MxwO48UXX8SLL74IAJg3bx4bMrF69WpcdNFFE9JYQnKdlIDPZrNBEISEf9DkLmWaqidMLJYgtjHekCcxaJOD1FKutbW1CAaDKctFJxqipVarEQgEWAljKZOkEhkZGQEAFsSOHucLXDhGfr8/6X6JgbUYVEevJzpAFiWbHJfuEBye5+MG2OLESgBsTHR073VhYSHC4TBOnz6NSCQS90uKmD4NQMqxwKl6gRUKBYqLi+F0OhNOohO/OGVaDljK/SoGs1Kzc0gplZ6M3+9nafbELyrAX6tQejweDA8Ps+JYhBD5pBUUDw4O4qOPPmKT69555x309PTg1KlTOHXqFJ577jkAQEVFRcywiksuuYQe85BpQUrAJwYccpYyTfQIPbonTAwwvF4v61G02+0YHBxEQUEBfD4fGxoQvV6fz4eCggKUlZWldSwynbhUWVmJsrIyyeWiRwuHw6zARKbHWpwgrFAo2FhakZjVAgAKCgqSbqugoAAcx7Hy0dFDMcLhMIALQWSqCclSzmv0I/Zkk+j0ej0AsB5acUiI2GZxqIrP5xsTEIvHROwFBxBTqGX0MlJ6gTUaDRYsWIAjR47EnURnsVgwf/58NnQmk/Mq9QuaGMwmepIgXtPplEpPdH8Eg0GEQiEUFBTEPY7ilz0xv/hUNdGZeQiJJ+3hE4sWLcKiRYtw++23AwA6OztZgPzOO+/g1KlTsFqt+OMf/4j/+q//AgCW3F7sTV65cuWYHhtCpgIpAV9hYWHKoQFyzeQXH+tqNBqcP38+7h9rt9uN6upqdHZ2wuPxjCmFLAgCZs+ezYIBKX+s5Ji4JK43k3LRxcXFmDNnDvr7+zM61lVVVWwoSXS+YDGgFSfNVVdXs17FeNuqra1lk/bE9UX3yoo9sTU1NUnbI/ZwJjuvYg9nf39/0kl0V199NRuDLY7jFimVSkQiEfZ7yYJHMUhL1lsqFjtJdoyqqqqg1+sTBkBiT7Ic2TDSTTWXiNFoRGFhIdrb2yWVSk92f2i1WnaOxJ7z6H0TvwTG+yI4VVDmDTJZMk4NMXv2bMyePRs333wzgAsXc3SQLI6f3LVrF5vFrFarcckll+DKK6/EE088kWkTCMkZ0cUykv2xXrBgQdIUYFLTSUnJ4uDxeODxeBAOh2PGJ7rdbpY54LLLLkMoFMLAwEDMY1mlUonKykpceumlUCgUkiuRZTpxKXr/E/XOieWi9+zZw8YWi9knxB7HVatWoa6uDiMjIxkd63A4jLKyMthsNlb6WaFQsPLMYmq0WbNm4cyZMwm31dDQgIqKChak6nQ6dj4CgQAKCwtjMksk+gISCAQknVefz5dyEt2hQ4dQWFjIhk6IGTF4nkcoFIoZdpIseBSDtFS9pfPnz8fJkydTTlrjeT7hJDoxJZvUaygRqfdrOukI4/XuRkt1fyxcuBAlJSVwOBws+0T0NaJSqWAymaZsx1K2M/MQEk32fGkWiwU33ngjbrzxRgAXxuJFF/V47733EAgE0NLSgnfffZeCYjKlKBQKSQFfqtzBUsvPppok1NXVhZGREYRCIRQXF48ZnzgyMgJBEFBRUYGysrKEZXWlppGTkl1AysQlqX/0xNLKYp7iYDAIpVIJg8EQU3o5021xHIfy8nJwHAer1comeykUCmi1WlRWVsJoNKYc8mGxWNj2mpubMTg4yAK+0ZklUvUmivtbXFzMhmOIAah4XgcHB9kkuniTEXU6HYaHh6FWq6HT6RAKhWKyj4i9loIgsHG+yYJHr9ebsrd0zpw5MBgMY661qqoqNDQ0xExaSzaJbsmSJRmfV6n3a6rg2ul0wufzYdasWXA6nSwPtUqlQnFxMQwGA3w+HxwOR8r7o6enBzNmzGATHr1eL0snqNfroVQqUVNTI3ve8FwgdeIjZd4gE2XCkwiLjy97enpw/vx5dHZ2ore3N6NSz/v27cMTTzyBI0eOoK+vDy+//DKuv/569v4tt9yC3/3udzG/s379erz++uvs56GhIXzrW9/Cn/70JyiVStx44414+umn2fg6QsZLasCbaSlTKZOExAk7UtaZKpexlD9WixcvlmXiUjpSlYtOtW9SiD2KbrcbFRUVcDgcLFA1mUxQqVQwm80ph3yISktLMWvWLDaGVKvVora2lmVwkJLLF7iQ8UJsixikR0889Pl8iEQiKdOb8TzPAsJAIMACOjFDRyAQwIwZM3D+/PmkwePRo0fZuY6WzjlNZ5yvlJLSqcjxBVVsc2Fh4Zi/bYIgQKPRwOfzwW63S7o/Lr74YrhcLng8HtbDnygn9FSSzsRHyrxBJoLsQbEgCGhtbWXDJ9555x3YbLYxy2TC4/Fg6dKluPXWW3HDDTfEXebaa69lE/8AjHnk9+Uvfxl9fX3YtWsXQqEQvvrVr+Ib3/gGduzYkVHbCAEyD8KkkBI8iJO7wuFw3EexHMehqKiIjQdNNFRB6h8rsVcz04lL6VKpVFi0aFHG60lEoVCgvLwcx44dGzM2d3h4mPUkpxryAcQGvOXl5Ww9/f39GBkZkZTH+tSpU9BoNHC5XCzoFSf8iSWtxfLcqfI9q1QqNq5Wq9XG9MxGj2GtqKhAdXV10rRk0b2lo3s4xd7Sjo4OnDhxgo1f1+l04HkefX19cLlcaGhoyGpJaSDz+1Us333+/HnwPB/TS+7xeODz+dgERCn3h16vjwnUxX2OfnIzFaU78ZEQuWUcFIdCITQ3N7MA+ODBgyx9ETA2AL7oootw5ZVXYs2aNVizZs24trlhwwZs2LAh6TIcx6GysjLueydPnsTrr7+Od999F5dddhkA4N/+7d/w6U9/Gv/6r/+K6urqcbWLpC8XZxhnq01iGqt4j5ClpNKSMklInJDDcRwcDgdLqahWq1FSUsIewYq/n2wMq/jHiud52O12tozZbGZ/rMSeSjHt1sjICGtHcXHxmIAmHA7j6NGjcDgcMJlMcatghkIhHDhwAMPDwygpKcEVV1wx5o9mJBJJ2lMsHuuuri7W7tra2jHHOlF7BEHAwMAADAYDwuEwPB4PCz5LSkpiioAoFIqE7YnucTcYDPj444/h9/tRUFCAuro6uFwutLa2wu12s3Pj9/tjJqyJWRz8fj+0Wi14nkcgEGDjdzmOYz3QYu+zzWaDRqNBMBhkvcBarRZerxclJSUoKCiA0+mEx+NhTwbE/0ePYTWZTCgtLY27b2JasrKyMhQVFbEqfxzHobq6GiqVio0HFgtT2Gw21h69Xo9gMIienh6UlZXBarWirKyMDRPRarXQ6/VjxvlKuT7kuqcT3YvideHz+VBcXBwzyTA60C0tLY35AjJ6MmL0/WEymVBWVpY0T7WUa1/O/ZeLHCW1M91WrsvXdue7tINit9uNgwcPssl07777bsy3ttFJ7RcvXow1a9awQDg6J+pE2rt3L8rLy1FSUoKrr74aP/zhD1lKqUOHDsFkMrGAGADWrVsHpVKJ5uZmfPazn427TvExosjlck3sTkxxuTjDWK42SSnz/Pbbb2NgYCAm5dTg4CAGBgZw1VVXwWKxJE2lNX/+/JSThKqrqyEIAk6cOIHh4eGYHLR+vx9+vx+LFi2C0WhM2mbxj1VXVxesVmvMej7++GM2prasrAxmsxmnTp2C2+2Gz+djy4lZN+bPnw+j0Yi33nqLfX6Iy+zZswcrVqzA1VdfDQB49dVX0drayvLoAsD+/fuxbNkyXHfddQCAlpYWNqZYDAzffPNNNqbYZrPhjTfeGNNuq9WKrq4urF+/HhaLBW+99RablCZ666230NjYiOXLl8Nut7NxtdHEnlDxse6pU6cStmf+/Pmw2+3o6elheX2BC58l0UG3WG7barWOmbBWVlbGxpsGg0EWEIvEiXeFhYVQKpVobGzEa6+9ht7e3jHpzYqKinD55ZfDZrPB5XLB7XaPyVNsMpnYGNZ41+PRo0fR2NiIiooKqNVqnD17Fv39/THnrKenBxUVFSxPt8vlgs/ni2mPmPpPrVajqakJ58+fx9GjR8cMDRF7SxUKhaTrQ+rk0FTLJLsXxX0XBAFWq3XMcdbpdCz9ntlsRmdnJxsvHH1ulUolZs+enfB+PHfuXEybUl37Uvctm+TKTJPptnK5tz1f2z0VpBUUX3bZZfjggw9ibvjoIFjMKiEGwVdeeaUsFbDSde211+KGG25AXV0dzp49iwceeAAbNmzAoUOHoFKpYLVaUV5eHvM7arUapaWlsFqtCde7detWPPzwwxPd/GkhF2cYy9UmKWWejx49ip6enjETkvx+P3p6enDkyBHU1dUlTaUFQNIkoQ8//BB2u51lSVAqlRAEAcFgEHa7HaFQCHa7PWWb/X4/zp8/z3oPxZ7EYDCI8+fPs94tjuPYeqMzQoyMjMDv92PJkiXYs2cPDh48yIKL6DGTBw8eBHChCMTRo0fZZ4y4vUgkwsaulpeXY8+ePWO25XK5sGfPHgiCgN7eXtZurVbLjrXY7v3798NgMODgwYMxhTjEtGIHDhyA1+uNyfYQfc6isz0cOXIE7733XsL2uN1unDlzBl6vN+6143K5EAwGUVFRwR7Fj84s4fV6UVhYyI7X6OMTPfZUHBYjvh6N53kWbHIcx7JPiEMxxMl7TqcTHMfh1KlTSa/HdevWwel0ore3d8x+idX+BEGA3++H1+tlQbfY7lAoBI/Hw+4hcRJidM9oOBxmkwebm5tTXh9NTU3suuY4DhzHQRCEmHsaSF6Fr6mpCUNDQ0n3fcWKFXEDffE4+3w+dm5HD8MR82gPDg6yYTji/SgOMSkoKADP8+jt7WVt6ujoSHrtA0BdXV1Ofc7KnZkm023lYoCZr+2eKtIKisUPGlFBQQEaGxvZUIhVq1ahqKhI1gaOxxe+8AX27yVLluDiiy/GRRddhL179+KTn/zkuNe7ZcsW3Hvvvexnl8uFmTNnZtTW6SgXZxjL1SYp62ltbUVHRwcUCsWYilVFRUUYGRnBxx9/jN7e3qSptFpaWvDlL3856SSh0tJSHD9+nAUOo/9gC4KADz/8ECqVKmmbT548CavVyqq5ie2JDsSsVivC4TDLPqDVamMyGRQVFSESieDkyZPo7e1lY2HFdYnrDYVCOHz4cEzQNjoHsTh3QcyYIPayAWABgtfrxYEDB9jyhYWFY7Jv+Hw+nDlzBqFQiE1IjB4XLO7bsWPHYDKZEIlEYnLojs7iIRaUSNSeo0ePJgyIRX6/H8FgkI1DFdcj5mJ2OBws+4TYTlH0v51OJ5RKJVpaWhCJRNhj/ehAOBwO4/Dhw1AoFGwIg9gzK060Ex/Pi18SEl2Phw8fZgU8ErHZbKw4SHQOXvHnQCAAt9uN1tZWhMNhVFRUgOd51gsq7v+hQ4fYF51k10dRUREbqjE4OMjWU1hYiEAgIKkK38mTJ9HZ2Zl0348dO8bWHx3oi/8Xg3mVSsWeCEQiEfh8Pvh8PiiVSpSWlrL3+/v72STZoaGhmN7kYDCIEydOoLW1Nem1dujQIfh8vpz5nJWrpLZcmXlyMYtFvrZ7KkkrKNbr9bjiiitYELxixYq8SCA+Z84cmM1mnDlzBp/85CdRWVmJgYGBmGXC4TCGhoYSjkMGwHoaSGZycYaxXG2Ssh7xkbjBYIi7jJgz1uv1ori4OGEqraGhIXR3d6O2tjbhJKGPPvqIBWzxPkQFQWBBeHV1dcI2f/zxx6y3TeyNE4ljgH0+Hw4cOIChoSEUFxezssbiH3SVSoVAIMBSmok9xKP3LV4BidHLRCIR1nNaUFAQdxmtVst6HhNVB9NoNDFBanRQHH2MQqEQy4WciNgDKhapSNaeVNxuNwoLC+Hz+cZMjhTLLovnYHSFPfHnSCSC48ePs7Li0cUgxJ8BwGq1Qq1WJz1n4mRpMSXY6H3T6XTsvCYj5nhOVJob+Os1KV77o7en0+nQ19fH9jnZ9XH8+HGo1eoxPe4ejwdKpRKdnZ3sCUeia7+rqwt2uz1pWruhoSH25S/6v+h1RSIRnD17Fna7HeXl5XHHFAcCAfZl2OPxxH1SoFQq0d7eDrfbnfRac7vdKe/rbH7OylVSW+5t5VIWi3xt91SSVlDscDjG3ID5oLu7G4ODg2w886pVq+BwOHDkyBFceumlAC6MHeR5nj1OIxNnMmcYS5lIlkmbpKxH7IlLRvyDn2w9Xq83prcwUdYIsXzw6D/UgiCwANfv9yfdljhuNboXTAy0xZ+9Xi/rlROXGx38iEEXMDaYEYlBjdjmeKLbII7lHB3MqdVq+Hy+MT2Jo7cVvc54f4iie/04jkuYxUPsbU4U8CULBEcLh8OoqanB0NDQmCwOpaWl6OnpAQC239H7EX3shoeHWcEH8ZyI74mPZcXrI9k5E8dRp7quRfGOd/TQFPGJwOgy1+Lviddaom1FrysecZ0ejwd6vX7MExmVSgWPxwOHw4GSkpKU136qtHbRx1y8BqP3W9y2eD+m2p74tzZRuz0eDyKRCAoLC+OuRywFHQgEciaTg1wltSdiW7kiX9s9laQVFOdKQCyOyxN1dHSgtbUVpaWlKC0txcMPP4wbb7wRlZWVOHv2LO6//37MnTsX69evBwA0NDTg2muvxde//nVs27YNoVAId911F77whS9Q5oksmIgZxlJImUiWaZukrEesNOb3+1lQIBIEgU04Esd0JkulJebVTjQrfvQsdHEbo4OJVGm7OI6DUqlM+IEtBjQlJSXo6upK2e7o4Qqj2zR6zsLoIR/i6wDYFxoxII5+nC72RIvbEx/bR69DrOAWiURYkJ1oW4WFhSgvL4fL5YpbnMHhcLCgJ16PcnTQmIpYFrm2tpaNRdVqtTAYDCwIF/dfDMRE4phxsc3R+xR9jKKPvRikxuspFq99IHlFO7FstdiuZPsmXpejh3MAYOtJtq3oin/xiK+LOYLjfdkRhxkoFIqU176UtHZi4Cx+URH3S9xXnudhMpng8XjgcrnY06DRVf/EXmUxi0i8dovnTLwfR39JFe9H8ctaNj9nE8nm5/5k/Y3JVL62eyqZ8OIdE+G9997D2rVr2c/iON9Nmzbhl7/8JY4dO4bf/e53cDgcqK6uxjXXXINHHnkk5kL6/e9/j7vuuguf/OQnWfGOn/3sZ1nfl+loIkqrpiJl8pvYJrPZPKa0rNQ2Sdm36upqGAwGnDp1Km6voyAImDdvHsv7Gz3uFgCbtW6xWFBTU5N0Vrz4WFj8oxkdLEQTH1Um2vc5c+awYHB0T7cYlBkMBlxxxRWswEOidldWVqK3txfBYDBhwCumfhODi3jE3k+xLHV00Cs+Pi8sLERpaSkGBgZYYBzdHp7nUVpaysaDxmuPuO45c+awoQij2+t2u3HRRRexYxQ9EVH8v1h9LjplZSJLlixBT08PS/8mnteioiKWgaG3tzfuUwdxH9RqNerq6nD8+HF2rEd/cYhEItBqtayssBiQRmd7iEQiKC8vhyBcqJCX6LyWlZWhr6+PfZEZvQxw4VqZNWsWzp07x77MiNsSA8H6+nrYbLak135VVRWbiJhoW+KXxuhhI9HnTJwwWVpamrRSX21tLUtDmKg95eXlrJLd6M4jsY0GgwHLly/H0NAQ2tvbWXATPTTC4XBg5syZrAplonYXFRWxeQDilxiReF7Fwln9/f1Z+5xNJpuf+5PxN0YO+druqSQ3un7T9IlPfCLmj7z43/bt21FYWIg33ngDAwMDCAaD6OzsxLPPPouKioqYdZSWlmLHjh0YGRmB0+nEb3/7W6pmlyUKxYXSqjqdDjabDX6/n82Yt9lsac0wlmL05AWx11OcvCCWpl2wYAEUCgXa2tpw5swZdHZ24syZM2hra2NtTtUmqft22WWXsbF+4lhUv98PhUKB6upqrFixAk1NTSy/cPR6HA4HOI5DY2MjTp8+jd27d8Nms7E8sgUFBWxWfG9vb8rH9hqNhlVXS7TvCxcuxJIlSwAgJmVX9NjUxYsXQ6PRoLGxMWm7V65ciSVLlsQE6+J5Etd78cUXY/bs2UnbPWvWrJiJvaMDf+DC+NPVq1eziVXixCafz8fG51599dVJ5xIAQGVlJWbPng2Xy4WhoSE2BletVmNoaAgulwsVFRVYtWpVzCNun8/H/q1SqbB69eqUk3NnzpyJGTNmYGhoCFarFW63m01As1qtGBoaQklJScrPKzFwMplMLCgfHaTzPI+SkhLMnTuXjbWNRCIsSBV/rq+vx8qVK8FxHIaHh8f8x3EcLrvsMjYBKHo4S/QXG7PZDIvFglAoxLYjfoEQn4yUlZWlvPZXrVqFSy65JOG2xPvQbDaz4FEM9sVCNmKO6VT3a0NDg6T2jL4/xB776Psj+skNz/MIh8PsS2h0MF9SUiKp3cD/Z+/Po+S48vNA9IstMyL3vfYFaxUAkmATxMJutthssaWWWnKrJcuynux5svw8tp/tGT/P2PN83hw/ezw+PpLHi3xmbGtsa2RbbVvWc3dbrYWtJs1eoCYAgiCxEKhCAYWqQm2573tGxPsj63dxMzJyAVEAt/xw6lShMirixo0bEd/93e/3/cCuI01GSNf99NNP4/jx40/sOTsIT/K5/6TfMfuFj2q7P074SEaKR/joYz9Kqw6LYZMXpqam+moUh8Ww5/a5z32ub/EO3ouUtKWSJCEajeLMmTM4cuQIvvrVr/bNir979+5QbbZL+uPPnaK3kUgEuVyOaVFp2TsQCLBl3IWFhb7tPnr0KBKJBEKhEHK5XAdpIhLn8/kQj8fZse3aRFFrr9fLImYESZKgaRqLvhMRs0ZBo9EoQqEQZmZmkE6nbbV6TqcTMzMzzDWAooPkGhAOhyGKIhKJBBYWFjA2NsacOKjtsixjbGwM8/PzqNfrSKfTti4ULpcLhw8fxurqKosO87phigJSMlY/1Ot1VpiDZBFEHklTTJaAgiDA7/eza9toNNg2fr8f9XodR48excrKSpcvsCRJWFhYwLPPPotSqYSrV6/2vK5PPfUU1tbWIMsyVFXtIOqkS15eXsYv/MIv9B1DCwsLbJxRe/hjPfvss/jSl76E8+fPM8Jp1WaLoojp6WkcOHAAXq+37/066F6kMT3o/sjlcqhWq4jFYkgkEsjlcuz8SZ6j6zrzoiaHCmo3TXSmp6dhmiYCgQDra/560PEikcgTe84Ogyf53H+Sx9pPfFTb/XGBYA7K+BmhJwqFAvx+P/L5PHw+3wfdnI8keiW+7Sfi8Ti+973vMfJiBS2N0tK2nYQglUphYmICL7744tDtG+bchtmmV8WqjY0NfP3rX4eqqrYas1qtxpZh7RLXeMJ24sQJ1Gq1nuceDAZRLBbZ8j0RSKfTiXA4jGaziWq1ipdffpklx/TSOedyObzxxhusoMHOzg7b18TEBPN/3dzcZMTMWrGNT2qLRCIA2u4X1G5KQMrlcpibm2NEw1odLZ1OIxAIsKz+bDbLnARkWUY4HIbf72fRt0Ag0NM1oFKpwOPxIJfLMc9zOq/x8XEW4aV+NE0Td+7cYRXtDh8+zNqcTqfZMelcRVFkem5RFFEoFGzlMHSNRVHEl7/8Zdy4cYMRJ6u9GUkyADAPZl7+QJOHQCCAiYkJXLx40VaGoigKXn75ZeaLS97N5Husqio8Hg9mZ2fxxhtvdBB1q9NFvV7HV77yFczOzg5Vra1fRTuSTZHfL++H7Xa7Ozxfh7kXhx3Tve6Pp59+GhcuXGB9za/i0P/dbjeef/555mdN2mJzzxLP5XLh+PHjuHjxInK5HLtmvKaYrtmP/diPsZWCD1N1tCfZng/buQ+Lj2q7P6wYlq+NIsUjfKB4lAzjYTFM8gLptSjiqqpqxzbvxwpnmHMbtI1dcmAmk8Hi4iJKpdLArHiKChKR5MkTT7LS6TQmJyd7njtpaal/rMVvKHLLRy9FUcTs7GxXu/gMa4rWWfdFy/YOh8NWrsG7F9B1tXqkkxyFNJ+SJHVp8ei6mqbJtHoTExMdhNc0Teb2QElb1j5yOBxIp9Oo1WpwuVzY2tpiSVRUWpnvR5ps0D6BtjVaJBJhhUIIvEMDLcVbEwPtNLWmaSKZTDJyv7Oz01VhMBaLoV6vM09coFMeoygKdF1nUg7qe+s4Im/pU6dOdUS5rAmtVNa5n9MFuar0G/t8tExRFHzuc5/rGmfAg6jbMOXUh8GwY7rX/WEYBqsayPtd07mTfeLY2BjC4XDX+VO0UNd1ds2s+yHXjWw2y/T2T+I5+zB4ku35sJ37sPiotvujjhEpHqELH7cZ6jDJCz6fD8VikSV4pVIpdv6RSOR9WeH0iioNi0HJgbOzs0NlxdNSNS2LU2SOjP4pm5/kD9YoqMPhYCRpP7Ki+UmKoihdfd1oNBhRpGV8q+UYXxCE9LrWdlO/ORyOvhMH/tzsXCOISAFgbS4Wi+z3Xq+XtbNer7PP+IhroVBArVZjiVP37t3rqEYHtCPdxWKROY8QKeWju3S9+ci/VWJCpNU0TVagI5fLMR00nS8AVkWNEjwBdLSbotX1er2jOh4POlaxWMTS0hJOnDjR02O2Wq0ONWZbrdbASnQPE+Gla0fXyYpkMombN29iY2ODXdfZ2VkcP368gzj3OtbDugZQH5K0w84ppp9XLzm80OTMuh+yZBvkGz3CCCN0YkSKR+jAx7HmOiUv9CsdevToUVy7dg0bGxssIkbkYXV1FePj4/D7/UNb4fRzhCAtZD9YkwPpRc5XNiKP1UEOFYIgME21dWmcIt9erxf5fB6FQsHWJkrTNCYNeNSsaJqkvPfee0x/yfd1IBDA/Pw84vE4G4P88SgpS5ZlnDlzBt/97ndZJJeH2+3Gc889h7W1tb5khc4tHo8zDSdf+UySJMzPz8M0TaysrLBy1dRmVVXh9XoxPT2Ne/fusc+tjgC0HQCmE7T69JJ7AZ0jkR3eMYK+89fSzg1EkiRMTExgdXWVTRzsnCVoMkQRbSv4MtyCINhay1F7yc+4V5RrenoaoVCorzsJ6XL7VaKjql6pVKrv8yqZTOK73/0uEokEux5UcjmRSOCll14CAPzhH/5hh5uHIAjY3d3F5uYmfuRHfqSjmpjdsSKRyFCuAU6nk91HtELB9yGNRSKzvfqRJnqUNGqN7gMPbP1GGGGE4fGRdJ8Y4fGAIpM7OztwuVwIh8OsetTFixdZVauPImgZdWJiAtVqFel0GtVqFRMTEzh79iwOHjyIWq2G+/fvs8gpldVtNBq4f/8+arXaUKRveXm5ryPE8vLywH0MkxyYTqfx1FNPDXR6oMx58tCVZRmtVgv5fB5OpxOf/vSn4XK5sLGxwaLlLpeLRUQ3Njbgcrnw7LPP7ktWNEVlKUJMBI0iralUCrquw+VyMbJF0VJyGBAEAS6XC+VyuWc0rNFooNlssmibXYSzUCggGo1ibm6OOUsQOREEgTlLxGIxqKqKVCrF9MfkYlIqlZBKpVh0miYVFMUlklqpVNhqg5XIUntovBGBtWrBiShTGW36nXUbAB2V76hAC5EmQWi7nlAZbtrGDrzlV68UFIoWD3I6EUVxoDvJiRMnsLW1hVKphHK53DEey+UySqUSNjc3sbq62vd5lUgk8Pbbb2NrawuGYUBVVbjdbqiqCsMwsLW1hcuXL+P8+fMs8kr6Z1qV2djYwPnz55FIJPoeK5VKDeUaoKoqS57s1Y9UXrsfVFWFy+Vijir8PcQ7q1hlPiOMMEJ/jCLFIwD4ZNRc77ccqes6dnd3GTGhCBZF8QzDwO7uLovA9YJhGLh06VJfR4hLly7hyJEjfaUUw1Y2mpqawiuvvNI3S5/QL3N+bW2NnS8P/v80sXjvvfewurrKlpkPHjyIEydODL2SQOV36Tz4SCglX92+fRvhcLhDX0yg5LBAIIB33nmH6SqtSVvVahUXLlzAn/gTf6LvKsHCwgKWl5f7OkvE43Gsra0xMkpL8eQIQG22lowmEAmiCLPP50Oj0eiQs1Bkj5Kw+jlv8NIIq75YEIQOYkUSBKAtIWg2m4z4A+2xxpfVtkMv/2YrpqamBm4zyJ3E7/fj4sWLMAyjZ0W3bDaL5eXlvs+rd999F/fu3WMyG34/brcbxWIRd+/eZTprkmjwhS/q9Tru3r0LTdMGPhtffPHFga4BZMNG1nPWxEdKnBuUuO3z+SBJEhuPNLmhlQtanRolgI8wwsNhRIpHAPDJqbneazlyaWkJ1WqVvRh5v1OKflWrVaaZ7IXNzU1kMhm43e4u0ktyhEwmg83NTczOzu6LRnFhYQGHDx/um6W/sLCAI0eO9Mycr1armJubY1W2eOsqn8+HarWKfD6Pe/fu4erVqyiVSuxlXiwW4XK5hibFS0tLKJVKbAnYmjnfbDZRqVQQCAQwOzuLfD6PfD7PXvR+vx9+vx/ZbBaVSoVF9Yg4EzmWZRmlUgnpdLovWSFdcywW6+kssb6+jnQ6DU3TuggkERFa7ieJA08kKTpIke5BoKgryRX4SQNNInjnCKucg/6eouWapvV0e+Cj6HQN+HbwhLsfBEFALpcbmhj3GrO8XtbuWUTlu9PpNGKxWM/n1e7uLiqViq3dIE0K0uk0i7xXq9WuyZckSajVatjc3MTMzMzAZ2O/iTfQzoAndxR63lAyY7VahaZpkGWZJf32Au2HJmS0skGkXlXVofYzwggjdGJEikcAMKq5XigUmLUU7y/LLwu3Wi0UCoW++xnGEaJfdv3DahT9fv/QWfqDMufD4TBLOOSTyIB2QtaVK1fw1ltvsYQ06pNisYg33ngDAHDmzJmh+7rXUjuROYrIzs7O2trEOZ1OGIbBIp/WZDy6hoVCoW/yF2mX+zlLUFSXxgavBSbiyZc4JkLFJ7XxxSry+TxLiOL3U61WWd9SMQt+LFISIiVK0iSCr1xI50LRUZo00H75cURRUeABcbfqXMkbeRD4yPag5Ld+Y5YmS/0qulFb+yWH2lX7s2szX8mQPxb/+/16NtbrdciyzHyxeUmT3+9HOBxm8odh9jM9Pc2i7TQh83q9CIVCqNVqH9vn9QgjPC6MSPEIAEY11ymaRBpXa+Up+v2g5UiPx/PQ2fV2zhJnz54dmBy4uLiIVCo1cD+DIrh07fsl2gmCgGvXrqHZbMLlcjEiRUSrUqkwS65+8hLqa7Lxoqg8gf6Wyiqn02mkUin4fD64XC40Gg2kUim4XC6EQiGsrq6yqCmAjoQ0IqlE7HutEgwz9vkILfnG0j7JR5lvP5/MSP+n81JVFaVSqaeLg6qqHZXM7EghWdBRgqSVFNE49Xq9rISzXUlxirxT+3tFhUkDTd+JAPPf6WtQsu4gV5Vjx44NbLPP54PT6UShUGDnbx2zbrcb1WoVtVqtK1nTNE02AeGdN+xAUWUi6VYCzj8bB507jbVisYhsNtuR1GkYBhwOR0/fcbsxSy4ZdqsbNE5GGGGE4TEixSMAGNVcX1hYgCzLqNVqLGJIoCVsVVWZHrJXJIzPrieiRcv+iqJ0ZNcPq1Hs5bEaiURw/vx5ts9Go4FKpcJexJSZP0gH7vf7oWkalpeXmTWbLMvsuudyOYTDYVQqFRa543XAtKxfKpUGyksAYHFxEX/4h3/InBZ4Ek1uC16vF2fOnEEmk+kpexBFEd/5zneYRtMOtH0/8GPfrsAHJdrF43GmAbaSQlploGQwIjkEirZ6PB6EQiGIoshkKgRJkhihJU9bGov8sWgyMT09jWQyiZmZmZ7FZqanpzE9Pc20x7SSIUkSm8DNzs6iXC6jUCh0ySeANqF3uVzs2HxEmU8kpMlCPyu1M2fOMC1wrzG7tbWFqampvm2en59HqVTC7du3GREk4lwqlZDL5bCwsACfz8e03lZybZompqamsLy8PJAUBwIB5mBhJeCiKGJ+fh6NRgOXLl3qayNHhWbW1tZgmm2/ampTpVLB2toaFhcX2XO2V/ES6/OaX934KDyvh7XRG2GEJ40RKR4BwHC2ZR/nmuulUgmhUIgtpUuSxEgIReBCoRAz3u9XDODMmTP4/d//fezs7HQRI7fbjRMnTmBjY2Mo/XY/kA5cURTcv3/fNlrG68AHvYjIocBqAUaRPCKD9XrdVntpGMZAeQn1w/j4ONMlE7EicikIAsbHxyGKYl+NZjweh9/vRzqd7tDE8j/7/X5bCzFrny8uLmJjYwNXrlzpsi2bnJzE3Nwctra2kEgkUKlUuiaNVFJ3fn6eSUwoMYwcIxRFwTPPPIN0Oo2xsTFsb293XDOqfFcul9lqBZFBOg79n8ZjuVzuGUmnicPi4iLi8TiznKP9lMtljI2N4dixY9je3mbXw+56jY2NQdd1JBIJZofHyzWoQMjm5mZfK7WrV6+iWCzC4XBgY2Oji/DS9Tx58iTi8XiHlZphGCiXy4jFYlhYWMCVK1dYv/Dg/3/q1CmUSiUkEomugiiTk5OYnp7GyspK30RCum83NzdRr9fhdrtZMRzyeY7FYlheXh5oI/fpT38amUyGSXBoxYXcblqtFvv8rbfewoULFzr0+6+//jrOnTuHM2fOfGSf1x9H288RPj4YkeIRGD7JNdfJLUJVVWxubnb5fk5PT0NVVezu7uL69eusMhltk06nEY/H8Tmuspbd0jcAlik+SKO4u7vLSr0GAgFGTnd3d1EoFHD48GGUy2WUy2VGwijCS165brcb9Xq974tIURRks1mmzbRqSknjC7QTunh3DgBM+yqK4lDZ7vl8HqqqYmZmBru7u6zQBUWcx8fHoaoqI/O9ZA8UZVNVlSWa8YSY7K+G8WrNZDIs6Yo/t1arxez7yOKKkuWozdQGTdPw3HPPwePxMDJD+/P5fDh37hyOHj2KP/iDP2DFNEKhUMexUqkUm2R4PB4Ui8UOkq4oCrxeL+r1OhRFGfp+tSaNUh+ZpolSqQQAzOLOev3JDm18fJxVZOMr47ndbng8HkxNTSGdTjMSx0dmKXq+tbUF0zRZ1T6ejNLvqKw23TvW6yoIAmvD7Owsk/xQcihF20ke8cwzz+DixYvs+lIRk2eeeaZDB97L6QMAstksfD4fS4gjd5JQKMSSA5PJZN9z39zcZEmmfr+fOYHwY9/lcqFUKuHb3/423n333S79fqFQ6NDv7+fz+klEbwdJZ4aRe40wwuPEiBSP0IFB2dMfV5BGz+v1YmJiAtlslr1Ag8EgPB4Ps63b3t7uWrKt1+vY3t7G5cuXGWGenJzskk/k83m89957CAQCA3XHGxsbfSUW5CtM/rM8UaGXDZGeW7du9VzWPXToEOLxOLMT44kTLesS+aO2WeUlpIccpjAJ6R1dLhecTmeHI4LT6YTL5UKr1Ro6SYiSzqzketjqgWSj12q1MDY2ZmuTdf36dbbt2NiY7XUlK60zZ87g1KlTtsveZMlVrVY7LPsAMMs+j8fDJCyxWIwV1SCCT9fA4/EMvF9pzJqmicXFRVuZxfLyMjKZDJxOJwKBAPMvpkqI1WoV2WwWzz77LBqNRpezClnizczM4M6dO32t1IrFIhtTVhJKxT8Mw8DGxgaq1SrcbndHNNnlcqFarWJ5eZl5UAcCgS5NrWmaSKfTbGKpqioOHjzIyG+9XsfKygoOHDjQ0U6rVIVWRGq1Wl93kmQyiXQ6zSYJdueezWaZi4rX64XL5epyA6F2X79+fSj9/n49r59E9JbG4sfZ9nOEwWg1dORTVeQT7a/5Z8IIjrs/6GYxjEjxCF3oFZn7OIPX1RI55iM9+Xwec3Nz2N3dZfIE64uvWCzizp07aDQaTPtoTTpzuVysYARZONnpt4PBIIrFYl+JRTabRaPRYBEyPrGLiJQsy7h7927fZd3bt2+zxC+rnpoS4sjaii8ewRMHqthXKpUGjh2n04lyuYx4PM6iaqTBrdVquHfvHsbGxgYmCVE0kSQfvB6WyB+VYO4Hq42elUy7XC6kUikEAgFomoZarcYmFyQ5sVppiaKIqakpRCIRdn6AvSUXP7HSNA1utxuyLCOXy7HVCwJfqXB6errveQGdVouU5MeDJDYkC+Bt3ghUMtjtdndEJq3kiSqr9bNSo4gu78RBIIJYLpexs7PDpCUU8eejrpTwSBNL63lRQp7dxBJo32fJZBKrq6sdpbT5CR/dJ3zlv17uJBT15Qmx9dzJMYJPxLU6sNBKTb1et53Y0QRwWP3+MKDobblchqqqbDVke3t7X6O3nxTbzxGAZl1HPllFPllpk99kFflEBflkFaVsZ7DD6ZJHpHiEET7M6KVRrNVqqFQq8Hq9tg91TdOYDGGQJVs0GkUqleqpB5yZmcH169f77oeSvsiqywoiEtvb2yzL3W5Zl5axe0Vn+ESq+fn5LnmJw+HA9PQ0i5oNgtfrRaFQ6IqEESGtVCooFArMNaIXqOJeLy9dkpH06kPCMDZ6dBzeAov3cuYtsPpF3ciKbtB+jhw5grfeegu5XA4ul4tdb4r2nzlzBqIoDozwDWO1SPrWfhZoDocDDoejb2Ryd3d3oJUaMLgQiK7ryGQyHbZyQGfUtVQqsXY8ysQyk8lAVVU2+bNKVRRFYefeb2WHtqWqeNbVBurDYcpca5rGHC3sQHKmQqHwyBFeit5ms1kYhoFMJtORl9BoNPYtevtJt/38uKFRa+2R3W7yW873D0TwyCcrgzd6ghiR4hFGQDuKMUijSMSoHyiKNUgaMTk5iUOHDvUtKHHr1q2++xEEgUXc7F5YgiCg0WgwT9xeBIPs5oj8Wl/U9DeU+DYxMYF0Os1IUTgcRrPZZJHPQdja2mIvR3qR80lb9PutrS1bX2UC+SnbecxS0l6j0UCxWEQoFOqpmRzWRo/8b/tZYPFSlV52Y8NYaR09ehR+v79vpcJh9JnD2s0FAgGUy+WeFmh8xLrXSpKqqgOt1FwuF3Mxsdrx0Tgg32d+NYbAR12np6eZlvf9TixN04TX60W1WmX6byLFNIEJBAIIh8PI5XI9CTgR0HQ63ZH4SW2mcu8ulwtnzpzBa6+91nPCc+TIEVy+fJkRaSt43fuj6nPz+Twr6GOdNFM5883NzX2J3n7SbT8/imhU28Q3l9gjvXvR3lyyimpheOLbD/lEdV/2s18YkeIRRkBnAYteGkV6mVer1Z6+p6QHzefzPSNBtPRNZYTtqsyZpsksl8i6iteCFgoFhEIhbG9vsyVmu2plRDyI0FAJYVmW4XK5mA0dH5WyOmaQP20sFkM+n0c4HGb2W/TStrOA6kVCKbErEAiwyYdVU0zRQKC3LRVFeIHuSmx0fHq594uo8dE76jf+2pN92OzsLHZ3dxEMBrGzs8POa3p6mp3/1tbWQLuxcDiM3d1dZs/Ft5fvx0AggEOHDuHKlStMSvHcc8+xZErSZ4bD4a5JSjqdxtLSEj7zmc8MtFqcnJyEaZpYX19nyVx8mWCKbA9jE0b2bzTW+Sg4fc/lcsyOjFxNyOWESmfzUVerpprIYiwWw8TEBG7duoWNjQ12/rOzszh27FjHxLKXv7CmaYhGo8x1hsosExmmc19YWMClS5eQSCRYZJnkPm63GydPnsSVK1dYqXirO0uz2UQwGGTXFehd5pqv9Mc7VNC92Wg0mCzoUfW5tVqNRYnJi5kmMKQnz+VyqNVqPfcxLD7ptp8fVtTKzZ5Sh2qxf/n3R4HL74A/qiE05Xlsx3g/GJHiEUZAdxTDTqOoaRpmZ2extrbG9HcUVSH3g/n5eczPz+P1119/X0vf6+vrbOmTrLSWlpa6iGosFsP09DSWlpbQarVsXQMoGkx6Z5JQ8PvRNI1F5LLZLGRZ7oisUWJYNBrFuXPn8O1vf9vWtowi3PSi60dCKTJLcgNeikFtp0SyS5cu9bSlAsAIO0X2+HMjSUU8Hse9e/f6RtTOnDmDb37zm9je3rZ1Xzh79ixCoRCuX7+OW7dudYyNjY0NRCIRPP/887h69SocDkdPizyyG0skEj2vK/Xj8vIyI05ECpeXl3HmzBmMjY0hlUqhWq3i8uXLXUmG9HmhUBjKugsA7t+/j1Qq1XE9Go0GJicnWZv6XQ/eJqxcLjNtNU8eFxcXsbW1hVwu17VMXqvVGBGnMt67u7tdTh8ulwvhcBiqqiIej2NtbQ2pVIr1ESVDHj16FJFIBGtra339hRcWFvC7v/u72N3d7RjX1WoV4+Pj7H48cuQILl26xNpEOvpnn33W1umD/78V/cquA8C5c+fwxhtvsIk4uU+QVOPkyZPIZDKPrM+lqDxprK33NU22SZ71KA4VZH34UbSR+yjDNE3Uy612tJcjvBQBrpf7W1Y+CjxBJ/xRDb6ohkDMBX9Ug3/vu+LsX+Tpg8KIFI/wiUKvh/qwUYyFhQU0m00kEomO6Al571JGOBGIQUvf5ElLkgs+uYWOTVIAelnR9pT4w5c05tus6zqL6PLZ/vT3lNREpO/73/9+B3GgY7rdbrz44ovI5XLIZDK2tmWZTAaZTGaoimWnT59m1b+AB1XgALCEpWg0it3dXbzxxhs9bakWFha6iDRPSHi7PIq89ypwQtIFu36s1+vI5/NYWVlBKpWyHVepVIqRU4rGW5eiiRgSgQXQ5QlNv19eXsZrr72GWq3GKpyRT/Brr72G559/HvF4nDmdkHcxVV+8f/8+6vU66vU6xsbGbIvAENmNRqNYXl5mlmXWYirpdBqZTAb37t1j14M0tORNbbUJ63WsSCQCj8eDdDpt24+6rsPn88Hr9WJra6tjJYA+r1QqjBC/9tpr7F6mvk6lUnjttdcAALFYDNeuXevrL7y2tsYixfx1aLVabEIFACsrK0xXb3WxIFtDRVHYig3/bKDPeZLaq+w69SMANgGhCDpZ+83NzeF73/veI+tzqVIjrcrY3YterxcOh2NfHCo+ybafjxOmaaJabHaSXo781iuPifgKbeJLhJcnv76oBsXx4SS+/TAixSN8YjDooT5MFCMajeKll17CzZs3sbGxwV76c3NzrHgH0D8SREvf8XicWaoRMVJVFV6vl9loEZEiqQAVEqjVatjY2GBRHiJFBEr0oegSRVQBdPxsGAaKxSKeeeYZuN1uXLx4kUXdHA4HIpEIzp49iyNHjuCrX/1qX9uyS5cusaXffsu6VEHMGpHlf2eaJt58882+tlSrq6tsideuQAf1JxGiXtHbRCKBtbW1no4Iuq7j/PnzHcUfrNpToE1kw+Ewq1xn505CMgVyrCiXy+y6krPFrVu3sLa2hkqlwqog8slflUoF169fZ0lRfFIbjYNGo4F0Ot0zWYv6mMbAIEu6CxcuIJfLMT9qvr8pSY1swgYdq9fEgkARy35a8UwmgwsXLqBarUKSpI4+kmUZ1WoVFy9exPz8fF9/4d3dXbz77rvQdZ1p7nlLtmq1ijfffBPVarWviwXZ2gmCgHA43NWHlUrloWUI/az9yOf6UfW5JGHhV1wINLZJl0+VCB/VX/iTavv5qDBNE5VCoyOxLUc/J6to1vTBO3kfEATAE1IRiGnwR13wxzQW8fVFVMjKR4/49sOIFI/wicCwpvHDRjF4PW2v4hC9IkH5fB53795FKpVipXwpyletVlnE0uFwMGcIO1sqsrYi3aE1aYlIMVlNUZt4MidJElqtFpaXl3HixImeRH5jY4PZltkt17pcro6SzP2WdcmD1u/3M0LLL/2rqopSqTTQlop8bKvV3okaVJ44kUiwKChpcil6SxFzIlRWS7pWq4VisdhxHkTYeAJFS/R8EhlPjIA2yd7d3bW1G6tUKhBFESsrK8zFhMgNHYOWsWli18uLmcYDVVWksW9XBGZ2dnagJR2/MkLnxI8jcvq4ePEiWx2xO1YoFGJ+2L1AfUNWaNYxJEkSizRTv/OEjkheIpFgkpRe/sKrq6solUpsnPETNd7+bHV1FZOTk32lCjT5sutDSg4cZA9ohSRJtrZr+6XPpXLiNMb4CDe/+nDz5k3U6/V98xfulaz5SYdpmijnGozo5vcS3HJ7Ed9W/TERX1GAN+TslDjskV9fWIOkDOf5/nHAiBSP8LEHn5Q06KE+KIrBk2u7lz4fMekl1ahWq0gmkywBzmpJVqvVkE6nGWHq5RrBV5cjWy0CEV4iCKQNJJJB21Ni2aDyzHxSWz6fZ64PRBw0TYOu6ygUCsxBgpaXeSJC5ETXdQQCgY7Mf/LvBYB4PN51TjyIZPCkyRopB8AS+6jAiTXqSgmMFG23Iz1EDPjxZB1fPCRJYhMevm1ut5tV6qP/211X6l/Sd/MSERpvdF1p8mM9dyLahUIB2Wy279hfWVkZaElHkytexmCVqui6jtXVVWbdZncsknsMAq3A0KSGlw6RDpb6FQD7P/+7VquFWq3W11+YkjwFoX/5ctpPr/6ha9HPjo7s3fYD+6XPLZfLbJJIkiwC9YtpmshkMgMnBSN/4eFgGiZKuXoH6eUT3VrN/paF7xeiKMAX1eCLaO2oLxf59YZVSNInh/j2w4gUj/Cxx8OaxveKYjwMuSatqp1UI5PJsLLMdlFQ8iElDWE/Wyoim61Wq8s7mAglRfLIRYMnGHx5ZrvErlAohDNnzsDjaWcIUxEQntg0m03UajU4HA7mVpDP55m1nVWuQPpPKvZA7SayQ+WqqVBIL1sqimx5PB5UKpUuQuNyuZimkizsiCARYbfKSuxg9/teiVS0HN1qtWwdCDRNQ6vV6lvkgU+ItNuGvlsj0HwyGkGWZcTj8b5jP5lMsrHgcDi6XExoLPLnS+OHP3/TNG0lBtZjDQP+GHb7ogixNRIPPJAOUV/0kxiQ5r5f+XIi1Ptpa7cf2A99Ll890a7sND2L6N6xw8hfuBuGYaKUqXUktBH5LSSr0FuPifjKAvyRPXkDSR32yK835IQ4Ir4DMSLFI3zoMWzGc6/tHtY0vtd+eHINoKMcrtPpZOR6dXUVt27d6lkhKhwOd2horQSLJzf9Ik+UgEVV1Oy0oGNjY8hkMiiVSl2RUNIL+nw+iKLYN7Hr85//PIvSEXkjEAF0Op1YWFjA/fv3sby8zGydiBwUi0XkcjkcPXoUtVoNiUSCReOoXfV6nRGrSqWCYrHI/t5K1FRVZRFKmmDwSWv0N0QSqf3WxDaKvlIk3SoN4BMP+WtgB6pERzINfntKECPiTNeItwCjBD2SvlDEnSeJfBIfSRHs2ubz+TA9PY319fW+kXsau9lslslF+GpuhmEgGAwyuzHqS+vxiDgNOhaPXpML3sOaPqNt6fc8gbNqYWmyQzaCvWwNDx48iHg8zogrfyy6/k6nk23Xaz8Pa2u3X4hGowgGg7a2fTx62ejxdoS9niHBYBBer3fkL2yBoRsoZmqcjVk72ptLVFFIVWHog1dE3g8kWdxLaNM63Bz8MQ2eoApRHGmzHwUjUjzChxrDZjz32+5hTOMHVSOjxDVeF0pR0FAohFarhdu3b/etEEVRW4rMWItXAGBknLdys4s8LSws4M033+xp/3b27Fnk83m8/vrrTDfLE3JFUXD27Flcvny5b2LXm2++2UVgaD8EURQ7ZBi9opwAEAqFkEgkGMnm2yRJEiKRCKampvDtb3+bZcbzkGUZTz31FN59911G4qyJe/RiJ+JdrVa7ttE0jTmHUFER+ownezMzM8jlcsjn8z3HqsfjYZpRijDy/UU68EAggK2trY4lf7pmiqJgenqalZYulUpd+1EUBdFoFLFYDG+//XZXhTgi9k899RTzoi4UCsjn87aRe0VRcOjQIZbYyF8Pkg3Mzs4il8sx/bZdVTqKLBYKBeRyOSa5IXs9v9/fMY7pfKyQJKmjCIj1mtEEolAoMA9ju/N3OByYmZnB7du3mV8xtUdRFIyNjbHPd3d3O64HQRTbXuIzMzPs2dDLRg8YztZuP2FnkXfhwgVmkddrG95Gb1AxkU9/+tNIJBKfSH9hXTdQTNc6CC/9XEzVYBiPh/jKivhA3hDVOpLbPAEnhBHxfWwYkeIRPrQYNjlu0HZnzpwZKiml0Wjg0qVLfauRtVotbG5u2lZ/qlQqLPGLEuTsKkSJoohAIIBMJsMS3XgSqus6YrEY5ubmsLGxwTLnqRCC2+1mUZ5PfepTcLvdA+3fxsbGOnxYgTaxHBsbg6ZpTOvZL7FLEAQEg0FmOca32eVywTAMlkQ3NzfHSBhfwMHn86FQKKBer7PqZlZvVJfLxUpq9wNFVK2kiK4rkSPykbbbhpLbnnrqKaa9JXIOPEgifOaZZ3Dt2rUObTUPkmtQRJ72wUcdKfKfTCZtCRjQJtPZbBYLCwssimxHCsfHx1GpVHqSLFrZ8Pl80DQNy8vLbOLHj8dcLoeFhQVWkIb6jR8jsiyzVQ87Ekrn73a74XK5sLq62iWxqVaryGQymJubY2Og1358Ph+i0SiKxWLXfqgfo9Eo0/ryunAaj6RzVxSFkXTeMYP061TAgwrGWI/lcrnYdnZRcuABsc9kMgNt7R7WcqzfKtmlS5f6WhYSBm3Dk+dez5BQKPTE/YWHXSF8VOgtA4UUF+3lqrYV0zWYj4v4OiX4oxoClsQ2f9QFd8DxWM51hMEYkeIRPpQYVr8bDoeHsgBbWFjo+1BfWFhglkO99kPRvWq1ygoTAGBL97lcDoqiMK1qr0SqQqGA5557DpcvX2bkkI8Ca5rGikUUi0VUKhUEg8EOLSz/IhrG/s3j8eDs2bO2lc9u377Nos+9EruoaICqqvB4PCwpicofU4lj0tOGw2H4/X7byoCbm5vI5XIsUmtN2hOEdiERck6giQIvK9F1HW+//bYtseJBulCCXTIeVe0aGxtjVc3o3KiqWalUYlFiSr7iZRjAg2qB9DteF0z/J8lKPxCBo/6m607fieTfvXuXkWS79ty9exfZbLbjnrLeY0BbBrS7uwtVVREKhboiquVyGTs7OyyKTzIkXtJAGl6qiGaaJnN0oHNutVpotVqIRCLMpswq1dA0DeFwmFWvo0RUq5yFrk0+n2djhu8jqrB39epVVmyDHCqoD8mOrVgsskkbuZGIosj6vlQqYX19HaZpYnFxsUs+kUqlcPPmTSad6GdZeOTIkZ6OIVb0W7UKhUK4cOFCX8vCP/qjP2J2eb22IRu9QcVEnrS/8H54IvNoNXUUkjXO1aHKilmUMjUMkfv5vqCo0gNHBwv5dflGxPfDiBEpHuFDiWGT4zY3N4fa7umnn+77UFcUZeB+dnZ2AACapjF/VP4lq2kaWy6lKnHW/VCC3MTEBF555RVcvHiRRZfowX/27FksLCwAQEebaRu7F5Fpti2xCoVCR6ST70dKqKP90P+pVHQv9wX67nA4UKvV4HK5WBIcAGYlp2kaYrEYNjc3+1YGpD7ifYEpskbLzhQBJCLEgwgY+QZbE7743/HE1LovIluk956cnGQaUUqe8vl8qNfr2NjYYH7SADqi6UTMieARobQS8EEEnsfdu3c7ou1WyUM6nWaOGtSHBLr+tVoNS0tLqFarmJ2dRTab7SL8wWCQVaAjbTmNDfrSNI2VLafraY3uUx+nUimm9eYnO0Qws9ksDhw4wLycya1ElmX4fD4WoSwWi5ibm+uSYXi9Xvj9fhiGgUgkwnTjdtXqwuEwrl692vd6rK+vw+l0MmkNXV8a0zTJyGazbGLKg54NGxsbSKfTfW3tMpkMNjc3exbs4MEX97HLSwiFQsxKjsY1vypBVnL0c79tlpaWcOLEiZ4WkoQn5S887AqhFa2GzhLb8px/by5RQSlbBx4T8XVo8p6bwwOpAxFh1aPse/+M8HgxIsUjfCgxbHJcqVQaOolubGys50OdKlr12w+RtEgkgu3tbVaMgSKek5OTjCwPY82kaRrm5uYYSXQ4HJidnUUoFGJ/E41GEQ6He0ZwgP6awbm5OeZMYaeDDofDbImc2mbXZtJnrq6uMg9dgiC0s/OPHj2KmZkZbGxs9JWqhEIhJuWgSBaBIot2ZJj2YQURM7uENH7J2y5SSu0jEm7to1wuh1Ao1JFUKQidVQQpsYxIvl07eYeGYVCv11m5Y7toeyKR6LBIs5sQ0Hm1Wi3U63Wm4ea1ruQ2Qde5XC536GFprNIYp+1oAsgnOgJt31uHw4FKpdJVvtvlcqFarSISiaBYLHbIeWilYXx8HDMzM7h+/TpzHeGtBKm/iehTBUG7ktJUDIVIKt8vNCGiqDb1gbVwC0W/yRml1z1E0eN+z49KpWKrj7eCVnf65SXk8/mhrOToXui3DeUBDCNXEITH6y88aIUwsZvEO2/ewsEpA4VUraNqWyn7+NwvnG65O+K7R4BV94j4fpwwIsUjfCgxbHKcx+N5qMpOvR7qwxyPfE1TqRSLtBExaLVaSKVScLvdQ1kzlctl3Lp1C5VKBbFYjEVDqModr5e2RorX19dZpHiQrvDcuXNotVq4f/9+Xx00SSkosYq2IZeEaDTKtL52hI8s2URRHOifOj09jZs3b3YsMRMo+syTk36gv7UWcCBCzRdk6EWKKdLaTytO7SH5gJWA85IDIpo8CaHIHB9h7wdKxOwVbSfCaEey+YmAx+PB6uoq4vF4x2oARXLX1tYQDoehKEqHawa/TbVahcvlYolyFL0mSQJpxcldhFYrrCWDC4UCI/W1Wq2rEiHJQsh54t69ex3jrdVqIZlMolgsIhKJYHx8nMmnUqkUu9cpqe3mzZsd14gH/Y7uXRonvEyHxhMlxPa7h+je6/f8oITDQcjn82wS3C8vgcYCL5mhfiQZjyD0t5uj1aL9liu8X+TzeSR2U1BNPwqbQKNotr9KQKMItGoRpNDCCt7b92NLTkBUW5A0HYrHRGDMjYWn5jF3dBKq236yM8LHDyNSPMJjwzCRh17b8BWbetkgTUxMMMupYTOjhzlev/2sra11aYqBNgnL5XJwu92Yn59n+kJ+uToYDLIEua2tLVQqFYRCIWQyGdYe+j+Vee6X+Pf8888zXSEfuSP9crVaxbvvvssqx/XSQbvdbhw9ehQrKysoFosdSX2apsHr9eLgwYO4fPlyz0inaZq4evUqXnrpJaY/vHnzJlZXV9m5HTp0iCUrAg+itzwR4b+GAUUf7ZaHBUGA1+tlqwl2IM2y0+lEOp1mtlREGDRNQy6XYy4NlERltYijsUkyAdLR8lpp6vtcLjfwvA4fPtzXSmxsbAz379/vG3kWRRETExP4zne+0xHN5T18aYxGIhGWHMpH6KlP3W43JiYmsLu7i6mpKezu7rLrOj4+jkwmg7GxMTax4Ik1f6xWq8WkBnZ64XQ6jeXlZZTLZSZxovFF+ysWi0xGIopiT0syurd5ZxI+cs0fl5cJ8Z/zWmmqwmiaJhsfVIyFIsapVIpVqOT7kCKf09PTA699rVZjUWJKYOXHI01IyJrQurpDqyTUDiLO/ESOzs3hcCAUCr0vucKjoF5pYnM1iWy8hFpeR71oIp+oIhMvo14iF4v91ztoPkdb6hB5oO81lTqWVq+h1qTzb09I8/k4VraKCE27oLqf3MRghA8WI1I8wmPBMJGHQdssLi5id3fX1gaJPh8mMkkJacMcj/ZDpv58YhuRWdIUW6PAlO0+PT2NO3fudNk8VatVjI+PY2pqClevXkWlUsHq6mrXcvX4+DiSySSq1WrfxL/z58+jVCp1+OwSKOJXKpWgqupQbaZSvvTCFEURiqLA7/czB4J+KJVKuHXrFp566incu3cP7777boeko1AoQFVVjI+Pd/wdH1Gk62wnnbDD+Pg4NjY2mJUY/9JXFAUnT57ElStXOso0W0FRV0VRkE6nu8YaeQuHQiHE4/GeFnGRSAQOh4NNungiRhMXr9eLWq1m64ZBcLlcOHToEK5fv46lpaWuZLRoNIqpqSm8++67PV0sqO13795lE4JeEwMi+OQGQvpaIrMulwtut5uN6zt37nRoijc2NjA+Po6FhQU2TnsdyzRNRuYpMsyPfdM0O9wr7Ep4U4GTfD6PlZWVnpZkhw8fhqqqfcuAOxwOZrXYD1RdMJPJ2EqHHA7HQHvEM2fODJVkxxexKRQKXc8HikiTtzRN1PhJJk0WALDqmXYls0OhEK5fvz5UQaKHlQjUK822hVmy0uHlm42XUC8/nnLFAOD2OzrkDayIRVSDQ+2kPKZp4vz586g19//8R/hoYkSKR9h3DJMoAWCobXo9iPjfD5MZPWzyxpEjR3Dp0iXs7Ox0VHU7efIk3G43M+En6yLeboy0p2tra0yjzGe8t1otxONxrK2tIZFIIJlM2i5pb25uIhgMIhgMskIf1nP3+XxYX1/v0LZal0eBB5G+QW3mtaZ8BTlqF+ka+8EwDMTjcVQqFVtJR7FYxBtvvIFTp06xffP2ZaQNHpYQA4Db7cb4+Dh2dnYYsaFo+fj4OCOh/VCv19kSM7WLB11DOg87tFot5lWdSqXY3/FoNpsIBoNoNpvY3t7u2Z5AIMCkOlZtLrmbNJvNoa5HL+szHrqus4gjSWd4CQG5f9y7d69rokeEf3d3FxsbGywibNc26kfS3dN14sc+6V9pYgZ0F/igJLgf/OAHuHHjRk/pkGma8Hg8fUmxpmkDdb50j9H1tJJisv2bmprCK6+80tfabBiQhptcMax9VKvV4HQ64fV64fP5mCsOf/9OTU0BeFDG2W7yRDr8VCrV9znTr4RzrdzscHJgGt9EFbVy7wnbo8IddD6wMuN9fKMuKM7hpFfAw1c7HeHjjxEpHmFfMShRIplM4tatWwAwMDpBL8BeNkj8DL5fZvQwbaLjrayswOFwYH5+viNxZ2VlBcePH2fHn5mZQbFYZFpjr9eLRqOBer2Oa9euQdd1uFwuAA/0rkB7afTq1atsqdpa6pmWPnO5HCPvFK22VgcjYsFXbePlA0SGKFlqdna2az9EsDY2NmCaJhYWFlAqldh5eTwe5rE6DGRZHmgVde3aNdYuu+QmvrgD9Ql/bvQz0I6+u91unDlzxlaGsr6+3jeaCrQJbbVaZY4F5IxB+uBSqcS++mF7ext+v5/9nbUoC+0znU4DsLd203Ud6XQat27dQjqdhtPphMfjYftpNptIp9NYWVkZiuyShncQms0mFEVBOBzushMrlUodiXEkGeGP02q1sLS0xM6RH390rrxUgcgbL1tQFIX1Pf0tr52mbcn27ebNmwMtyQZNiMrl8sB+5J0o+o0Pihb3szYbBk6n05aA8/8nSU4wGMTU1BRSqRQb+yS5yWazbIXE4/F0SX7q9TpyuRxbLbJ7ziiKA/l0Cdt3soi32t69vJdvvTJYH//+YEJ0GnB4Bag+EZLWQlOswB1S8MJLz2NicmxfjvKw1U5H+PhjRIpH2FcMM/MmazNKVLPbhiJppIO1JhvZzeB7JdEN0yZerhCLxTq2o8+3trYQDoexvr7e0waKNH8UtbKSHpI0WBPD+PYQoaAs80KhYGvLRZFra+SOTzZTFAWTk5PI5/OIRqMd/Uha6WAwyFwD7t+/b1uJjIjcIKTTaUYQrESAXuRk30af81EuSlKivqGXEU+eiBg4HA5G3GVZRiwW6ziez+fDnTt3hmo3Ja/l83lbuzEqoNIrWmia7UIgu7u7tlZi1I+JRKJjBcFaCloURVYVkf6OHyNOpxPFYrFvpJnHIMkLD+s58+dJxVToczsNKxUA4a32rOOR1wj3agO/X/pupwOuVqsdGm5+QkhkddCEYJikR9pOEARbOYP1GIOszYYBkVLSM/P3h2m2K2KSnR5VNyTQfe12u5mUwjqRobHWjoID2WQR+WQFtbwBs6YATQfEJmDWFZitEN74/r1HOh9bCIDiAhwewOFtJ7mZSh2ffvl5OD0iVu7e7pC6TT2GxL+HqXY6wicDI1I8wr5imJk3FS941G2GncEP0yZyXhi0jDg3N8cqsrndbuYSQFE9n8/XUcKWJ75EFvhomdV9wZr0Q0vSqqqyCCpl+c/MzMDn83VIG2if9LPf78eBAwdw586dnprrmZkZXL58mbkK8C95vjrfMKDiBzyx4iHLMhqNBouG6breUaCC9JGBQACBQIAV6KBJBUktJEnCU089xdwKel3XQVFiApEPXdeZhIBWCUifzS/3A92R62aziVqt1kUaaf8UCTVNk7XN6nZA16Zer/cci8Ms+RPIq3YQOaTVBDvHFKfTybS/vbysiczzSVx2fcwnf9mVOKe+ofuaJhUEq+0dTZzs7Mb4c7aL1D6MTIfaTBNNIv80UaKS7PuBRqPBJkOtVqvj/qCESbfbjbm5ub73dSQSwe3bt9tjs9GELKgQmyrQcAB1BVrTCbHpRHFTQ1EXAXg7+2dfzsaEqBpweIHIpBe5ShKBMTdUnwDFDYjSg7FkGDLS6TxUv4ixsTGMTcQeuyfysAnWH8cS1iPYY0SKR9hXDGttBuCRtxl2Bj9Mm+il049gFQoFxONxRnyr1Sp7KYZCIeZPzL+8+IcsEQfgAUGgbQlEAOjlS9vy4EkzOR7QC5tfXiVyMzY21mFdZdVcy7LMIptWDSNpNCnDvR+5EoR2GWg6T16bTKAoaSwWQ61WY/3I23uRQ8eLL74Ip9OJt956i52fILQTm06fPo3nnnuOaZd7XVdVVZkPaz/QeQPoSP4ib15eo21nF0cEixLJ6PrRNSTrLpokNRqNjmPS3xKxGvTyH3Y5npde9IIgCHC73Szy2CvCTW20A59U2A+GYSAYDKJSqTAPZb4dHo8HbrebJTyS7ITfhog56Wt72Y1Zz/FRQXIPsmjjxwf/zHpUOJ1OuN1uuN1utkpktb8D0GVJVygUIRoKfMoYgvI4crcrcMYPQmg6gYYDgjG81vZhIIgCfGEVhlJDS6zAF9Xg9ApweADFLUAQRSSTSbiCgF5sQnU/mn3mvrZdEIZO1B7hk4ERKR5hXzHszBsAdnd3e24zOTkJ0zT7bmOdwZvm+7dbi0QirExxrwc2bRuLxVg0y6rPJa9XPunLbgnZ5XKhXC5D0zSWXMYnInk8HlbcI5/Ps2V9spqiSmsUaaX20zYej6cjmtivCEgmk2ERXsrG590OeD0sHw0k8BOKU6dOYWVlBYVCoUPzzJNBn8+Hz372s7h8+fLAEtaf//zn8ZnPfAavvvoqMpkMQqEQvvjFL7JlZN62z6rxLhQKOHDgACN0/UCJl0TUCRSpoxdlrwQ3On9VVdkYoAg7n6wWiURYVUM+CZOPsFISlV3UmRwZSCPKe+zy14NI+cmTJ7G8vNw3KkrevslkEgBYqW4i7KVSCfPz86zyIkVrre3mNdK9vJybzSazcCPfXZ6AS5KEubk5yLKMeDwOl8vFEu9IPtBqtRCNRpktHC9psd5nNJaJVBJ4H+tBEWP+uG6321ZTzMMwjEfSFPPPq+np6Y7S7KFQGMmdLAJaFFs3yigkqyglAqjuyiik69AbBuLQAWy1zw+BoY/bD6IkwBfpTGhrV3HT4AmpKBbbyY0ulwtOZ/e5ko+1x+NBLpd7JPvM/caTLmE9wocbI1I8wr5imJn3sWPHAACFQqHv7HyYbegh+TB2a3b7evbZZ7G8vNyXONODnaKmVp0zLYmTVZLdcqogtCviHT58GJcuXUK5XGakgUiEoih4+umnGXHIZrOoVquMYBmGAU3T0Gw2oaoqarUa0uk0I0ZUwczlcsHj8aDRaPQtApLL5VgbrDpMimhTFK9QKHSRCJKJhEIhmKaJc+fO4bXXXrNd4lcUBefOncP4+PhQJayt1fp2dnawvr6Oc+fO4cyZM1hcXMT6+jquXLlia20XiUQY4e0Fivza6W91XUe5XIbH40E0GsX29nbPaGgsFmOrBZSYRUvg9XqdTTD8fj87Zyso2S8ajWJ9fd1WzmCaJg4dOoRcLoc7d+50OZgQATxw4ACLvPbzRna73QiFQrh161aXVKZcLnckMq6srNi2WxDaya6pVKpjRcR6bgDYOOk1ITp27BjGxsbwrW99i8k/gM4JwdNPP81cL6y+13Qsyg3Y2moTRDvyG41GUSgUmEOF9b4HHiTvkY+y1bKPv8+Wl5eZ+wTvXvMw7hMwgdmJQ7h7dQdb1zdg1hWITRViS0OiZUAwQ8hAxyqWh9vfsBBMmI4GTKUOwdmE6hPhj2nQpSpeeuUzmJgc7/mnwyasURXCR7XP3G88qRLWI3z4MSLFI+w7hp1579c2w9qtnT17Frdu3cLOzg6LKE5MTODYsWOMCPcjzkePHsW1a9cGyj5IX0jJcQQ+SY4KfPClboF21G5sbAwHDhzA/fv3WSUyvspctVrF6uoq88Utl8uMUPPRMnopk39wr/4ZHx9nUUf+74EHZZ5pib3VavU9L3qZUGTNSrDpcxon/V5Eg6r1AWBJgFTcgIgR6bxLpRKcTifLvLeCdLyDNLrZbBbz8/NdzhgESZKgaRqbnPD6ZzqOabYLT1CSpJWAiqLIyhMvLi6i2WwimUyiVqux8xJFEZOTk3j++ecBtKUevB0d0J54TExM4JVXXmER634olUq4e/dux8SLQAltN2/exMGDB7G6umpLeCVJwvj4OEvItBtHNEnw+Xw4fPhw171Ilejovg6Hw4xs8ZOdcDjM+sntdrOJHPU1jVVN03D06FFks1lUKpWuNrtcLhw+fBjXrl2D3+9nUWv+nDweDzu+nUUc9ZEkSdja2sKbb77J8g3oPksmk3jttdcAgBFj0zBRytWZi0OOc3QoJKtoNQ0AETgQ6XvtHhamYABKA4rHxOR8FGOzQSytXkOpmYEpNaCqnROwVE1EIBCA5lL77nfYhDWr5ONR7DP3G09CrjHChx8jUjzCY8EwM+/92MY0h7Nbi0QGv1wGkflIJILt7e2hqt4JgoCJiQlUKhUW6XC5XCx7fXNzEx6PB2fPnu1YHqVyy5ubm8jn812WU2ThVqlUUC6XWfIWvTh48kDL/Zubm337h6IxJJ+g86FtabmYSM3k5CTTg5KmmeQdHo8H3/zmNyEIAqamplAul9l2brcb+Xwely5dwpEjRxiJtXsR6bo+0NrtzTffRCAQgK7riMViXVKNfD6P1dVVRlo0TWPaZNJcU9W5QS4EjUYD8XgcHo8HpmkyuQnvjJJOp1Eul9m+rfIB8kOmUtETExNdEpxKpYJSqcR04P0mcQDwEz/xE7hx4wZWVlbYGDp69ChOnDiBaDSKu3fvDmVJt76+DgDwer1d7a5Wq7h79y4ymQwrO22t1meaJuLxONuntaIdTyp7lTrmdclLS0tQFAXPPfecrUXg+vo6K1wzNTXVNR6pIl4ul8P4+DiazSay2WxHdUlFUVhOgKZp8Pl8rPS0LMvQNI3ploG2rISi3NZx1mw2cePGDdTrdeaYY5qAAhe8ghfleBPf+60l3InUkE/WUEhWobf2J5XNCkECvGEnwhNe5uPri6io6DnoYg1er5dJOgzDwPJXL6CWLCLgta94yeuYe+FhEtZotexR7DNHxTRGeFwYkeIRHhuGmXk/6jbDmq/fu3cPN2/eRKVSQSAQYCRyd3cXhUKBRR8GkfBBMgyqeqcoCrLZbJekgQjU7u4uAoEAJEmytRKjKm0Oh4O9pPnkN5JqUJIa+czSMi8lIDUaDXasXv1DWlJyg+AdBmgpno6taRorHuByuVhVMtJGLy8vI5PJsOVSPvGQSHcmk8Hm5iZmZ2d76gaXlpYGWruRhpjK/fJRPopek76XIvd0PUhnapc01wv0oibnAWuZZ7re1F6+3VZySH/ndrs7tuEx7JKuJElsdcLaX++9995Q50bSBSLCPBRFYRIdv9/P9PTUf6SnL5VKTEdu1Tnzk5pms8kigXb34vHjx9k9TefGg+RJfFENu340DAPZbJblAExOTnblAFQqFfh8PlYiuu3N+6BMM7WRVhto7JNGvFatQZW8qKUkVPM6VGMOjaQTRk1uW5uZ7b5UADQArG0PZ204CKZgwB1UoCt11KUmTK+CmlNAXmihhQom/E6cemEei3OTEEW6nuGu/RQKBTYB6FXxkirr9XtOP2zCWq9n+qiYxggfNEakeISPNIbRshUKBSwvL6NSqTBje/ISjkQiXYVA+pHwQdFka1KPVT4giiLLXO/XZtKO+v1+VKvVropVmqYxLSQtV1KBE4p8AWCfD7K/I9sxa7loOh4de2ZmBul0uqsyXjgcRrVaZZFwSky0VuOicy+VSn11g6Rd7mftxi+t5/P5ruilqqrMYo1fXifQ7x4mCaof6PrSNbbKEOgzujZU8YzXApPzwDD2XrTMXC6XoaoqXC4X013TRO9hig706gciuERCc7lcVwVETdNYwRo+2spP5BRFQTAYxMrKSt9I4PLyMpvkkdbYWriG9PQk57GzkSP7v345AMViESdOnMDFixcRj8e79P1utxsnjp/AvdubiKhB5BMV1HcB1JS2tVnDAdMU0ET7ZWoC2LcCxqIBU6lDl2sw5Boqso6kJGFbcmKlLqIEB5r1PeJY4VcD2uf5j669C4d0DRMBFbMhFz57JIKf+tQUYt4H/UBa90EVL4cZR/uRsDYqpjHCB40RKR7hI41htGy0fOd0OnH//n3bQhgPE33oF8HLZrMsKaxXdbBarQa32923zfSSB9pLk3bVqGj5k7yS+eVnStgju7lBx1IUhe3HSi6pfx0OBxRF6VkZT5ZleL1eJkWg4gPAg4Q9epnx0UI73WA4HB5o7UYEJpfLdRB5ipyTVKFerzPyZi2mQtdnGJA/MPUnL1UxTZNVMKToPU8cZVlmEblAIADDMJBIJFAsFtk2mqYhFApBVVU4nc6+k4ZIJIKlpSVks1kYhsG8qmlMNxoNLC0tYXp6Gjdv3hx4bjyRty59k5+wKIrsePwEo9VqMb/oQCCAaDSKeDyOarXKzs3tdiMWi0EURRZ17BUJzOfzANpRTPLOtt6vVLiFJr1WMufz+djEYpDONRKJIBQKo1EyYVTlvaQ2FZKuQUl48PZyGaYe3Pur/trah4XslNouDnuODv6YhrpZxBs/+BayrRbWmx7kDAW1ZguaUUHQLGK6VcTTQhFOtNCUJLTQ+RU3g1g1J7FthtHQgfV0BevpCr6/ksIvv7qMlxei+OOnZvD5xRh7fvareNlqtYYuXvGoCWujYhojfNAYkeIRPtIYRstGpDeXy7EHPEWVeJL6MNGHQbIPngzxvwPANI35fJ5FrvkS1oVCAbOzszAMA6lUiskiCFRNLxAIsBK8pFPlE83ovEKhEKt8Zdc//LH8fr+tZjISiWB2dhbxeLxnZbyJiQlMTEywCJ1dshVJKHZ3d/tGC71eL9xuN4s2WaOujUYDXq+XXUNrX5N1mKZpTFPcS+cLPLBk6wUiDbdv32b7I8kGeQ7Pz8/vaSKX0TAAXXKjIcio6CLKNUAXJIx5xrFVdmBnNwEIAYhiGIoEKIIBqd7E/ftFHD0QwGqigPWla2jW7CcNx44dY7ZfNDHix7Qoitjc3MQXvvAFlqzYC7IsM+cIu4IahmEgGo2iWCyyhETrCghFuWdmZpBIJPDUU0/ZaoG9Xi9zcOkVBaaJ3/r6Ovs9f265XA5Hjx6F2+3G7u4uZmZmbMvAW60fYQLNCtAoAfWCiXxSh2wE8Mab66gXx6GZE119sx+qX1PQYcg1KB4TJ88cR3DMxQiwy+eAAADlJMzkMrZXb2Jr9T08U7qJcSQxJ8ThFarA+7BArpsK7pnjWDUnsGpO4E3jOC4Zx/DarQReu5VAyO3ATz07iZPuEPK5/vf1wxSveJSEtVExjRE+aIxI8QiPDcP4TA7j6dlvG17LlkgkupJ73G43jhw5gvX1dTSbzY6yubIsQ5IkFq2jiKSu61haWmKEenFxsUt72uvcqBpVuVxmpICPutLy+NzcHG7cuIGlpaUuB4JYLMZsqV577TUkk8muSLHH48Gzzz6LN998s+81EEURCwsLuHXrFuLxeFdCksfj6ThWNptly+WCIDD7t7NnzyIUCqFYLGJnZ6dLU0r91Gq1EAqFkEqlmK6Z35csy/D5fEin032rB2YyGTz77LO4cOFCR9ELItbkwfvuu++yfVvLXdMYIYkCX+CEIsWkgR1Eir1eLzRNY5ZrzWYdgtmCYDYhm004xSZS792Ho1XAfKsMp9CAigY07H0XGhBgwNwWYUDY+xJhmCLKcKIEDSVTQwku5N/V8PV3X0XCDKAkh1CRKmhKHgRcMmaDfniLRdSMB1Fip9PJJnzkY02e2a1WC+fOncP58+dtreQEQcALL7yAqakpvPrqqyiXyx1RcIryPvfcc/jud79ruw/+dzMzMyiVSojH42xfiqKgXC7D5/MxBxfy3s7n82wc+f1+piOm+63VanXcQ9b7vlAoIJFIcHIloFUBHPBAKcRQytRRulNHptSEXhUBk3+2aGgLHgDg0ZK2ZKcIwdlEpZWDLtdgKnWYjjoMqQZdqEMQBSwuHMULn1MgpG8Bqdswbt1Cef0dyLl7UPUiBABTe1/YB0WPU2hiUbiPRdwHAPxl/BcUTA3fNU7i2/opfKd8Er/+Rw2osohXplw4pyfgkLqfn7wWeJhn+qNs87Da5CeNYc7tSe5nhP3HiBSP8FgwjM/kMJ6ew2wTjUYRi8U6/GxpGfXcuXMIh9sJJnYvdCusvriiKOL1119nvriDzo1IbyaTQTab7dp/MBhELBZjkTKg2+KJfr+wsIBXX32VZb8DD6QBqqri4MGDeOutt1h0x2pdRRHmUCgEALhz506X5dSzzz7LEgyvXbuGW7dudUUB5+bmOjxWV1dXe+4nl8shGAyiVqshlUp17SsQCDAHh37RwmKxiMXFRSQSCdy6datL53zw4EFMT0/jnXfeYbIM6/WlyDnJHex0uopDgegQURNrqLlrqDQr0IUWHEIJTqEMh1CGJDWQbzRwY6kGh1RFwF2FU6jDRJtKSQBE02x/hwnZBBymCadpQjZNSKYJwTShmiZchgmXYcBlmtBMczgqZgJ6U0Aq78d2LoJVcwJ3VyexjRgyQhBlSAiKMsbEGhxCnbmSkDb585//PNbW1nD//v2uXU9PT+Pzn/88AODatWu2UovJyUkmR+hV7IKSOsnC7c6dO13X/tixYzh48CBWVlZw/fp1NOp1oNWCaBgQDQPNZBJFScKhuTkYrRb8xSJymQxE3YBkGhBMEyIAv9eHVkXAblKCsFlBcauMVssJAx4AHhCjTGB77+j7U8lNkk0IYgWGUIYpVSAqdXhCCp45tYDJ2TH84OJFZDNJVDNrcBsF+IwSgmYBESGLMamIwPUUhGsPJl7iXmufJHxCFT8pXcBPShfQMkVcMhbxH/XP4/fXz+D7m06cdWxiRsxBkh48P3nry0HP9P3Y5sNaTGO/vJOftAfzCA8HwRyGKYxgi0KhAL/fj3w+P9Cy5pOEXj6T+XweLpcLZ8+eRSaTwWuvvdbl6Vkul+F0OvHKK68AANvGLpHmlVdewcLCApaXl/tud/r0ady+fZvZg1m3IbuwiYkJXL58ucsXl5J1Xn75ZRw4cKDvuZ05cwb/5b/8F2xvb/fsn8nJSSwsLGB3d9dWPkFLv++9916HzZUV4XAYgUCAnZc1aYvOy+/34+rVq11OEiQD+PSnPw0A+MEPftBhvUZSA7tteu3n5Zdfxn/8j/+RFZWwLn+KoojZ2Vl4vV6Iooh8Po9iscgmPF6vl91LoVAIFy5cYDpCIvykM37qqadw9erVrgivLuioSBVU5SrqjjqaShMlo4S6WEddqqMh1dAQa2iJTTREHcYHFKAR9oixxzDgNQx4DRNew4DHMODb+/LrBvzG3pduwG/oCOz9jiIaLVPEqjmBa+ZB3DVmEBeiqIpejDl1/IU/+4u4efMmu25WdF3bVguKrkNptSA1m5AbTThMAzORCBL370NqNCHpLci6DqmlQ9ZbkFqtvZ91eBwO1EslSLoOUdch6Tokw2j/bBhQBAFotSC0WhD7vHpMCKipQVS1GCpaFFUtiooWQ1WLoqqFYYr2iViPCqlVhauagFZNQqsm4arsfa8moDRLDx9PFkwIoglBhOW7CUHifhYBQWr/LNLvpfaXuLetIQG6pKApOWCqGlz+ABrNCiDqkBUBkgKIaEDVU3AKVYh7fy8MEXXeMUP4d60v4N/rn4ffKeFzvhR8qEBVVbzyyisIhUIDn+kA9mUbIob7FZXeDwzzThuG0O7XfkZ4eAzL10ak+BEwIsXdME0T58+f76kJSyaTGB8fx9raGlKpFPP0JBiGgVwuh0gkwrYnyytep6vrOsbGxvDzP//z+A//4T8gmUyyBCZ+qZWslvx+P0vysUvcMQwD9+/fR6lUYglTvCaWrJueeeYZJBKJnmQ2Go3i/PnzfZO3SNJARS6sqNVqyOVyWF4eXLHq6NGj0DQNuVyuq2QuJejduXMH9Xq9w2qK+pomACQvsPOX5ZPdaIJgtx9VVfFX/+pfxT/7Z/8MyXwZBVNFyXSiaDpRNJwomA7UTAWSJEJ1OtGo1wDThIImvEIVXqEBj9SAT2xiPuZHLbsDsV6CX5PhkrC3wi2gZbSQbmZRdTWR1LPIKxUU5BqKcg0luY6qtG/5/x9qeHUDwT2SHDQMBHQdwaaBSE1HoCKgXg6gZYyjWnRDqDvgbYlwNppQGg0ozSbkRgNyowFHq9X+udmEMsCveT/xgPjyhJd+fjLElye9WjX5/ojvhx2SAEE2IYk6RNmEIBsQJROi3P4SJBOiYkKUTOiyiJviHH4gnoDD7cakWoU/FkN0ZhqpchmB8XGYqgpTVQFFgQmwZzrwQL/d67k/aJuJiQm8+OKLQxHbJxVxHeadNky792s/I7w/DMvXRvKJEfYVw/hMrq+vI51Ow+1223rQulwupqOlSCRP1kjzmEwmceXKFeaLS3ZgvISAluIjkQhqtVrPpBwqiEGODVZ5gCzLKBaLWF1dRSgU6ulicevWrYFuBoZhIJlM9iwo4nA4bJe67ZBMJhEMBm0/I99Yslyz62v6HACLwlpBWmkAffeTqpr4H/71t/Bmchppw8U+96GEE+I6Pius4YC4i7BQQLhZQFgsICLk4Rcs1cYMALtAE8B9RcYdQcFdWcEdRcEdhwMbiozWJ+SlIbdM+CuAvwz4yyZ8FbS/qvSzCG9FgKcqwVtV4K7xctQWgM0PrvFoE9+6M9Amuq4H5Lcd/Y2MiO+TgG7C1IHWkDKSKSTxs/hO1++tTxlTEGA6nYg4nWgpMgyHE0fcbkDTYDqdMDUVprNNoB2yhLJhwHA6MRYKQS6VYOyRa1PTgL28hGFdgJ5k1bv98k4eeTB/NDAixSPsK4bxmSRS2m8bqpAmyzLThgIPrL0ajQbq9Tqy2WyHLy5vKcX74sZiMaRSKaRSKWYVVq/XWfKG0+lkUWZKwiIQOTdNkyUP9XKxsCsna4d2slaTeRLzmlrqn2FAZY/JEos0n+l0GqqqIhwO9/Xi5Yte9Fo0sivCQCgZMu42ArjT8CFpuIENE08Lu/iT0jU8Jd7DCWENs2Jy4HnUBWBFceCm04GbDgduORWsOBxoPmHyqxoGVNPc0wQDoiECpgLDlGFAggEZJmQIEAFBggkB5t4/A0b7u2DAEAzogs6+05cptPvS2TARKgKhkolgEQiWgUDJRLAEBEsmAiXAXwE8tQEN/hCgTXz9XLQ3hoqrHfWtqhEY0vuwThgCYqsKZz0JZz0BRz3Z/mokoNSTEPUHxNckK18JqD6o8QEBgGDuffE/732J9GXY//xJh2CaEGo1iLXaUEQiNOBzw+mE7nAg/s//BbLBACS3B6LHA9HrgeTxQPR42//3eLC6tQmxUsHk+DhMw4CpaRBVFdFIBEmL7/yjYr+8k0cezB8NjEjxCPuKYXwmee/bXtvwrgV2s2qKGlPFN4rY8vpSSZLY7ycmJhAMBnHp0iXs7Ox0JO2dPHmyw+eXr+gGgPnlkpQAQE8XC2uU2LpERvD7/UgkEsxejY84U4WuYQo4KIrCHBGoYhtNHNxud0d1LztiPKxHL7+9KIooGTIuVcdxuxkAABwTNvCL8jfxJfEi5sXeOmigne+/LUt41+nEu6oTV51OrDiUfY/+enUDY3oLEV1HSDcQ4r4HDAOyrqCiB1DQA8jqEaT1GOJmGDn4UBHcEAUJQbGGqNKAX5VYOWhrshn9nxL6RF2HVqnAVanAVa7AXSnDValAo69qFc7Gk5MpPAqaUrtORdUBlDU/yu428W04Y2g6ojDkGEwpAkF4PMS3IdWQV5OdX1oSeTWFmlzqYxrxeF9tgmlCNACJvnRAMve+G4CsP/hM1gHZAGTdbP/MfSn0vfXg/0rL3Pve/r+jufe91f6do2Wyn53N9u8drfZ28uOpHP1EINbrEOt1GMUiahsbfbf17X1ZYYoioqoKXVVxJxKBIxCA6PVC8nr3vnsgen0Pvvu8ED3e9nefD5LHA8FCWvfLO3nkwfzRwIgUj7CvGMZncm5uDqZpMg9eqz61UqkgGAwik8l0JZBZK2RNTEywzHdaxicQWVZVFc1mEysrK3A4HJifn2dEplarYWVlBUePHu3SLvPtpupsZOFFyWxWqyhVVVGtVjv+1g4nTpzAW2+9xRINqXBGOp2G0+nEmTNn8K1vfWtgfzscDuRyOWbNxfdPPp+HqqosOs+3k/qaJgd2SVhWSJKEesvAu80I3q2PIYgC/nvpa/hJ6U0cFnsnFhoA7igK3tKceFtV8a7TgWSPSnUPA69uYLrVwnRTx1RTR6wFeJsOaC0nHE0XGqYbRbiQNr1ImAFsmUG8gwDiZhBp0wdZEBAQq/ALNUQcTXjFMsJiHWOSDqDA+qh97b3Mpxi6Dn+tDnepCK1YhFoowFUqIdhoQsqkoVU/HOHdhgyU1PZXRQXKqtD+2bn3pQoPfnYCFUWAKfsgm1E4jSi0VhS+egz+WhS+WhiKYf+yftSpjJX4FtQU8moSOS05gPjuPwQTUAwJkiFDMBwwDQd004mW4ULTUGEaTpiGApjy3pcEGDIEyHCKIhRTh0sWoUiAQwY0yUTQocOsl+D1eXD8xHF87/z3UDdaqEkmDPHBSkILLdRRR0tqAU6galTRFJuooQZDGI7tioYJxx5RdnLf218m9/ODL19dga+mItSQMVmvYKyWh9AyYbQEmC0RRktofzUFPNGL8T4gGAakSgVSpYJWJoP3M/UUNK1Non1eSF4fRI8H8/U6yqYJZygE0+WCqWkwXBoMTUOxVkdsdhbuVgtmowHBptgQMPJg/qhgRIpHeF94FJ9J3hc3l8vB5XKxbahk63PPPYdr165hZ2cHlUql6wFCBQdEUeyw5OIjeHzU9Pbt26xYBMkTnE4nvF4vUqkU7t69y/x17ZwcRFGEz+eDpmmo1WpIp9NdllMul4uVni0Wiz37LhAIIJ1Ow+fzQdd1VKtVVKtViKKIUCgESZKQzWYHeufKssyquTmdTui6ztpK1aiSySROnDiBa9euMSkJ3z+SJGFubg6rq6sDrjdQCh7B724qcJo1/A35P+BPS9+GJnRHs00AG7KMi5qKS6oTb2kqMtL7t8VytlS4GgE4GyHIjTCEehRGPQKIAcSbwCYkNEwJBgTIQlvgIEGHLJiQYEAVWnCLdbiFJqJCAweEOJzYAh+YVp0qk+AYhgFB1+EpleAuFOEtFRGs1TGnOnHw3hocuVxf54THAUMQUFNV1J1O1FUnmpqGhqqiIiuoq07UnU40HA40HE40nA40HA44fT586Utfwtuv/wFy2RW4hV0EpC0cEMoo6zEUW5OoNcehN8ch5MegNKKQjP2t2EZoiLW9CO8D0pvf+15Vio/MteQ9Fw+3YcJlth08XAbgMBWIugLRUGGaLpiGC4bhgm64AcMFj9MLl+RCvVCHZEiQTAmiIHbYJVJBG7pH+Yk3TYy9Xi9eeOEFXLhwof1MlDmHm+KeW87ZVxAIBNBQG0gmk8y1xfqciUaj+NTxT7FJs8PpgC7pKBkl5Ft5tBwtzB6bBTzAdnkbu6VdbBW3kKqlYIgGak6gZjt/6dXJOoAy+59kenCwLuOlagpnalWcrDfgMk2YJmDqbYJ8VzqO9dCPoqarcIsiFmZnYVYqWLu1hFapCA2A3GzBrJShl8pwNJvwKgqMUgmNfB5CvQ5piJWwDwJmtYpWtQokEux3KnrXMiRZyJ2974Kq7pFqX/u73wdpLyo9I8lANoOaJMEZDEL0+dBQFBR0HZ5QCAtHjoyS7D5gjEjxCA+N/fCZpCQI8iCuVCqQJAnRaBRnzpzB0aNHkUgkkEqlmNaXL7qgKArGx8fhdDqZ3ICsyQgkRwDAqmn1SpArFAoIBoPQNA07OzsdCXsOhwPj4+PQNA31ep3phq2kmIpKRCIRlMvlnn6uwWAQhUIBsVjMVlNcr9eRz+cRi8WwtbXV8zoEAgHkcjkIgoByudzVHpJVHDp0CIVCAXfv3u2KXM/Pz+PgwYO4d+9ez6h2wXDij5rzqN838Jflr+G/kf4QLqFT91YVBLylOvF9l4bvaxq2lPfxaDEBb8uLYDOIg+6DcBVdCDQC0Mt6V+KjFmjANOOs73qBqtzZbmOaUGs1BMplBMtluHM5aNkcvMUCXOVKF/E1sd9FfoGGoqCmaahqKmqqhqqmoe52oenxoChJqKsqaqqKhsMBnsW73W6MjY2xIhnWhro1H2Yih5C9Z0BJz8GTm4aoq6i0NNTMB9dmP0UPhlhH05FG3ZFE3ZFCzZlGxZlCxZGCLhcgCgYUwYQmGPAYDRw0W1DqBpQaIMOEYrY9nhWYUI22zzPpu1XThGaY0EwDLqNtZcd+NkzkEUIcUcQRxS6iSCACR2gGsbExVvjHCo/Hg+nJaQBAQSsgHo+z/AGgPc7GxsZYyW0qylOr1TqccNxuN1qtFvx+P06fPo0LFy4gn893+KWfPn0aCwsLyOVyiMXa5ZV3d3dtnzN+vx9Hjx4FAFy4cAGFfIHta9ozjXPP2Xim11OoC3WUpTKaniYQBFJGCmuFNaxmV5GsDdb1E3TBwIrawIrqw7+CD7Jp4kS9gedrNZyt1vBcvY4F8wbmCsu47HoZzhf/X5g+17b1U/fas2N5Nxyy8ymOx2FUKnDoOsKahvmxMfhkGUapBL1UglEs7f1chFEqwygWYZRL0IsllBIJmOUy5HodQp/AwQcBs1ZDq1YDkvZ9bpdeTbUU0wCybvcDIu31QvT7IXm9kPy+PaLta//Mvnsh7W0jaNqIVD8iRqR4hIfCsFm/0WgUkUikr4fkwsICjhw5Ylutjl5MiqIw4stXRiPCSXZiVODC+kCo1+vQNA2NRgPxeLxngpzL5WKJGadOnUI8HmftHhsbQzabxdjYGKuMFw6Hbe3fgDYB76XVpXLKU1NTfRMuBEFAJpPpey3I37dXlTGKeN+9exdbW1tdWmnTNLG1tcUiYXZYbQXxbnMc/w/59/BnlFfhER5IA3YkCf/V3SbBl1Un6j2S+XrBoTsQrocRroURaUTgr/uhmO2kygl5AoVae6ICP1CtVtlLVtM0AEA2m2WSl15QFAWGrkMpl+HPF+DL5+HP5+ErFOAtFOB4jC/Upiyj7Haj4nah4nKj5nah5Q+g5HSg6HCgqmlo2YwBh8PBXFZ6odVqIeANI7NVRQARGFUJQsMJsaVCbKkQDBmpe0AKCQAh7JdKURZq8Es78Eu7CMg78Evbe9934BKzHdF36AAqe1/7hBYkJIQocto8btf92DQiSCAMXeh+lSmlEianpph0yAqyF6T7/sCBA9jd3WX3/fj4OHO2oWqQoih2lEI2DAP1eh1UDj2RSGBsbAzj4+Mdcq9EIsEcZyKRCJrNJp5//nlkMhl2vFAohEwmw+weB+0LaHv+lstlqKoKt+aGZmio5WpwN934mbM/w4hooV7AO5vv4Fb6FtbKa7hXvoc72TtoGIOjtS1BwFXViauqE/864IfTMHCqVsenqzWcq76B0LevYr319zD34s8O9dwfZptB4N9DPpcLTsOAXiigkkrBBWBxZhZmuYS1996DXihCM03IjQZQLkMvFqE0GnABECoV6MUi8CEi1ka5DKNcRgs7D//HigKJj1D7/J3RaiLRPv+ejtoPye9jmmvhEVb0Pi4YkeIRhoZpmlhaWmIyBHqIOZ1ORKNRFgGglwxVMOsHKuZgRT6fR7VaxezsbJe3sMfjgc/nQ7VaZVW0eIcIHvT7Wq0GwzD6lnn+9Kc/jStXrmBlZYWR2mq1ikKhgGg0iunpaWxvb0PTNFSrVTidTiiKwiQQmqYxbVg/UEJeoVBAPp+3jVzX6/UObbIdhslS1nUdt2/fRrPZZEl8BNJv3759u+vvWqaAi81ZzJob+H3nP8GUkAYArCoyXne58Lpbw3sPmRDi0T2YaE1gypyCv+SHWlMhidKDVQBJYFrvTCbDJlxUJZBHrVZj0XA+8c1Rr8Ofz8OfyyGQzyNYKMKTyTwW/11DVaFHI9BDYZRcLogT47hTKCCnKCi73WhyhJfGHBHefkmUPCEWDAmirkJqae0v/cH3jTUFMg7v+3npMNGQqnBKKYxJ6zgoL2NCXoNf3oFbzOBJBaIqcCIujCMrjyEljyMujmNXD0KHCEmQUDEqfWUXzWYT9+/f75tkms1m8ZnPfAaFQoERUpJyZTIZVlb4zp07aDQaUFW16x6q1WpwOBxIJpOoVCqIxWK2HrRLS0t48cUXmbwsk8l0yMvoeFSQaNC+TNNkpb4zmUzHM6TRaHQ8i31OH1469BJeOvTSg/4xmriduY2vX/g6riWvYdvcRl7KD7wudVHED1wafuBqT04jLR0vvvs38EPJ38Onf+QfwO2ODnzuD/Nu6Ieu1chWC7LbjcjcHBYXFxGJRNqewKqKaDSKah9PYAAw63XohUI7Gl0sQi8W9/5fgl7kvheKD74XCuxn88PiGNFsQk+noafTD/+3ggDR44Hk24tI732Jvj0SzRNqPkK9t73YQ0v9UcOIFI8wNJ6kzyIti1PVNqvEwDRNpNNp1Ot1pgW1S5ADHkSEBtWpsf699fyazSZkWcb09DTS6TTy+TxLVPP7/QiHw1hfX+/4G6ukgf5fKpWQSCTY+fCRa9JZ7xcqlUrXyxxoT0gcDkfX8nvOUHGzEcBfk38TX5Tewoqi4J96/HjN5cI9x/C+si7ThWlzGtP6NGYxi6AYRKnWLjBSrpZZNjZ/7cgrudFosOVq22TMchnhZhPK5iYC2Sy03Th8mQxcAyYSD4ua6kTR40XJ54U0OQXXwQMQJiagR6Mw3W4maajVakgmk9i6f/+Bdtsqv+DcSzrGhilCau0RX90FqaXB1VIh6RpE4/G8aEwY0OUadKkKXa5C9YmITvlQ0fOoNPKQdR1xQ8M1cxG7zU+hWkjisL6Bp81VPC3cwwFhFxNCGvKQCWD9kDa92DKjSCOIouBDRfAio0wAgVk0mq0HEgMIUFQFboejo4R6v/usXC6zypR28iKKVvaTe8myzJJ16RnEJ7RS3gKVOB/0bBwkL1MUZeBzdnt7G/V6ncm0rM8QURSxubnZ91msiAqm5CkcKh5CuBaGrutoyS3EpTi2hC1siVtIiklmI9gLKVnCN7wefKPwNuTf/jyeDx3DDx3+Sbw0/RJmfd0Bj/1Cv4hzLpd7qHeVoKoQVRWIxd5XW4w91wy9UIRRyHOkeu93xQL0fKGDUBuFwh6xLgJPsGhOT5gmmxSgj2yvFwRVbRNpknbY/cxHpunnQKDd9x8SjEjxCEPjSfosWu1rVMtNQ9XXSqUSezHZuStQAgt5HdNLkn+pOZ1OuFwuFiFeXFy0LfCxsbEBSZJQKpW6IrzUZl42YWcnRy9migRaiTr9fxg3iGFBiYl2IFJKuNMK4qixjH/q+if4rkfGVzzjuDNkBEAwBRxxH8F4dRyTtUn4m36oTpW9rCm63mq1UC6Xmae0tY+oP+fn57G2toZ8JoNguQx/Kg13fBfueAK+TAbyPi15tiQJRa8XRb8fRa+n/bPXh5LXg6bDwdoTjUZx9OhR26gjRfuoL7uuqwFILQ1Kww2xuUd4WxqklgpRf0wWTIIJSbELm3sAAQAASURBVDNQ1XPQ5RoMuQZdrsKQazCkOiA8sORzBoPwjAdQTxtAs831xx0NHPHWEA67sbZWw72khgvGGXzT/GHkdRVVU4LbLGNKSGFaSGJKSMEt1KGgBQUtyGjBIeiQoKNkasjCiypUNOBAU3DAEBwwRBVO8YFcmul5TQljDic0l7vD5cVOLtPvPqN7nyQ3/MS31WqhXq+jWCxidna2J8GKx+OMWJKMhz+21+uFpmloNptDPxv7ETqSefXbV71eRy6XY/kU1tWvcrmMXC7HZGW9UKvVWLSZ9hNAAAtYgGmayJVy2DK3sC1tI+VKYRe7fUlySwAuZG/hwlu38Ctv/QoOBw7jh2d/GD88+8NYDC3uu961V8T5SXsCi04nRKcTco+CTP1gmibMarVNpPP5NpHOF9pEulCEXsg/iEwTmS4++L1ho5f/IEBa6haXoDgMfD/+45j6R//wMbXq4TEixSMMjSfpszisfQ1pjWlZmieTRH7p4ehyuZBIJJh8gZYaw+EwKwEdCAR6RhZKpRIajQbW1tbYMWnpvlwuY21tDZFIpCOK1QtkJ0fSkHq9DlEU4fV64fP5BuqJre2jfrH7Hd8HdgRDEAToBrCh63g68K/xrqeKP64O93BXTRUHcAAT1QkcEA7gKy9/BVeuXIHga2ssSfcsSRI7t2KxiGKx2OGGwciKaUJNJjGeSuFoLo+5mzeB9XVI+xBJaSoKigE/Cv4AioEACn4f8h4PSpoGcU9LZ5Xg8EVjHA5H37EPUwAaDigN7wOZw57UQdSdEB6LnZUJ09GE4GxCUJuAowFDrkLUdARibjz11Al84xtv2q+CcNxG0zTcv38fuq6zKKogCCgWi6hUKpBlCT6xDp9Yx2Fk2KTShICyIaNsxnDVnIYoK5BFAaJgQpFESAIgwoDLrMErNmDqLRjNJiRgr74aTSI6m+Z0OlGtVuH1ejsmdOQC4fF4mIaf/8zu3gW6i87wf8Nva0ewSL7jdrtRKBRsxzTJYfoV5LE+G/sdj56zvfYlCAJznQEe2E/SfS3LMltFo/O0I+BU4EhVVdu+02QNE+UJTGACHskD02liw9zAkr6BO+Y6TCVnbX4H7uTu4E7uDn7t2q9hyjOFl2dexhfmvoBnY89CFB4uB+Fh8FHyBBYEAYLLBdHlgjI29tB/b7Za7YTEQuFBpLpQsCHWe9HqDmJd+MCj1KK/d8nlDwIjUjzC0HiSPovDWLstLi4il8sxiySSVfBL8fV6HZIkweVyYWdnhxVfANovynq9jp2dHczPz6PVai/V7u7udul8w+EwWq0WcrkcszKjSCe9iIhQUDt7JdspigKfzwe/399TGtLlKNCnn6j/efB2dE6nE5VKhUWNePJgCAYSgRw2pLex6c7isiAAA9KyXIYLh83DOCIcwYQ+gVa9hVarhYWFBczMzGBjYwNra2td528YBkqlEubn51nio5lOI5hMIpRKIZjOIJjJQNmLALeAIQvTdkIXRRR8PhSCQZizs0hoKuIOB6ouV4eDA8Hj8aDZbNpGjWg8OZ1OHDx4EPF4AgF3BM2SgEYRqBdNNIpANSdBr80hYM6/jxb3hwkThlSHIdfQkirQpTbpDY27kSnFoTjkrvLmzWYTXt2J48eP43vf+16XhSCPUCgEp9PJIobWfZFVIG8RyCzLYMIjNuFBE9N7EdlyuQxtLwueTxCrVhtwux9EfntBkiQcO3YMd+/eZUlkdH+R88ORI0ewurraldRqLZDDV8+0k+koioJQqG2qZRiGbdIv/9ybnp5mk2OHwwGPx4N0Oo3JyUmYpon19fWeBXnm5+eHejbS8dbW1tgKC+1L0zRIkoRIJIJiscjsHK3XDGhHQ0nr3MstyOFwsOuqKEpX8nCr1YKqqnA6ne2ggRLAUfEojkpHUdGB39pqoiG/gznveSxrRt8CPFulLfzmrd/Eb976TcS0GL4w/wX86PyP4mT05L4T5E+SJ7Agy5ACAUjvQ7LIotR8FJr/mSQf+YLtNuY+yNUk34frGoxI8QhDY1iiul9LZMNYu8myDI/Hw15U9BLnk5kouY68jOlloes6Go0GdF1Hs9mErussMceq0atUKnA4HKhWq/D7/axMM72IKDJRLpcxNzeHlZWVnue1sLDAqu/1koYQ8e4no5BlGS6Xq8MT2aqvDAQCiMViuHnzZof3albOYs27hg33OhoSyRB6Xzef6MOPzv0ovNteODIOmEb7vJtmE6IoYnJyEqdOnYIoiojFYrh27Rrq9TpcLhdL/MmnUgjn84jt7CBw4waaN96Da8iy2L3QcLmQ8fuQDQSRCwSQDwZQ9Hph7k1YJiYmMDU1hfXLl7vDkXt9cfz4cSwtLT0gxSYgGEpHYpsq+LD+moRyJoyUsbdRBx49a9uQGjCUGlSfAG9ExcbuHbTECnS52q4qsQdRFHHq1Kl2RcSijmq1ySobGobB/HRp7M7PzyPdJ/FmcnKSuSBYCSatYFCiZj7fOxHL5XLB4/GwcudEPomMyrKMYDCIWCyGO3fu9NzPwsICzp49i1qthkQi0SEBEEUR4+PjOH36NFRVxfnz523JviAIOHLkCJLJJHZ3d1EqlbqIkaIoiEaj0DQNy8vLzB6Sr3Z55swZLCwsYHFxEbu7u1heXu6qZhiNRrG4uIhMJoPr16/3LMhjTZrrBUEQuu4hctDJZDJwOtuTnXg8zla9FEWBLMuMRFMUulwu49atWz3dgo4fP45gMIh0Ot01wSCnjXA4jMXFRfzgBz/o8JUXmw38pLOCC7UXcWn9x/Cn5G/inPf3ccGt4Lsura83eaKawFdvfRVfvfVVxFwx/Mjcj+CLB76IZyLP7Mv740m/qz6q6IhSj48/9N+bjQYjyLz0o62dpsi0jfxjT28NAJLPu9+n9UgYkeIRHgrDENWHQa/oDH+8fvY9gUAAhw8fxs2bN1mCltXPeHp6Gjs7O2yJkKJHFMUxTZO9bHjSS1FmVVWRz+dBdnCapkHTNKZnJmIOALlcDk6nE+FwmEXeCORRTH20u7uLcDiM3d1d1Go1qKqK8fFx9tCmghy8VhV4sJQvSRIrJkLLp7SdKIqsXZSsVtWrWNfWcc97D1nnYImHqsuYrMzilalX8Jd+6i9BFEQkk0ncvHkTGxsbLFo2OzuL48ePIxqNMsson88HM5uFcOMGAru7CCWSCKTTEHUdJgBl7+thUPV6UIzFUJ2YRNrvQykaRX6PsFHkj3m+7kW96CVOJJGHYMhQDDd2b1Vh7IbhbUxCamkQdRWi2f1oLJWaeNQqE4bYgNMnwJBrqKOEOorQxQpMRwOa2wmfz4ejR4/ixRdfxBtvKLh06RL0+oNrT9UOjx8/jq997WtQVZXZgtG5U1JltVpFuVzuO0EDgNXV1Z7WfsCDpfdBJcFrtRpCoRBcLhdKpRLbJ8kxXC4XI5t+v9+WYPv9foyPjyMSieCll17Ce++9h5WVFXZ/HD16FMePH0c4HMbW1hbT8dtFt9PpNMbGxliE1Op1LssyxsfHsbu7i9dffx21Wg2KorCociKRwGuvvQagHU0ngt9sNlk0lewT+XHfbDZRLBZRLpchSRICgQAURUEikcDRo0cHEjF+XxR1pokOSb0omU6SJCbroWeaqqqM2G9ubqJSqSAcDqNUKqFQKMDhcCAcDiOdTmNrawuBQAA7OzsdLj40oWk2mwgGg3j22Wfhcrlw8eJFpNNp9tyPRSP4e2dP4UbJjb/5dRnfz53Br5R/Df9f8TauOR143e3Cay5XX9/yRCXBIshTnin8+IEfx48f+HEcDg52VuklCwH2/101QjcEhwNyJPL+tNS63ibG8sO+CR4vRqR4hIfGfvhMAhgYnSH0s+8RhLavcDqdxvb2dseLj8z3Z2ZmcO/ePabHs4LIML1Md3d3u16gbrebfZ7L5VCpVDpIFhFZQRBQqVSY16idDCOdTuPkyZO4detWV+RpdXUVsVgML7zwAjY3N1kU20qKqS2URGRXQU/TNLRaLawUV3Anegd35DtoCf31Y07DwImKG57Ss4jUJiCLMg7MH+hY3iRSDqCDFDQ2NpD63vchv/oHOHH3LrRsru+x+qHmdiMXjaIQiyHu8SAd8KOxp/+TJAkejwf1eh1NrgodgVYJRFFEo9bC5koKcj0Ex16CG2l9RbP9MK4kARWB991WKwyhCV1uuzq03R32EtykKiAZeOWVV7C6mkRxZwfNPZmNKIgs6ZMiWE8//TREUewghUeOHMGJEyfYkjnQXlngSSHQLjfeaDRw+/Zt28IVPCpctL6XkwN58fZDq9VCOp1mpJFPOiTiTqRsbGwMpmmiVCqxdns8HkZi8/k87t27h6tXr6JUKrF7qFKpMEvETCbDItO8NMLhcMDpdCKTybBJg1176HeXLl1iLg7VapXthyKvFy9exPz8PPL5PJrNJrN3JPlUPp/H1atXUSwWoSgKK0bEJ+HGYrGhnXnI5adfcZ/d3V12nelYdF7NZpPZQ+7u7sI0Tdy4caPj3DRNQywWQzKZZDIJa1Ie7YcQCoUwNzfHAgs0IQ6FQviZhSgOxTz4b//tZfxc8W/hl6Q/wF83/xM+Vc/hf8jkcFtR8F/dGv7Q7eqbuLtV2sK/vP4v8S+v/0scCR7Bjx/4cXzpwJcw4Zno2nZQESlg/95VI+w/BEl6X5KPx40RKR7hfaEfUR0Gy8vLeO2119hSIy3rJZNJFp3hifEgOBwOuFyurugUJeCRnhV4kIBH5ImIMlWr47PL6W8rlQq8Xi8EQWA+xPzysK7rKBaL8Pv9TKes6zqL2hEpuH//PiKRCG7evNle/rbR3ZI5v9PpRCKR6IrgUWJfKBTqICx8hF0XddwUb+IO7iAZGFzN6rlaDZ8tCiiVX0Ie0+3zQvvcr1y5AgA4e/YsM8wP+P3Q0mlI129A/O3/H+IbG0jv9Utg6KvWRkNRkAmH21+hEDLhEHRfO/mC+fUKAkSur/P5PJMLtJ0dXJAsfr5iS4NkPJ5EGkNotd0cpAojvRNzYazvrsAU+0w8zPY4S6fTtvZe6XSaJVlSX8/OzrL7I5FIoFQq4dChQzAMg02G+NLDNKYpqXFQhLejeT3cUOw+s4I0v3Q/8fcHEU4ikVSeXNM0dn/UajV2f7z99tu4fPky07rSNsViEW+88QaOHTvGCKp1okv3sCAIrBgHLx0iAl2r1bC6uopyucxWY/h+pBWI3d1dVKtV5HI5JlXgHVWIJJumyc6Lb3OlUmHnxevWe0U5eecEivzzIBkAtc9679P/m80mKpUKCoVCzzYFAgHmwmEHematrq4yGUYsFmPjkSQcZ8+exbMzUXzzr7yIP//v3sa/uv8lfNc4iX+o/HM8I97DQrOJhVwTfzFXwKoi41W3B78bGMd9obd8aiW7gl/N/ip+9cqv4vT4afzEwZ/AF+a+AK/DO3QRKeqPR7UIHeGTA8Ec9KQboScKhQJbBvT5PlwZlB9mGIaBr371q0gmkwgEAl02arlcDtFoFL/wC7/QM2ucYJpm26R9Z4ctEVqTYAKBAK5evcqyrO3M9yVJ6hsRM02TJQ9RZM2qUQTA5BiUoDLoWLQfPiEJeJDxTgTJ7liBQKAjexwA8kIey9oyVpwraIj9q1XFWi38VLGML5fKeLdxDlfFT8EUHV1tNk0Tkijihw4dQuPKFYS2d+BcuQ2x+PBWQCaAvN+PdCSMTDiCdDSCciDAEuD4iB5BFEUIECDqTogtDWJTbduatTRIugpRVx+Ls4MJnRFeXarCUNqJboZcgyDrTEnBryoM8ziNxWJMpkOEkY86RiIRzM3NIR6P2yYJJZNJ+P1+XL16lRHNrrbvjddnnnkGFy9e3Lc+GRZE1vj28PKOfvcHubpUKpWO6pa0X8Mw4HK5epZSJxDBNQyDJcZSG0gqQD+TFMHaHppkkwOGZimjS24YdP34ibDdef3SL/0SQqFQ3yinoih44403mITKilqthlwuxzTAbre7K0GO5BU0QejVJooSG4bRMUHjJ2qyLOPkyZMol8s9xyMVwhAEAfWWjr/3e7fwb99ch4wW/p/S7+CvyF+HInTnR1wNHMJ/mjyDC80VJFqDrbyckhOfm/4cDtYOIpgPYjw2PrA9I4wADM/XRpHiEZ44Njc3kclk4Ha7bQtKuFwuZDIZbG5u2la740FLjYqiMP2ctTpcNptliXW9zPdlWWZk1zTNrhcI8KCKmqIotsUAyAaJotT0t/yyNkVwSHpB5MH6YOeXTCk6SiC7LHKoEEQBW44tvOd8D1uO/qbromnihypV/EyxjBerVSTMEP4v8xfRFH0QBEDizttdLCK6G0d0ZwexeBzq+/D0bMoyMuEQUpEoUtEIMuEwmtzyKetn0wRMQDScEBtOiLyl2R75FbD/Fk4mDOhSDQaTO+yRYLkCQ2zYSoglSwIR6ZWHjS+Q9Z9V50xjNp1Os2QrAEwzTsvnPp8PiUSCaUjtiCGRnYex9tsv0DW1uj0QAW02m10lx4EHEU5KfgXa9w+/Hd1PdJ/3A+3DSnapjfx9ZY220jZENnn7M/6epvuebNL6nVer1WJ2bnx5ZpJ4bG9vI5/P48yZM8w5gWRYvGd6odAuf05RaWoDgcYhtZsIe682kXSNzp+25SPO29vb7FlsNx55aYhTlvC/fPkpPD8fwv/7P1/DP238NP6r8Sz+ofIvsCBudrTjZO4unircx+r8L+C7wRN4q3wZK+IKkjX71a26Xse31r8FAPCIHpzOnsZZz1lMOaZYm/eziNQInzyMSPEITxyUhNPPWL1SqQzUQgJgVZ3K5TLTZPKuEbVajSWcEfG1mu9TQgsdz+4FQi94XmdsJbL0AqUXo12ZZmuxDCshpn0B7Zc6TRyskSDDMFCoFbDh3cCKewUFpX9p6YlWCz9TLOGnimWM7RGB/9z6LL4vfhpBqQkBgLNex3g8gdjuLmK7u3APaQvHo+V2YzcYRDIWRSoaRS4QgGkhG4Ihd5ct3vtZMB/dxcEKsjSj6m0PtL5VGFLtoXLnekWeho0SAw9IFWlU+QQxiqLSCkAve0ByiuiXHAdgKM/sXufyMOdkd458W/iJJpFHqhDJT1BJEkNkmP6Wb5t1/4NA5NdK0vn9Wiev9HfUDl7iYU2e5Se8/c5LEASUSiVsbGz0Lc+8vLyMhYUF7O7uYmlpydbtYm5uDslkkj377AoS8VHsfn1NEwO786c+pzFpNx5DoZCtBOOPnZzE8Qkv/uJvXsGNxEH8scb/ir8q/2f8t9LvQuLcVCSjgSOr/xeivkVMxf4Unn35b2MTm/i91d/DH67/IYqN7nwJACgZJbxRfANvFN/AtDKNs56zOOU+BbfDva+FOUb4ZGFEikd44vB4POzl0ctYnZKpBsHhcDDJBGl+gQeVnYrFIisXTY4QpLGTJAk+nw+BQADxeBzAg5eclQDzyVz8cir/IiGyTfII+pxA+6WXEf2Ohx1B5km4IAgoSSXc0G7gTvgOmmL/qm6fqVTxJwslfLZaZaZhCTOA/7X1p6HBicXEfYzt7mJsdxdBSzGEYVB1u5EaG0N6fBzCieMIHD+Oi5cusdLFSt3VSXy5BLf9hiE2Oggvn+iGAeWIhyWAg0josCA3E55UNRoN5ngCgNkDyrLMCCXZA9IY69cemqANA76YCz92+Sgk6bsHoRdhpd9TIR2rraHD4WCTRmtyKX9eDws6Lt/XfISYVn548kpEmj4HwPqGjyjTShPlL1DFRrvzomOS246d9aMotsszT01NdbTDGnFXFAVutxuSJCEejyOfz3ckLJKvOiUQ92oT3xd8YqC138jJopddZSAQsH2WH4558V/+8mfwP3/9Br72zhZ+ufXz+LZ+Cv+b8i9wUNzt2DZQWMKPFP8OGuNlTLz8P+L0+Gn8zbN/E9/b/B6+efeb+P7W99Ey7PX6m81NbGY38fXs13HceRzPiM/gZeXlhxwpI4wwIsUjfACYnp5mujpK/iBQEkg0GsX09PTQ++ynHZMkCcFgEIlEokPawEdvKDucj8by0TJrxKgXQaCXI2XJWzWj1WqVuULwn/HnT+dDbhn0Mks70ljyLGFT2+xbatWrm/jpUhF/olDCbEfSIPBq9nnc2DmILyf/CLFkEvJDlpOuuFzITk0iOzWF9MQUamoIqDvQLItQUz4IVwMIJk5D0h9PLXtDaEGX2969grOJOkptqYNUgynan0v7GvbeJxG/fqSPl7H0Iw8kEegFURRZMRUrEaf/O51OKIqCbDYLSZJQqVQ6CKqu60yPTH9np981TXOv4Ei898nvwe/3M/cB3tpOlmXIsoxoNIqtra0OMmmNJrPExz6dTbIQ0vpbV0ByuRyCwWDHKotd9NLap/SZNZLMF9Wx62uHw4GJiQnE43Hout5BQGliPTY2hmw2y5Ia+ZUmek5omoZQKIRUKtXzvKLRKILBYFdZZaCzPHM2m8XS0hKq1SrcbjdbWZMkCW63G9VqFVtbW1BVlTmT8OdGyXtPP/00TNPs26ZYLIZsNssIuTUqres6vF4vnE4nu2bU3zRZy+Vy8Hg8PXWaLoeMf/gnTuKFQ2H8rW9cw5XWUfx44+/jb8i/hV+SX+3YVjKb0M7/fWD1W8CX/w84x47jC3NfwBfmvoBcLYdvrX0Lv3P3d3Atdc32WAYM3KjfwA3cwLe+/S385KGfxE8d/ikcChyy3X6EEawYkeIRutArK3q/IIoizpw5g9dee63DDJ4szMiLdVCSHdCOrpEXb7lc7npZU8JaIBDAnTt3mNsFRap4M/yNjQ2m+bOTOFBkhqIy9Bn/s9frZUb7dlXpHA4H/H4/HA4HMplMR0SKP2YkEoHH48G99XvYVrdx238bKWeqb1/MNAT8UiGFL5Uq0EiC0RBQ3nUiueNDeteL+eo25rE9+CLtoep0YmfyEKqHn0VCjaIueFmym7DjhLKnPaDYbypbhYRHI8SmoENwNmE6GqjqubbMQanBkGswhCbMvcIZkUgELklCPN5b5jE2NgZd1/tWdAsGg8zRohe8Xi+mp6dx48YN22smCAKOHTuGbDaLeDzeMRHiJ1XhcBgAUC6Xuwgm8IA0k+682Wx2kD7StZOGlSfAVvC680EYGxtjFdR4lwKKnp44cQLRaBRXrlzpWEXhz//QoUO4f/++rWyIoKoqDh06hFKp1PPen5+fZ7aIdN79wEdR+e0pakqk0no9yPf36aefRqlUYoVK+GQ8p9OJp59+Gm+//XYHOeWjuERWn376aVbggqKp5HdOzzSK2PYqqyzLMqrVKnZ2dlhVTL6iX7lcZtHkcrnM+pqXhJBsIh6P44UXXuj7nD137hzy+TzeeOMNJrGgY5EDxsmTJ7Gzs8Os8PhIcb1eh6ZpkGWZaeXtIAgCfvb5GSwGBfz1334XSzkn/pfWf4Nv6afxD5R/gVnRoiHevgL82g8BL/0N4DN/FZAdCKgB/Nziz+HnFn8Ob997G7/x1m/gSu0KCqa9dCxdS+M33vsN/MZ7v4Fnos/gK4e/gi/OfxEex+AVyH5jbWTv9vHGiBSP0IFhvB/3A2S3Rj7FtCQcjUa7fIr7PYiI9Oq6jmw221G8w+FwYHx8nFXi6mesT5XoqAgID5I8TE5OIpvNQlXVDm9UoP3QpxcdvVjsXuaSJMHpdGJqagp3795lyTJ8tC0QCGDxxCK+m/4uvj35bRRle01du3HA81URfyG/gzO1etvyKycjtaOitO1ENe0AzHZfedGbrDRlDVUthqJnAqnIPAreSTScEQCets63CaAJ9HYYfUgIJkRnC3WhhJZY3ktwq0DUdEzOxQC07a1Qr6NVr3clGlLRhmAwiEajYaudpeppHo8Hpmn2LKZy8OBB3Lhxo2/SWr1exwsvvACHw4F33nmnIyIqiiI+9alP4Utf+hLOnz/fMRYJDocDwWCQVTsk8mUdQ6IoMg9iSjqztllVVUbgBEGwrXooSdLQ0gmgLY3w+/227SabwS996UsA0PP8Dx48iJ2dHbhcrg7vYwJfvOOVV17pee/Tc4dcEeyuB527NeEVQMf9HwwG4fV6mYc5v4/JyUn4fD5MTU3h9OnTePPNNzs8kT0eD06fPo3JyUlcuHCBTXatMgQqnjExMYHTp0/jBz/4AZuEUc7C6dOnmU6Y7MPoWtP50goSuUdQwRPrNpVKBalUillDWiPlROi3t7cRiUT69jX/nL1w4QIrSCSKInw+H86dO8f0yzMzM0in06hUKmz8kVSjWq0yDW+/5/XTh6bxb3/JgX/+7ev49+9VcdE4hh9t/DL+R/m38WekVyHyK2BGE3jj7wE3vgb85D8BZs+xj04dOIVZzyxu3rqJt+Jv4Ur9Cm7pt9CCvbziWvIariWv4Vfe+hV8Ye4L+OkjP43nYs89FKF9Uu/GET5YjEjxCAwP4/24H1hYWMCRI0f6VrQb9CCi2vW0tMtHjChrmqrPDTLWF0Wxw9eY1/M6HI4Ov1C75Vig/YLKZrO2pABoe6im02m88MILHcmAdG5wAu/J7+G3tn4LuWau5x2qGBJ+rCLgL2Q3MFnVUY47sbPtR3lbRatmT4Z0UUFVi6CixVBxxVDVxlB2xVBxj6Eld0dP+ig0hoYu1h7oe+UqRGcLgtpEdCoAEwbyez6yBKfihGHoLBLlcDi6qpFJksTGZrlcZhUEybWAJCyKoqBSqeD48eMsKYmvRqYoCoLBIILBYEfikRUU8ctkMojFYvB6vSgUCqw9Xq+XlfBdXFzEysqKrf+0w+HA5OQkrl69yqoRWpe0G40Gs8qySxQieZHT6WSk0Lbf9yK+wWCQEcxeoGV76h8e1Mebm5vI5/N9z9/j8TCvYjtQARKPx4PZ2dme9/7GxkZfOYtptks0k7NMr+I2kUgEgiCgWCx2TELoni4Wi6z63vr6OiOh/DhbX19nkXt+ksGTKRofiUQCb731VseKAxHEt956CwcOHICqqggGg9jc3OwqvZ3NZlniGuU90KSZPxatctFYp3bx8hGK9C4vL+PTn/70wOfsmTNncOrUKSwtLaFQKMDn82FxcRGSJCGXy0GWZSiKgtnZWdtiIvTzMMQxFovhb/3fPo8/vh7H3/q923j7PvB3W38av6efxT9Qfg2HxJ3OC568Bfz6jwLP/d+BV/424AoBaBfm+KHID+Fk/mS7gI3YxJuZN/GNu9/AtaS9vKLaquJ37v4Ofufu72DON4efOvxT+PKhLyPq6v9ee9LvxhE+OIxI8QgA2g/UpaUlpuflI7HRaJQ97OhFs18QRbGn7Rr/IHI6nSyjmn8QhcNhxONx9tK3Zli3Wi0kk0moqopMJsMe1KQRLZfLzMTe4XCwCk68dpRKPedyOUZarNEpykxXFGWga0axWEQwGOwoQZpr5nBJv4S3mm+h1rAnFQDgb0n42UINf3p7G9h0orTtw+2kEzD2lo4hoKaGGPGtuMZQ0WKoumKoOYOA8BhszcQWdLkKhxc4sDiF91beQQNFNMUKTM6blMiIIitoNDVWbYt/OTcaDdy/f5/pyYl88dtQSVuXy8VcGLxeL7O2omtWLBbZNSZPV97xxDAMpNNpxGKxjix8K4gQ3blzB0tLSyxBlCLLpVIJb7zxBtue9Kl8m3VdRzwex+bmJusLPokNeLDsb5rmwMz5er3e072F398LL7yAd955h/WFFbTCUSgUbCdyptmuOre9vd1RUENVVUa26fxfeuklNumwA2nxJycnAfS+96empli/9Jp8yrKM2dnZDrkTHymVZRlTU1NYX19nWmC+r6nYjqqqWF5exvb2NmRZhtfr7ZAGbG9vd1xn637IZs3j8eD73/8+Uil7iVMqlcI3vvEN/Nk/+2dZkSA7UNIaSSXonHm5BlXOoz6kPuGfy6QHpglKv+csgWQyVvj9fmYRF41GO4IDpmmiUChgYmICjUYDly5dGrqgxon5cfz2XxzDb799H7/86jKulNta4/9e/hr+vPTNDocKAMCVfwMs/R7wxb8PPP2zwF6/8JKNn43+LH524WexmlvFN+5+A9+8+02kqvbXZL2wjl+98qv439/53/HZqc/iK0e+gs9OfxaK2HlffVDvxhE+GIxI8QgAHvj92mmkaAnwSXo/0oMom80yTSgRHk3TUK/XsbS0hJmZGRbJoGVDeokqisIe2ryukI9A0f+z2SxLjONt00RRhCzLbQu0QqEnuaA2U9nafjAMA2trazh16hRaWguvX3kd31z/JhpG72Ibh6s6/tJKASfuCihvO3G/epAR3sp8DFUW/Y3CEB+Du4NoQtJ01Mx8u3iF2gScbbeHptF+8c4uLuL5z87jyv0/5PygpQ79KU1U8vl8T82oaZrIZDIsAkhkwAoiZxRRsyaIEd599120Wi2MjY3ZJhutrq72JMQEXddx584dRpD5yCtFCn/wgx+wbck5hY/eVSoVLC0tsfFkZ5PVS8LRq00Eu2Q0wzCws7OD8fFxRuisCYKCIMDv92N7u7/OvFQq4Z133kGz2YTL5WL7IReDSqWCP/qjP+obkaY2b25uYn5+vuc2hUKhgwhaySxNrqhIDxWm4LXANEnlJ1V8H9F2NG7IXsya+FYsFrGzs8Mmwr32Q8fif0+g67G9vY14PM5KNAPd1wNoBwOsPub8Pvn7wbo6xn8uiiLzun4U0ApIPp9HMpmEz+dj2mQqb7+wsIDl5eWHJo6iKODnTs/iiycm8I++vYx/d2Edv9L6k/gD/Qz+vvKv8JS41tmYSgr42p8D3vlN4Md+GYgds23zwcBB/LVTfw3/3af+O5zfOo+vrXwN39v8HnSz+z7XTR3f2fwOvrP5HUS0CP7YoT+Gnz7y05jzzQH48L0bR3i8GJHiEQCgo7QoRar4JTKHw/FEvR/z+XxP6yI+2YReAPS5NWJEpVityXP8C5ReMHwEmF68AJjGkn8Z2UUESHYxDK7cuYLfb/w+vn7n62ga9svEnoqCn1n249nNCFqVKeQc43jNFUPl6RhaimvInnwICCYcbgEOL+D0AQ6vAIfHRLGRQXjcv2d9pyMez6Barbb70BTgcrnYy3d3d7fj5W2XAEWJQP3AF1KxA5FIQRA6lpKJSFHlQKfTiXw+z/yercmbLperg8wMahONGTsyQxFJkjZYVxscDgdqtRrT1lLbeX3qw4whui/s5CXkUrGxsQFVVTE7O8uSt6ya+15yB7vzt1ZGA8DkReVymRFHa/Ifr3VdX1/vS4pTqRRM04Tf72e+4nybST6Qz+cxNzeHfD7fIYkiJ4R0Oo1GowGn08nkBFYJDiXEhkIhW8KjaRpyuRyTbFg13DTBsctDsMMf/MEfoNFo2DqV0POLJu28VphvE/2O3Glo7BPoWef1enHsmD1pfFhEo9GOla1isQhZljExMcGq8D0KcfS7FPydLz+Fnzs9i7/9O+/h0hrw5cbfxS9K38Jfk38bbsHy3rn3XeCffwY4/WeBz/1NJqmwQhZlfG7mc/jczOeQqqbwzbvfxNdWvoa1wprt9qlqCr9+49fx6zd+HafGTuFnjvwMntGeYe9GOzzpd+MIjxcjUjwCgPaLXJZl5PN5tpTKG7T7fD5GkJ8EarXaUNZFtBRrJWAE68vJLpLFL03SC8XOoJ8nA4Oiwb1QkktY8i9ho7YB47YBwRThr0Xhr0URqMYQLUYxlYvC24hBl4MAgOXI+zpUTxhSA6ajDlOp79mb1XD2hz6FfCWJaq3CRYLqKBQK8IZcmJ2dwfXr1xGLxRCLxVAsFlmkzuv1AgDS6TRb2hXFB+VjeRCJGxSZtf6dXeRN13VGMAzD6CB8ZBdFiWr9XmgP479Llb/slqr5yDeVIOY1rIqiwDAMBAIBFAoFljRH44+kB263e+gXLBF/KwHniTL5dPMaVUrojEQiuHPnzlDHskZteVhXV6waXj6COwjUt5qmwev1or6XaEnnSoUkdF2Hz+djiYD8JN40TWZFxxNiOo+HuX+J6BNp5fuAxsSg8UyglaRezjpWrTK/ysJ/13UdBw4cwOrqasfEis5NURR85jOfeahky0GIRqOIRCK2SXQkYXtU4nh80off+vPn8DtXt/Erry7jX+d+HL+vn8XfVv4NflS63LmxqQOX/k/g2n8CXv7/AM//EiD1pjQRLYI/89SfwS+e+EVcTV7F11a+hlfXXkW1ZT9Bfzv+Nt6Ovw237MZx4Tg+U/kMDnm6rd0ajcYTfTeO8HgxIsUjAGjrxjRNw/LyMtPQ0sO+WCwil8thYWGBJbbtF3plKlOWdz/ronq9jlAoxCIm1nKu/BK1XZY63wYiwnYvt16FNvqfF1AyHaiaCuqmBE3IoqJtoiTKGE8/g4WtVxCoxuCrRyDZVHHTH/HO5P18TaUOOBuQXTrgbALiA/LW2HtJzR0Zh8t1sG8k6NatW0xPax0HVDnQ5/N1VGaj6DtpV2VZZhnuw4CPlvEEE3hwfWn5mycrFIWlMdFsNuFwOLoIFpHqhwXfHiso8spP0mjCJUkSFhcXsbW1xUo1EyRJwsTEBILBIM6fPz+wDUToeaLOE2yHw4G5uTlsb2+zQiA+n4/d19VqFffv33+ocU3Ex46EExG2Lunzk05JkjA3N8f6xC6xKxwOM/sv0orz/U5e3xQxdji6PVEajQbcbjdSqZTtpIf6iBIeqfIbn/Sqqiqq1Soby5RwZ1cEg4j6IIRCISYJs07ieekXP86t7jSEp556CgcOHLB1zXjhhRdw5syZjvPdDysxq4aXQEGVfgWZhiWOgiDgy89O4YtPjeOrFzbwf7xxB3++/NfwBf0y/o7yG5gULKXLazngD/46cPlfA1/4u8CRLwB9zk0QBDwbexbPxp7F/3Tmf8K31r6F/7zyn3sm55VbZbyFt/BW+i1MF6ZxWj2Np+Wn4XV4WdntiYmJfX83jvDBYESKR+iCHQl9HOiXqUzuAfTSs0YKW60WHA4HIpEIDh06hKWlJdRqNTgcjo7IoSAImJmZwerqat+2DBPB6rVNwXAiabhR1jXougeulowDRgkHjCYcuhM1IwzR0B6uc4aAAXOvVHGFefnq0t531AEBzFqpUqnAkCTIkCGYDzSspmnC4/Ew7V+vSJBpmh3JNtbrQS+GxcVFXLlyBfF4nJE1elmTrIGif8OAJ5520WKKQNo5EFBmvsvlQiaT6SrOQJ/7/X4kEomBbaFz4SOO/HI2SRb4z+3w/PPP4/Dhw7h16xY2NjZYX8/OzuLYsWMIBoO4ePHiwGIiR48exfLyMvPn5vtGkiQcOnQIMzMz+P73v49qtdqz8IIdqbQDOQ3U6/WuKDAANiHqRUQFQUAkEsHs7CwuXbrELMBofLz++us4d+4cTp8+jfn5eXZu/ASdJlmHDh2Cy+XC+vo6c+TgV7ZEUcT09DTW19f7nhPt67333mMyCb69qqri6aefRjKZ7FsEY3x8HBsbG32PJQgCvvKVr+Af/+N/bCvD4okv2drRvUef0/jiJxHPPfccrly5glwuh0AggOeee65jgvgkrMSsyXi9ng8PQxydsoRfevEAfu70DH79/D38n9+T8cP1p/AX5G/iz0u/C1WwjLHkEvDvfxbm9FkIP/w/Awd+aOAx3IobP33kp/HTR34ad3N38bWVr+Gbd7+JbN2+PPpmcxObzU38jvk7ONg6iOOt4zgZPonFxcVRkt3HBCNSPAKAtoa3Wq12aPR4L0qfz4dqtbpvyQSDLG6OHz+OYDCIXC7H3Cf4rHBa/tU0DS+++CIqlQq2t7c7ojiKomBychIHDhzA3bt3B7apV+SP/xymALGlotwKoNwMwGx64DUkfEo34TB5ctHWuDUAPJLfg2lAbJZQFBpIOSU0tSZcShaqkkcg6oYsCl3WTjwCgQAjtvV6vaN/RFGE2+1GJBJh0bhekaBhkm3oJb2wsIDt7W02SSFbtXK5DEVRmAXeIJBFGiWEEej/FLHkiS7rtj0CUa/XMTMzg93d3Q4LK5o0SZKE8fFx5kzSry0ejweFQqFjcsSTKLfbjXK53Hc/oiiyQgjAg2RQftlZlmU888wzePvtt3vu5+TJkzhy5Ag2NjZskzs1TcPx48dZxJ/8ba2JfZqmDR0pn5iYwPr6eseEhCfHhw4dQiwWw7e+9S3bCYEgCHjuuedw+fJlVixCURQmKykUCszF49SpUyiVSkgkEh2aZ1EUMT4+jlOnTiGTyeD69eusIA9FctPpdIfWuh9M02SFO6xtptwKp9OJs2fP9i2CcfLkSRSLRVu/bEIgEEA6nWaV7/ioOt+noVAIc3NzeOedd5hVoLWYylNPPQVJkroIb7lcRr1eZ4T3SVmJDft8eD/E0e2U8Vd++Aj+1Lk5/Ivv3cWvvenGf6p/Dn9T+Q/4CelCd1s2LwL/5iehz30W0it/C5g5Y7PXbhz0H8SfO/Ln8PPTP4+3sm/h1a1X8eb2m6xYEA9d0LGirGBFWcH56nns3t3Fz3t+HhFtn7VuIzxxjEjxCAAeJNqFw+GeGr10Or0vyQSmOdjiZmtrC1NTU+zFZiXpFAkiwvcjP/IjuHnzJjY2NpjWdW5uDseOHcO9e/ceqn0CBECXIbW09pfugtRUYbbcUHQnRAiwT+t4/1CaJWiVBFyVOJR6BgmnhM1IAKthD2TVxIRYhJuzNzMFAYojwMz9exVLIN0o6VWr1SqLclHhE+rHQRiUbBONRhmRiEQiKBaLqNVqqNfrEIR2dTGv1zv0i5HkDlbyRSSOIpcAWBIUr62k6mGpVAoOh4ORCiIiZKvGO2H0gmmaCAaDqFQqtlFQkoz0q4oHtJfCt7a2WBEEKh7TbDaxu7uLQqGAM2fOwO/3w+/3M/cEAiUt+f1+xONxRCIRplEm0k/6/0QiAb/fD1mWEQ6HsbOzwwg0JZHFYjHm9tBPZyuK4v+fvT8Pr+s6z0Pxd595ngfMA0EQAGeRIkiJsiTTcmTHdiLbGew6aeK4cesbp02T1K1v2vQX39wmjX9JrtM2SZMbN3HSpLFri/IoR5QUWzIHkARnAiAIEjNw5nke9v3j8FtcZ5+9zzkUIQqU8T0PH4nA5h7WXnvvd33f+70vstkso2xIzTtIA1x6vtIxnJ2dZZlkJRWP06dP4+DBg3jqqacwNTXFGgRJ53l0dBQejwfT09Ow2WyMCkIUCJfLBbVajUAg0JI7LIoi5ufnGRVCygOvVCq4evUqnnrqKQDKZkMOhwOdnZ1QqVSyi1S32w2/3490Os1kzVZXVxuoEV1dXbBarVCpVPB4PIjH44wiRHOW5kwwGGwqgTY+Pv6GFCHeaLTzfrifcJp1+Ox7x/AvnhzCX52ax78/2YUv5S7jN7V/3ahSAUC98BrwF+9GbuBdML7z14G+xxRpFXLZ9J/x/Aw+/c5P4/uR7+PL17+MaDkq+28jYgR/NvNn+OKNL+Kp3qfwoeEP4WjXUahVG8fn3ooHFw8lKP7+97+Pz3/+8zh//jzW1tbw/PPP47nnnmO/F0UR//E//kf8+Z//OeLxOI4ePYo/+ZM/wfDwMNsmGo3il3/5l/GNb3wDKpUKH/7wh/GFL3yBSSn9sIWUE8bz+ADUCbQD98dR4yVuANTx+PR6PetU3rt3L5LJJOPgUWZJpao5kfGZB6/XiyNHjiAejyMajcJms+Hw4cPQ6/VMG1YaQlUNVcUAddkEddkITcV0BwgbIIgb/2ioKkUYcyGYsgGY2H8DMGVDKGqqWOjpQ7ivFxG/ByUARoMa2wtxxQ97NptlyhsOh4MZGZBpQy6XQyaTwc6dO5nsGGUoAbByNBlPALUsbDORf6/XC7fbrbgN3duenh5oNBqsra2xOdLZ2ck+2q2kxyiLTSBbqmJBUlwEhHiKBl0HgZp4PM7AIUlwaTQaOBwOlEolhEKhtjOKxN2VAlVq6qO/K2VKq9Uqbt26xe7RwsIC48j29/cjFovh4sWLCIfD7L6SJTTZCWu1Wty+fRt6vR4+nw9qtRpLS0tsnHt7e1EulxEOh9Hf349yuYx4PA61Wg2r1Vq3cIhEInWNcUoOckCtSczpdEKlUiGZTLJxJGDKZ3Wp8Y6nWFQqFdy+fZvdG7kKAFBT8ZiensauXbvgdDplqQHxeBzhcJhd//z8PLMg7unpQblcrlsMKzX+AbV3G1E0KGNMIJkMduh8lEwwaF5RBUtqJEMLAIvFwjSU+/r6sH7HvEav16Ojo4PJFSaTSQwODkKj0bBkBJnU0JylxbDb7UY6nUYymYROp4Pb7UYkEsHFixeRTqdbvmep+tfOO73VNs0oWHy0es80C6dZh195Zgd+8R3b8HcT2/HJ7+/F49kT+Ffqr6FXahcNwDj/MvA/XkbSuRuWp/8lVLs+CGjuVvVa6eG/d+d70WHswIpmBWfzZ3EldwUVNPaelMUyXl58GS8vvgy/yY/ntj+HDw5/EN2W7rauSxobxQPfbMfa7PFQguJMJoN9+/bhF37hF/ChD32o4fe/93u/hz/6oz/CX/3VX2FwcBD/4T/8Bzz77LO4fv06A3sf+9jHsLa2hpdeegmlUgkf//jH8clPfhJ/+7d/+6AvZ1PEvXDC7pejRlnoYrGI9fX1Bj6gy+ViHxDSkeUjEolgaGio7lhf+cpXMDU1xcDI4uIiLl26hNGRMezcvg+6gguqkhHqivFuBrj6JnQLi1UY8lGYckGYssE7/w3AmA3CUIhB4EpxKYsFKz09WOl5DBGPuy6LQY1izYJkx3K5XF3ZtlwuM4Cg0+kQDodZFi6bzdbRS8xmM4LBIHbs2IEbN26wTBh90F0uV50drNy9X1hYYPee7m0ikcDa2tpd6TahRvPo7OyskydTClK14Jv1+DAajSzTC4BlgfksHwEvkvGT0gwymQwsFktbTX+iKDJqhBwoLpfLdTa3SvsAgFwuh2g0iqmpqbrfr6yswOVyMfc8KoVTkNoG0QQcDgeWl5extrZW1yC6srKCzs5OxvGlsjoBMxojjUaDfD4Pu91el3WXBm1LerxkmEFBDXEkjUeLEekYETCmsaA//DZA7V4mEglZ3vHp06eZ9XC5XMb8/HxDRnh1dRV+v7/BqIW/D9J7RCCWH2vexY63VJYzwbDZbMzIhDfWoOMR+CNDEXrPkkkNbUcLel7FRao1TM9SPp+HIAi4evVq3XNGFQCiaGi12qbv2UKh0NY7vd33vhIFi2JmZqble6adMOs1+Gfv2IaffawfL1wYw6defxb7wt/EL2ueR4fQSGOxxa4Cz38S6W/9BoTDn4T5sX8G0ehsqYc/MzODSrmCbbptsOfs2J/djxn1DKa0U4ir4rLnFsgG8N8v/3f82eU/w5HOI/jQjg/hWO8x6NTt8fcfpKX0ln11fQhiq/rSJg9BEOoyxaIooqurC7/2a7+GX//1XwdQy175/X785V/+JT7ykY9gamoKO3fuxNmzZ/Hoo48CAF588UX86I/+KJaXl5nMV6tIJpOw2+1IJBKw2WxvyvU9yOBXzHKcsMOHDwOALEctkUiwbehBUlp9xuNxfOc730E8Hm/QICaKBDW0NJOL2rlzJ37yJ38SX/7yVzB9dfYu1aHMAd+KAcL9sXplwygkoBXXUcxF4A0GYU/VQLAxH4K6qgyy4nY7QkNDuO33IW63N+2SbicMBkNTcGm1WmGxWNhLn890kpyU3+/Hvn37cPLkSXav+Puh1+vxzDPPwOVytbz3Wq0Wx48fr3N1o481Hc/tdiORSDR1/rNYLFCr1U3pCATEeJ40BQ/8qBFPKdqV1CKQw1M4ANSV3NvZT6t7ZjQaWclcKbRaLdNYlnJ8KbxeL97//vfjlVdeQSAQYNJ0dL2lUgmCIMDr9SKbzSIWiylmuG02G/L5fJ3sHYWUbtBuSNUXeLBKtCdqtOUVHrRaLQ4fPozLly835fBarVbkcrmWXPFW5ywIAj784Q/Lur1RxONxNu9pnGlRViqVoFKp4Pf78dxzz6FUKjV9z+7cuROXLl2CyWSSVWvI5/OIRqNIp9NMYk96PMpY63Q6RplSes+Oj49jamqq6XMNtP/ebxYzMzM4ceIE44FL7dqfeeaZewLGfIiiiFO3Ivjr78+gc+7v8Cn1C/AKyhrkJUGLSOfTmMYwZsrdKIuC7BiZzWYAYBUbZhQlVrEirmBGN4Obmpsoo/ni2qF34P3b3o8PD38Y253bFbdT4oHf61i3Ew/yWG91tIvXHspMcbO4ffs21tfX8cwzz7Cf2e12HD58GKdOncJHPvIRnDp1Cg6HgwFiAHjmmWegUqlw5swZfPCDH3wrTv0tj1acMI/Hg9dff70tjlo4HFZcfbrdbpbNVOqINxqNdZ3jgiAAVRXUFWMt41s2YulkBf9r5gxCK3a4xSMbPh4qFOHUrMChXoNDswK7ahWGRAiF1QTySxXoC+0B2qjLheXeHiz39CBts92Ta1mraGW8IG0ikrqgUdn73LlzyOVyUKvVddlkjUaDXC6HM2fOYGBgoOW9f+yxx5BMJmUblwCwbBpZ2DY7bz7kuvRTqRRsNhujLfDglABiuzzfdkKahZZeV7v3tNU9a2VsAtTc/MhdUQmkR6NRxgUmcEWLCMooAqhrwJQLagjjs8xyZfU3MqeVFEWWlpaauudNTk7WzRGl+dHKCrsdIE8KFc0in88zcxOqWlBVgQxPcrkc8vk8Ojo6Wr5nV1ZWmlbt3G43VldXUS6X6wxVaG7m83kkEgm4XK6m71mLxYLl5eWmzzVVNO6Xm1ytVjExMYFCoVB3Pnq9HlqtFvF4HBMTExgeHn5DMomCIODxIQ8eH/JgIbIf/+9rH4dw4a/wT8Rvo0+GVqEVS+hYfQkdeAkHYMGc+QCWNY8iou6r08NPp9PQ6XSsCsBn5T1aD+xpO571PwvLuAVfm/0apqJTMmcHxAtx/M3U3+Bvpv4Ge7178eHhD+M9A++BiTNiaqffZqN44A/yWA9TvO1AMVlo+v3+up/7/X72u/X19YaSlEajgcvlqrPglAbJEVG064T1MEUzThjx+Fq5Ft2+fRvXr19vqixBHfFU5mer83wBBpUNieUKDOkuqCsmaMpGqBToDpHlDFT3MY1FiKiqC8ipC5gXdFCpYvgp/ddxQHseFlUEEEVkAnokFw1IrRiRL955kaP5SyLsdmO5rw+rfX3IWmqZhpp6xRsDD280RFFkWrxS0wkCMpVKBdForYmESoe0HYGlYDAIlUrFsgZK/ERq7GkGsPhmL/oZhdzP5P5OQWNJ/FSeG8tzjDcq+P3z3Fxp09iDCCUVDApqEiuVSjAajdBqtUwjWqPRwGKxMEc3yi5Sdp8/Bs+1pWPJHa/dsabjSIE0/XtaiJEUo3SxQwCFP16z+dGK491OnD59Gk8//bQiF5bXVZfTw6ZvBy3gWnFvWyk5dHR04MKFCw3PNF0XmecAkH/PFgrM1n59fR0Oh0Pxnb62tgYATbdpx+Z4eXkZ0WiUuUvyQZSOaDSK5eVlWYrKvUS/24zPPncQmffuw9cv/Gt86bUv40dSX8W4akZ2exvSeCTzfTyS+T4iKjfWTDuxat6NeXUPMvkCS2TkcjlWbaG/q1QqaKoavKfjPfjI6EcwFZnC12a/hm/d+hZSpZTs8S6HLuNy6DL+88R/xnsH34sPDn8Qez17H6il9JZ9tXy87UDxmxm/8zu/g9/6rd96q0/jTQ8lThhvBS0XJGTeuuN5BkJFA6+pH/FAFoV1AAUtUNABRR1EUYAGwEa2PFaFEiqaHDO0qKhzqGhyKKjzuFDpxHLZgl/X/DX+ifoVCKKIbEiH9SUbUksGVArtdRGvdtgR6BzEcm8vinfKM9Vqtebkgbvl93spMW9U8OCRgs9YEyiScpnJVYu4vcQDJ14pNW8RPzEej7c0MeCvXw783ktWgjfl4MEagad23ero3yiBJ/ovASmpaoJcA96DCOK8Su8rAKbKoNVqkUqlWBmdIpFIwGg01oFhKVClxRQve9dsjFpdvyDUbMGpyVN6LDLrIY1eApf89dKc5Pep9PdKpcIkAaWNdtRY3E7EYrGmXFha/JMKBk/pocoAyRPy56kENlpV7UKhEGtmJIMium5q+KOx7e3tZWonvIKP2+1GLBZjlBS5IDAO4L7d6tLpNKN6KO0nm802pVXda5j1Gnz0yCDEw5/BhaVP4g9e/S62z30JzwpnoBfkF7HuagTu9GvYnX4NeeixqB3CQmkHlrWDiMOG8p2qiVwVAADG3GP4Dfdv4Nce/TW8tPASvjb7NZwLnJM9VracxVdnv4qvzn4VQ/YhvLvj3TCXzLBr5dWANtJSup3v+Q+jffXbDhR3dHQAAAKBADo7O9nPA4EA9u/fz7aRivWXy2VEo1H27+Xis5/9LH71V3+V/T2ZTKK3t3cDz35zRzuuRVTeczgcqJZFpKNF5BNVVHNqVLIa5JMuhFOAWHbf+VeGhv280RBRQUWTvwN674DfO39EVeMLMFI14nvFUbxbdRZ/qftbmKN5BBetSC0aUc63B4RnugUsDfUBvUeQ05tYU5ga9R9wAg1vBSCmkGbFpFk6OZctvmGK1AOk1AjSr/Z6vayk3060akhrJ3iwxmefCCzcSxlWzj2PHwu9Xs+AHG+OQNQJ+v2DDqV5JggCnE4n1tbW6rL3PKc8k8nAZrOxayaHOh4AU6aW//fSUOI1S4M4mtVqlTnFUVZfo9Gw0n4+n0c+n2+gqhAXmg8lOgf9jhw6pS58RCVpJyqViiwXNhQK4cSJE3j88cdhMpmQSqUYp5jMXHK5HAPAUne+N6rkQIsdeh/LuezRglGr1aKvr69BZrNQKDCQ3uyd3s427bjVUZ9As/2QHvhGhyAIONDnxIGf+whimQ/hKxPXEZ34exzNvISDqlnFf2dAATtK17GjdB0AkFI7ENBvw6p2EAHDENJ6NwrFYl0VgP1bjQEfGPoAPjD0ASwkF/D87PN4Ye4FhHPyGu1ziTnMJeaghhq7grtw1H4Uo4ZRqIS7838jLaU30oXw7RRvO1A8ODiIjo4OvPzyywwEJ5NJnDlzBp/61KcAAI899hji8TjOnz+PgwcPAgBeeeUVVKtV1lQgFyTX8nYPpZe1nEKFWBVRygKFpIjIWhHqkgu5RAWxnBqVPABIV6H31/RGdIeKOoeKNocnnzmME699u+bkpq65uMkFfcQoZsturJV0+Ivs76JvKYD4ohGhjLWtc5jpBk6NqhDcNoRhzRGYRFPNDCKbZW5tlJHhy5tEVeCzL81Ces7SoOaj+w0+A6oUlE1Mp9NQq9V1usClUgnpdBpms7ntJlXp8SmUMn5K2xBgIzDF//xexoYMJPhMKYFEQRCYpjNRUfhzIIDscDja0sbdqCBgozTPDAYDjhw5gjNnzrBr4a+LtqWsLDURScdRajABoGEb4O48ovtBx6LzIivo7u5uBAIBFO+ACX5Ro9PpsG3bNsRiMWbSw997mqd0v2hRJHc+tD/KhvELGcqokqwfv39+GwpSAlHiwl65coUBcDomcbcJkJOGNHD/Hf89PT1wuVwIhUKKLnvkHhgIBJg2Mn9tyWQSXV1djELRTHUIQMttWmmd8+dMjYH8PaPqIq/I8WaE06zDz7xzP8Sn9+Hy8mfw757/OnpCr+IDwg/Qr2rubGmtxGHNTmI7JoEEkFeZEFR3IqztgXXJBlieApyDDQ3U/bZ+/MrBX8EvPfJLeG35NTw/+zy+v/J9VMXGd24FFVwuXMbl4GU41U4cthzGYfNhuDXutsc6X6pgPZFHNFtELFNELFu6898i0oXae1EUgbV1M7LZLIxGwKgR4DUJ8JtU8BmBYjKJ7q4fPvvqhxIUp9PpOlWC27dv4+LFi3C5XOjr68Ov/Mqv4Ld/+7cxPDzMJNm6urqYQsXY2Bje85734Bd/8Rfxp3/6pyiVSvj0pz+Nj3zkI2/oo/52CqWX9WDvdqgrJuizPhQWsrh5JQ8xr0U5o8Ld55pW+PcvWq4zqlHV5ZEuRjm6QxYVTR4QagfcuXMnOnbpUT2fQrVJBlatVmNwcBCzs7MoV4G5mAXvXT6BI0vXUEpqEEFrMHyzAzi1U4WTowLURj/2R/djrOKHRtCgWC6yEuTRo0eRTqdx8uRJRkngpcHUajX27t2L2dnZpg1gdrsdvb29uHbtmmK5emRkBHNzc02zk+T61gzw8mCG/k5BPyNeqRKHkbJXciYicsFTN+Suj8wwUqmU4jYmk6nB3IM/bwKvrRobNRoNrFYrU0ORKnRQudlsNrOyv7SkbzAYGNewldoB0LxioNVq2XxVuvcHDhyAyWRqOs8OHTqEubk5Vk6nf8uPEZXZeZBJ2/HVA/o90RCkVQaiI6hUKmbuQedE+yGTD4fDgYWFBZTLZVit1joLZ5KIc7vdCAQC7Nz58yHTDJfLxZ4POWc4cvQLBALI5/MNyhtk9JFKpVh/iNx4WyyWllzYSCQCm83GsuBGo7HunCkTnkwm69QnmrnMUZMbb17S2dmJsbExZhrSzGXv8OHD7PpCoRD0ej07p0KhwFzmgFriSIm/PDY21nIbXjNeKamiUqlanvP4+PgbarJ7IyEIAvb1OtH10R/Fayfd+JOV9yMRD2AoewHHVBewX5iDSmi+wDVUs+irzqGvNAe89D3gJQAGO+AdA7w7AO8o4Bmp/b+tB1qVFsf6juFY3zEEMgF8fe7reP7m81hKLcnuP1aJ4cXEi3gx8SIGVYMYN43j6PBR5EoVLMdyWIpmsRTNYiWeq/2J5bASzyOcvhfKgxaQUc7QqkzoXSzh4PplvH9fF44OuaFRP5h781bGQwmKz507h3e+853s70Rp+Lmf+zn85V/+JT7zmc8gk8ngk5/8JOLxOJ544gm8+OKLdSvl//k//yc+/elP413vehdUqpp5xx/90R898GvZTLG2GsCpfzyHTLQETdWCSlaNZLKKtWQBl8t8R23zju52Q6URoDWLEPQlqIxl6Kwi3F1W7D6wAz0DtexETX94uSGDODY2hp/8yZ/EtWvX2IddCUCo1WoY83mMLKzAen0WH43VGkdKLab/vA84NabCyTEBAacAQ9mAj3Z+FEfdR3Hy5Emk02nWaGG1WvH4449jfPyupSh1WhPQoJf+rl27kM1modVqZe2OPR4PfD4f0xIl2SUKklvyer1IJBJYWVlRvHabzcZkq5pxL/myuZTnyTfdkdpDsVhkgJG4kmTw0k5QWU7aMCUINee7SqUCu90Os9ks2/za0dEBjUaDQCDAQDlPpZC7FiVjCsp8EziRXr9Wq4VOp4Ner8fg4CBCoRCTuFOr1XA6nfB6vVhfX2fHlgO9BO6MRiPy+byiM57D4cA73/lOWK1WXLx4seHe79+/H+9///vZz86ePcvmGWUmDx06hGPHjuHUqVMQxZpWrVSLl+4d3Te6fqlVOoFJatgjaSr+nMxmM8vI8o1mPGAmU6BkMgmbzQaTycQysMTPpXtqt9thNBqxvLzcQA3o6emBXq/HE088Ab1ez8aIX8Dt378f73vf+/D666+jUqkwd0VeecNqtaKrqwupVApGoxHBYLBhLvp8PubWR5bcUhoGcS+Bev4ugWHi7xLvdHZ2tmXHvyiK+N73vodQKFRH54lEIggEAnj66aeZdJmSyx79fnh4GBMTE0zPmsZ63759LCvdjhPd4cOHMTU1hcXFRQZ4+/r6GEgHWmfA2z3nBxlerxfvePwwvNPTCIfVSOR9+LvEB/D5RA7dyUkcU13AYdUU3IJ801xD5BPA0unaHy5ErQmCc6CWSXYNwu8axC86t+ETT34B5wtBfO3WN/HSwksoVOQB7e3qbdxO38bff+frKMb3oxR/FNXCGzMGaSdKVQG3IjnciizjK+eX4Tbr8KN7OvGBfV14tN8JlertqUjxUILip59+ummJUhAEfO5zn8PnPvc5xW1cLtcPpVGHWBWRiuURD2QRD+Rq/w1mEV/PIhXNAzDe+UNx/ytDQVcG9EWI2jwEQwlmtw7veu8T6BrwQRDQlFf3kz/5kygWi3jllVcQjUbhcrlw7NixuoYVURSh1+uZRid9QIyiiK6FRXTPzcEbCEDVRll71Qn8YKeAkztVWPHcabASBYwmRzASG8HB7QdZ1oPAmBynEWjMBvLgWKPRYPv27RgZGWFuXEajEQMDA3WuVsPDw1Cr1bLOV+vr69BqtbDZbKyJhYK4edSAw0s28ePGN5FRw4UUGNB/eZoFgRAeeNJxWmVmCTiazWbYbDZks1n2ATWZTCiXyyxzRICVB5BarZb9jkrVPCjiM4qUKef3Q4CG36/cNfNBv0+lUmy+0n4SiQRzRBNFkTmYSY9FGUq6L9SQxW9jNBrhdDphMBhw+PBhmEwmTE9Ps3s/OjqKPXv2sPM6duwYnnzySVnXN6BmKkENa9Jro4yhSqWCzWZjQJYauHhKg91uZ816cpWCSqUCk8kEi8XC7KBp0UX/pXueSqVgtVobKiUEIClr6/V60dnZKevoRmoKhw8fhtlsxvT0NPL5PAwGA0ZHR7F7924IgsBUHMhenFclMZlMGBkZwaVLlzA4OIihoSEsLy+zY/X09KBSqbCmNpqn0kUDcaFb8Xc1Gg2KxWLLjv9QKIRoNIrV1VX273nViNXVVZw/fx7PPvssRkZGFF32gBpInZ2dhU6nw8DAAHs2CZy7XC54vd62nOii0Sjm5+cRDofZM1WtVuH3+xmgbycD3uqc34qQXv9zd64/mHoO376yhl+6tITY8hQeFaYxrprGYdWUrEFIsxBKWSB4vfaHCxWAgxDQJzrwXsGNF80OnLIVEDYoSDOqctC5TkHnOoVKvgul+CGUEvuAqkl++w2KSKaIvz69gL8+vYAuuwE/81g/PvHEIPSat5ed9UMJireideQzpTrAGw9m7/w9h0pp4yXBRFUZgr4Ed5cVeTEJo0ONTCmKdCmKcrXE7GA9Hk+tScQMqFTtdetTmZPE5vmXp8vlqivtawH4V1bRNz+PjuVlqNuQXIpbge/dAcK3/ajjg/nyPuyP7IetZGOar6dPn27o+E6lUnj11VcBgNEnpKXocrmMkydPQhRFxs12Op3143iHo8e7WskBTCo7kq0zZfN4MCN11qpUKg2ZQiqr0yKDPubSDB85o8VisYbzyWazyOfzsNls6OnpYeYUSplZg8EAh8OBVCrFssKU5SRuocfjgUqlwtLSEgNU/DguLS1haGiINZLJHYvGuVAoIJfL1ZmXEJCja6cGSaWmrRrvzojFxUUGfAlgZLNZzM/Po6+vjyleSD/wdN5arRZutxsLCwvsuvhsfLlchtPpRLFYxMTEBNNiJjAXCASQzWbrRPUFQWDUDrPZXHf+IyMjTJ6LL2/z/69SqdDT04Pp6emGpjpaaPp8Pmg0GkSj0QZ7ZmqW6+7uZo5tpERCwNDpdEKtVjMN83g8zrK2dG8pm2s2m5nBi9vtZmV/mqPEq6QxovlC10XgjMaIz4JKs5e8JrDH40FnZyfbRq1WIxaLoa+vD8ViEYFAoK7xTxRFNs/9fj/j77rdbtZ4RUCZzpnnONOihAfPOp0OkUiEaUybTCY21nROqVQK8/PziMfjzHZbTsJMFO/q0Ho8HqTTaeTzeeh0Ong8HkQikTodWmoGlAsl041wOIwTJ05AFEWEQqG2NW9pAUB9OnIL0Qcdctfvtxnw8aOD+PjRQSys7MDzJ4fxt/NP4TeTAqzlGHYLt7FbdRt77vy37WyyJFQQ4Rdi8COGJzMAMsC0TouvWSz4lsWEpFoeeKoNq1B3vAC975sop3ajFD+ESnYbKJml16jgMuvgNOngNGvhNOlgNWhBSV76+ooiEEkXsBDJYiGaQb4FRlhN5PF7L87gq+eX8Z8+uAeHt7mbbs9HqwbTtzq2QPFDHJVSFYnQ3WxvLJBF4s7/51LtS1G1HUKN6lBSZSEYiiirs6hq8xC1BWgNAkrlEoxuN+x2e41vFggxGSgCEFRC1ev1bTWbvPLKK6w8TB+9V199lZWHDQYD9DodbIuL6JufR8/iEnRtdJSr9BXMbQf+x34dZnoBUfJQ6it67IvuQ2+mF8Kd7j2DwYAbN24wlQ2pu1exWMSpU6cayss88K9UKjh37hx+7ud+DpOTk7JWv2Qsc+nSJczNzTF3OIr5+Xn4/X5YrVbGUdTr9Q2UBuI5G41GWVUIAkDkeheJRBggpvMmMOVyuZDJZOoMI/jrI0UGyjotLy/LTyGh5qC2Z88evPTSS0wDlSKZTEKv1+PRRx/Fd7/73YZsND+egUAAvb29WF1dlV1ciaLImpBIgUFKjTCZTCw7R+CSL1dTlpXKvFIgSwsLakIkkCU1JyGKgNfrZdtTGZ5fyBDgmp6exvLyMpO44+kDDoeDNb2ePXu2wQr55ZdfxpEjRzA+Po5UKsXmhpRzTWEwGFAsFpljnDRrTplVMi2Q/nv6ezabZTx3MpagyGaz6OrqwvDwMBYWFphesnQBRlnqI0eO4PTp05icnGzYprOzEyMjI5iZmUEgEGBgmrYxGAywWq0MhLWjCby+vs4WBfz88Hq9GBsbQzQaxfr6uuz4EB1hbGwMy8vLsufc1dWF0dFRlllOJpNIJBIN1stkF10sFlmWX/r8GI1GpNNphMPhhkU1H6RDW61Wm1pBt9Khbcd04+TJk2xeKmXA6VilUumhtBU26dQ44Negs1pAoVBEQjQhqX0St/A+fCtUwMx6An4xijHVArYLKxgWVrBdtYohYQU2obUhjzRGiyX8n9EYfi0Ww8smE75mteCMUV6tSVBVoLVfgtZ+CX7o8R7LGD408G5sGzhao2vcQxZeFEUEUwXMhzO4spLANy6t4tKyfP/LXCiDn/6z0/ipR3vw2feOwWlubmP9MFhKb4HihyBy6SLCy2kkAjXgW6M9ZJCK5NFGovWeQ2ME9FZAZwP0VgE6KwB9AfFsGIHAOvtwaDQaqO9k74p3gGg8HodWq8Xy8rJiRm10dLQuy6NUarty5QrLuBJgoOzMyZMnoVlewcDiAp56/jgMLRzSAEClqcLak0dgexmf3ePEikF+dTqYGsSe2B7oqvWaoqTDLO22B+7KeCUSiZbZ73w+j+PHjyMej8v+npRRMpkMVldX686BqAGrq6usc5yc6HhQRfxRohjw/57fHwDGtadsrZRiQU1QlNmTNrXRvviGP6VGMvr56uqqov5loVDAuXPnWNOe3EcWqCkCLC4uKlI1qtUqotEoy9zJSdIVCgWmpEEhvX7aluaqlHdLdBgCZs2uvVKpMI44z4Mn4F+pVBiPlgANfy6FQqGOd3rq1ClWtaDzTiaTrGrhdDpl1SP4sSyVSownKgd2C4UC5ubmGvjf0u3C4TAuXryI9fX1BhOQSqWC9fV1LC8vI5/PM4DGZ8mp8kGgMRqNMmDNZ9Kj0ShWVlYwNzfHxohvoiPurlqtxp49e5jpRDPgp5SpEgQB6XQaiURCdgxpDiQSCSwvL7OFJX/t5XKZZX937NgBo9GImZmZBmoE2Tb7/X52PtVqVfFZbpVdKxQKiMVijO7AW0Fns1ksLS2xSkqzaMd0Ix6Ps14HuSDe9fr6Om7evIl0Os32VSqVsLKyUkex2GzBU0McDge0Wi38pRISiQj2mXL47LsPw2Rz4vJyAtdXE7i5HscL0SxWU0WsxPNwlCMYVK2jTwigXwigXwiy/7cLzZuT9SLwo5ksfjSTxbJGjRcsFhy3mrGu0L8RQAF/lb6IL125gCNn8/hQtox3Wgeh9+8GfLsA/y6gcx9gkLc6FgQBfpsBfpsBh7e58c/esQ3z4Qy+eXkVX7+0ihuBRh3pL59bxompIP79+8bwwUe6Zedmu/Satzq2QPFDENOn1nHyqzdbb3gPoTWo4fSbYPeZ4Owwwe41Yn7tBmKZdfg6G2V3QqEkTCYj0y7kgQRx60qlEvL5POLxeNOMWjQaxdTUVNNS2/Xr1zExMVH3QRdFEcZsFn3z8+i7PQ9HIoEMWigdq0RYOvOw9+cgdBfwh14nvmJ3yW7qrDhxJHUEzqwTJbEEEXd5g9Q81MpFrB06CFDLcjaL69ev1zWt0QeEKAZA7SVjMtV4ZMViseGe0XiTEoBS8xdJYxGoVnOlOvr3mUyGZZ4JwPEZTgLFgUCgqSskUAPES0vy3dYUt2/fZh9+JXkvaqCikHJYASAcDrcEqmTfSpxfqRECL19mNBqZ+QTfbCWKNVvlVtbDgUCAUVHkFhflchnhcLguIymVrqLMdyqVYtJ+fDZZEAQUi0WcPn0a7373uxnooXvHb0emLK1snuVoM9LI5/O4fPkyKpUK05rl7xnZM/PPkfS+0KJ3cnISxWIRBoOhgXddLBZx/vx5RCIRVCoVpvQA3KUY5HI5hEIhZnGsFEQxqFarbLFOGSydTsfs6knX3mw2s/vAL0BDoRDOnz+PcrkMv98vK5E2MTGB7du31x1bei5AzYHOaDQikUgoZtOpea9ZUINgMytoaiBsFu2YblClopUG8eLiIgKBAGtS5jPXmUxmU9oK8zSUZtSQJ554AtttVZRXV+DShHHAVYbGp4Hb7Ya3dx9gsKJQriIQjuDm7UVMxhIolKuwqvIYNBUw5gY6NEmYsivQp1egSa9ASCwDmbsW1T3lCn4pnsC/iCdw2mjA8xYzXjGbUJLrgxAEnDIaccoI2CoBvG/+Nj549e8wViwBEADfGNB9EOg5VPvjHQFU8jSNAY8Znz42jE8fG8bFpTh+84WruCzJHkczRfzqly/h5akg/vCn90OnqU8uPCyW0lug+CEIp/+NEegFlQC71wiHzwiH31T3x2TTNUw+Z0iNM2eUZXfcbjdmZmo2mUrZO2riaJZRSyaTWFxchM/nUyy13bhxg33MNYUCepaX0T8/D28g2MJguRYmbwG2gRxsPTmo9SJ+YDTgP3q6EdA0lpHUohqPFB7BzsxO2Mw2aK3ahuxMsVhkKg4PKnhAxn9A+QwLNT7JjSOBI/qYSkEYlX7z+TzLpBUKhYZ7RhnySqXCMlvSjz4ByqmpqZaZp3YNEyiTKBdyL0650r4oiooZeYp0Og2j0cgqEXwTHWXRSWqNDBGkQfNFCqIp6GfSpkA5TWA+gyx3X+k+8plbPuNKx00mk5ifn68DpnL/5RcN0rlEx2p3sZfL5ZgrnXSukWNZKwObYrGISCTCroVfpNDcjEQidfeCX6TRfC+VSohGo3UmTtLgrW75+01BNsfE3ZdTV6GKQzweh81mq6vYUJCF8fT0NHK5HPr6+pBMJutc5qxWK2w2G+OG04KOl7ajbHp3d3dL691UKsXmIy2m+TEirnkqlYLL5VLkerZjukFlcDLxkc6hZDIJp9OJ5eXluuw+n7mWZvc3S7Rrh3z79m1cv369IRO6vr6OZDJZ80DQAMsrU/CWs9jec3ebRCKBxbIJnQffD4s0W1rKAYllIL4IJJaA2ALU8UUcjS/gaGwBscgKvm0x4WsWC27o5ekLSbUaf2e34u/sVowVingulcb7wtOwB68DF/66tpHeBvQ/Dgw+CQy8A/DvlqVd7O914Pn/4yj++tQ8Pv/dGWSK9c/zt66sIVeq4I8/dgAGrfqexnAzWEpvgeKHIBwtQLHJpmsAvU6/CVaPAep70BVsZS1KDSL0gZC++Hg91GYZtUgk0tJatJDJoHNlBQO359G1stJWw1zM7oC6r4QDA3PQmmvbZwQBn3e58FWbvEuSr+TDE5kn4FV7UdVU2YdWKnRP196O6cZGBk/PkP4MAGt6MplMDVlAAsz0b3npOvoo0t/pg0/8U35BwDeolUolWS4s3eONdnSTqkrwwE+JEiAN4kUTsKCgf18sFtHd3Y3V1VVGgaAolUpM0SAUCiEYDDKAzGfvyJSFXNikzWgA6sab/i6lbUilBZsB0lb7ISoBD+ilwd8/+rv0vNsFxPw+aWHBzxHeerhZ0D2hJjVpRYqyucDdRbicjGA7Gad2rG4JxPPPF3+u9DMCekr7yWazSCaTKJfLcLvdbBHKN9oRDYUMVaTPP4Ftcihsdo2ZTIbNTTn9cMqqZzKZplzPdk03xsfHcfbsWcWkSnd3N5PPU8pch8Phltn9Bx3tzJFkMomZmZmmmVDqH7nnbKnWCHiGa39kwlnM4mOx2/gn4VlcX5vA86Fz+HZhHSkFjeUpvQ5Tehf+/24n3pXJ4oOpDI7k81AVksCNF2t/AMDoBAaeALY9DQz/COC428ypVgn4+aODeHZ3B37r69fx4rX66uAr00H84pfO4c9+9lEYdeqHylJ6CxQ/BGH1GKA3aWB1G+pAr8NvgsNngs64cbexWVNKPB6H1+ttKobvdDqRzWZZVkGaWSFpIurSr8s8iCI08/PQnjyJd5w7B12uNcAq2my41dODhb4evNP2fexT3WK/O2vQ4z943FjRNo6PRtTgQOYAtiW3QavWQueoae2SAYVUBkmtVrMS34MMOetlKbBSqVR1vFLKqFFXP5WfKTPM75syUXq9nl2bVHJKFGvyVcVikTUR8k5jBLZMJlPbOsXtBN88CNRTR6jk2q5ZCABZQAPcHU+eU8tnZHk5tLW1NdbBT46CmUyGSXFRRp32wR9LjirQ7O/thlLVBrhrAa2Unaf7LzVSkdJQ7iXIUIUH65RFv5doVgGhKgItTvh5TYsgvV4Pq7W5MU87VrfUGKxErwFQp+XcjD5gs9nqjifNTJNqBXFXqemOb6DVaDRIp9NYXl6WVZ2gsFgsbIwAebdI+n0rrmc7pht+v79pUiUYDDbw3ylUKhWTrGuV3X/Q0c4coWw48df5oEwoNRU32+YNZUt1JsC/C4J/F3bteg67APx6OY8TCydwfObLOBO6IPvPSoKAFy1mvGgxo7Ncxo+nMvjxdBo95TvvqVwMmPpG7Q9QMyTZ8SM1gNx7GFBr0Wk34k9/9iC+e20d//rvLyLLZY1fmw3j4385gb/4uUMPlaX0Fih+CEKtVuETv/+OB8a1oVK6NOx2O4aGhhiXM5fLsdIfieEPDw8zLUt6CHidWFolk3yRx+NBdW0NxokJWCcvQBsKNZ6QJIpaLZb7erEwMIB3fPKTmP36l/HTpS8zi86cIOCPnHb8jV2+kaCj0IHxxDjsoh06o44Blq6uLuaiReCG/5AZDAZZsw1pmEymuk596djeK5ijf0fBA2KLxcKoDHz2kjI6dG285BkPDglQeDweJjfGf/QpY9XX14d4PM7OWy5zbbVa0dHR0aCo8UbD5XIhGo0yUM9ft1arZQuwdkJ6zdIM3NraGhs3adaxWq3iypUr2L9/PzweD1KpFLLZLLN8tlgssFqt8Pl8uHHjhiIolv5MjmIg3b6dkGal+WP19/djenq6Yf/ScaGMIp+Z5vdDFJlWQZlcnq7DA+N2rckpu61k363X65lEIlWg6Fi0mNXr9eju7mbX0q51PT82yWQSfX19qFarCAaDbAHKP2fVahU+nw8A2HtPFO+6IQqCwN57o6OjiEajTY9nNBrZQlQQBNlqWzwer+Ovy0V3dzfTV6beAynH22g0MhONVnxZADhz5gwDvPTeOHz4MDPdaJZUCQaDdYtNpYz7Zot25ggvoSkXtIgA7nL75eT4NipbatAY8P6h9+P9Q+/HcmoZx28exwtzL2A9I9/vsabR4E+ddvyp047DuTyeS6XxTDYHA/8eCk3V/vzgC4DeXgPIYx8Atj+DZ3d14K8/MY6f/+JZpAp3n+/Tt6L4uS9O4Is//2jLMWzHvvpBxBYofkhiM7wsBKEmhk+ZYv7Fr9VqYbfbMTY2Br/fj29/+9tMKosv6VssFoyPj8Oh1aL0rW/DeO4sHKtrLY9dUamw3tmJ+cEBrHV1oXonUxmam8QvlL8Ej6om+H9Zr8NveNyY1zW+nDSiBuPZcQymBgEREAWx7sM/NDSEUCiEcDhc9+ErFovo6upCT08PZmZmmoIDtVqNHTt2YHp6uk6tgf6r1WqxY8cOXLx4seU1W61WxhuVOyZJIGUyGUVrWdJFpexxw5jcadwhreN8Pl8nfycINYkrMtaw2+1MZo/nFBNdRo5PqjROzcaR6Bx2u72uKY3njLabeeQ/xFLAR7+nD7hGo2m4tnK5jHQ6jVu3bsFkMtU191GYTCbmxBaNRgHIO+jZ7fa6xjY+w8lnA4EaP1cpjEYj47IqUSwo085zXKUAk8aSSsBKYTKZmqpPAHf1kkulkuy9od83szensFqtjG/Lc0+pAkJSZMSx5Rc59JwRUCGuu5JdMhl8NLM59vv9ePHFF+sUUWj8TSZTjS8K4MUXX2Tgj6fDmEwmjI+PQ61WtzxeX18fUyBRqraROU+zII1rUuOQZri1Wi3MZjMCgUBb2UuXy8UMQHhHO5fL1fDv5JIqVqsVer2e9ZrIZdzbye4/6KDvXrN7tmPHDly+fLlpJpSeazJmkpPjezOypT3WHnz6kU/jU/s+hdNrp3H85nG8vPgySlX56tEZowFnjAb8p0oV781k8MFUBruKxfpenkICuPKV2h+NEdj+Lhwc+zH83T89go/9zQwSubv7PrcQw89+8Sz+4Me2Nx1D3ir8rYwtULwV9xwETviGIykQouwG/29QqcB96xaq//n3ED17Ft1tNFyFPR4sDAxgqa8XRcnLolNcxWOX/iuMQgFlAH/usOG/O+yoyDxYXZUujEfHYSqZoNFq2AeLmjvsdjsikUidpBJdJ0kqDQ0NtQRzGo0G4+PjsFqtTNuTQq/X49ChQxgbG8ONGzeaZjkNBgMzLmhmBW2xWFg2ifiKZJSiVqvhdrtZZlMuKIOczWbZR0qaldVoNIjFYjUZIr+faf4SANHr9UxGqre3l+knK4XZbGbd9UpWx1arFVqtFl6vl2Wy+A+Iy+XC6uoqu09KWXm6hmb3jOcAE5+azzoSvYBe6MQfpn+Xy+WwtLTErIvz+bzseNN5UyOZUnaf7lkzUEy891AopGiS4vF4YDabGYgkbWD+eBaLBYVCgRmuKNlckzW19Lnmt6OsKGV5pUHVolb3TKWqmVFQRpLXOuftmcPhMANZ/DkRdUKj0WB9fR1Xr15FMBis2yYSiSAYDOKpp55q2UtBMlHkpsfTkciMhYAhvw3ROKTb0PGkQJ20jN1uNy5dutSSw9vT06M4PwAwo42uri6srKzUaTnT8QC07O/gpdTo2HJNZK3ktHp6ehj9TsrLJ/WXdq7rrQile0aLK4/Hg9XV1ZaZ0Gw2i5mZGajVauaCSZXXeDyOkZGRNy1bqlapcbT7KI52H0U8H8e3bn8Lz88+j5nYjOz2KbUKX7ZZ8WWbFduLRXwwlcH70xm4pM9/OQdMfxOY/iZ2q7R4vfdJ/M7SbhzP7UP2jjbUpaU4fuulJXz+/eO4MTPT9Dl7q2MLFG9F2yGKItNHVZIvmpqaYgYXBoMBAgB7JIKemzfRPXcL+jZKQwWXC+mDB3HGoEdaIRuyrTqLj+Jb0AhVLGnU+KzXg0uGxhW2Bhq8U/NObEtvQ0ldAtRgMkeUBQVqH5CrV682lYG6du1aywyoKIrIZDKIRqOwWCwsU0tjFI1Gkcvl2MdBKQtKH1eSC5NGOp2G2WzGgQMHWDmW/8jk83l0d3djeHgY165dY2CDB/sEfovFIqNGEM+Qvx4CeA6HA8lkkoEsPssTiUTgcDhgMBjQ0dHBJJqkmXKVSgWfz8caseQoDaQ4oNPVeN5KtrlUEm4WcrxUuaBGHymnlhYNAFhDl1KTUDKZhNlsZnbEBEJJQiudTiOTyeDQoUM4ffo08vk8K5FTJt9gMGDv3r148cUXm55vKpWCz+djDnPSIEMJUg6gBQ1ZOROPnHdea5aV5zn2SqFWqxndxmq1NjxD+XwemUyGAXDpvaGxt1gs2LFjR52dM0/zsNvt2LFjB+bm5lAsFhsc0WhOx2IxTE9PY2VlpQGE5PN5rKysMLvkZmV/eu/pdDocOHCASeHpdDpYrVb23gPQdJtWklP8QrQdDq+SMguFXq9nzoJ0D6QycXa7nS0sWkmp3a+cFn9d9PzyfRtGo7Gt69qM0U42eXR0FJOTk2x76b9/kOEwOPCxsY/hY2Mfw1RkCl+b/Rq+ffvbSBblkxk3dTp83q3DH7qceDqbxQdTaTyeyzcCyGoJ1sWX8Z/wMn7ToMNLlQP4euVxfK+6D9+/EcI3Zjz4xBNPbDnabcXbI9qRVSE+sSmXQ//8PLpnZ2GNty6XVkwmZA88guJjjyPd1YlQOIz0rVuy245Vr+OnhBchAjhuMeN33E5kZV6kfdo+vE/1Pmx3bcdccg5VdRVmsxn5fJ6BFYPBgEwmg1KpxJrIpKYKvAxUq5J9qVTC+fPnsb6+zoAHfWxEUcTKygosFgu2bdvGMgTSLKjVasXQ0BAuXbqEUqnEOIY8RzGXyyGZTKJQKCASiTRkFUulEiKRCHPDo8Y4/tqIn1itVusc0eQ4nMQ/zOVysNlsyGazbAxNJhOSySQsFgtEUYTBYEBvby9zAKPQ6/Xo6Ohg8lQAGJcVuCsRR4YFHR0dzDaXrpUAZzKZhN/vx/LysqIiCPGjW0nAEdeVzkMqb0bleH7BIOVmE82C527zwf9927ZtsFgsOHXqFEKhEBtHn8+HI0eOIB6Pt1RpqFariMVijNYgVXqgjLXVamXKAQRAaA6TKoHdbofFYoHX60U4HEY0GmVzxuVyMWt2JQBO58Nn3Hn1CaKj0HZkOSx1flSr1TCbzfD7/dDpdA08bH4BRdQFWqRIKS9kELK8vAxBEBjPn5+z6XS6zi65Wq1iZWWFcUStVivUanVb7z2+iQqoLbKJ2kJSa7yj25kzZ9gCwWQyMe1pyroSR3diYoJVSsgRcXx8nP2+WdBzmkqlGCimICk2k8mE/v5+BAIBxQynw+FAOp1uW06rWq1ieXkZ6XQaFosFPT097LmRXhfv9Ci9LiUeuHTeKR3rXqPZ8ch4otk9a1UB0Gq1yOVy6O/vZ26G1JNjsVgY1eVBy5KNucfwG+7fwK8f+nW8svgKjt88jlOrpyCiMQFUFoATZhNOmE3wQo0fi8fxXCqFAZnvogFFfEB9Gh9Qn0ZCNOGblcfw9e++A0cGfx49ls0DgqWxBYq3ou2gbB2VzaRgzmOxwHbuPPovX4J3PdBST1hUqxHbPoSV4WGE+/ogaLUwadRw3/lYy3FT91Uv4jnhFSRUKvyWx4WXzI3ZQkEUcLB4EAczB1lTXzAYrAOJFPQhJgF6nt/GB3FN28kUr6yssLK6nPD+/Pw8nnnmGSwtLTEtUvq3hUIBXq+XOZFR9k1a0iZO7euvv66onxyLxXD16lUGFHjwSLxjymTz6gM8fYCCOMqVSgUrKysNxyHlCTILoOwW7Zu4sgRGiAfKA1YCOwRQnU4n5ubmcPPmzYZx7OjowJ49e3DlypUNkcmTZselc4+apwB5ri/JW1G5mUC/dIyIPpFIJNgHmMY8Ho8jkUi01FWmIFcwKVeaFnKxWAzFYhHj4+P4xje+0WCHTWBx//79WFxcxPr6esPzQXOY9HebBd9QJ31WCKxTlYEWWtJtDAYDenp6GDglcxF+QZhMJnH16tW6cZDuh+5XLpeDRqPB6uqq7HOfz+cRiUQwOzuraJfd39+PcrmMRCKBtbW1BtOJzs5ONgeDwSBWVlbqehJu3bqF7u5uliGfnZ1lZii00KB3aLFYZFnXkZERDA8Pv2HQl0gk2FyVqn9Qlj6Xy6G7uxupVKopx/nKlSttyWnNzMwwwMsvrHjA2851tWMH3M6x2o1mx/N4PJienm7rnskFzU36frrdbtjtdlk5vkgk8pbJkunVerx38L147+B7sZZew/G543jh5gtYSa/Ibh9CBX/hsOIvHFYcEEx4LryGZ1NJmGS+kXYhi49pXsbH8DKW/98/wZLvMSzZD6Ng6tyyed6KhzeoHLe0tIRqtVorqQoCLPPzcF++At/cHNRtND+FPR5E9+5FcHg7ijod9Ho9jJzVKd/Mwj52oohx8SzeK7yOSb0e/9bnlrW5tFVtOJY7Bn/Vzz6iBM6Im8gHgVcCYkqZsHuRk+KPQxlaAipUUl5aWmK8Ur6RplqtIhKJsJczgUE+eOAu1/TFRyAQYFkNKdCjjCZPqeB/L/1/Xn1CGtlsFtFolNFHiM5B2V/6+c2bN1mZXmlMSYZqfn6eUUwIVNG1BwIBLC4utpTIa9coBKhl1ih7yWfTzWYzU/CQGxsaN7J6bjZGyWQS09PTOH36NNPEpgVOKpXCq6++yhQTWgUpu9A50HkQ75RoBDz45oMWYZVKBYlEoqmlOD0/zYKk2HjZPD4IuJjNZty8efPuO4TLYCcSCVQqFVZxIu1fnr9NlA9+MScNytjzGr38GBEH32AwYGZmBlevXm24H2SXffjwYWQyGQQCAXY+dM7ZbBa3bt2Cx+Nh1ZlqtVr33NNc7ujoQLFYZGBQev20yFleXmbZQuJXv5EggEWLWen9oIY5AE051VqtFlNTUy3ltJaXl3H69GnGZSbecSgUwokTJwDczRQ3u6527ICj0SijYbQ6VqtodbyxsbG27tmtW7eYUytZQfO86507d7aU49sssmSdlk58at+n8M/3/nOcWz+H528+j5cWXkKhIg/YJ8UsJt12/I7Xg2dFIz64OodH8jnZxFgPAugJHsee4HHErKOYCx7CROwZjD92dFMA4y1QvBVth81mYx/HznIZvrNn4bl2DbpUc2kgAEibzVgYHMDCwADSdxplUqkUHHecxAAw+SVyhmIhingHTuEp4TT+1GHDnzjsqMpwkI6YjuDd2ndDsNSAsMViQSQSwdLSErNMVZImklo4y3Ed2w0CKpRZ4TOcpVKJZRaaWcLOzs4y/UvpfgCw/bQTtB9enYL+S5xNHhhLj0XZx1byZ9FoFDabDbFYjOlWS3WRCfwRWJXLelFpfHZ2lrl7yZ33xYsX7/neKIUg1JRRHA5HAzWE1Ccos6ZEZ6FqA4WUhkJjNDk5iVKpBJPJxLYhMJbNZlvaZFPIudDx94wAzw9+8AOW6ZeeU6VSweuvv15nukLb8ACXAGqr86Hj86CQB6KiKGJpaQkqlQpms5k1NgpCTZmiXC5jdnYW8Xi8qckDPc8EQOma+Ww1v6CkRRV/XQRYr1+/zmhK/AKH7KIvXLjApNj4e0bvK6Io0AJD2hxHFZFoNAqVSsUyjjSvaV9kpBGPxzfEBIcW4lQRkHLFeffKVpzqVnJaHR0duHbtGgqFAgPzQC2RotVqmc318PBw00w38bdbmWDMz8/f97HaPd7MzExb96yVecfKygrcbjfW19c3vSwZhUpQYbxzHOOd4/js4c/ixdsv4vnZ53E1clV2+1y1hOMo4XinF/06B57Li/ix5evwKfQiOFPT2FMI4rbjiS2b5614+CK+uIiO85PYMTkJWzDYcvuiVoulvl4sDA4i7PEA3GQnCTECZdKGC/qIVSsVvEv8HoY1l/CLXh/OGg0NxzEKRvy48cfRk+1BJBtpkLlZXFxkLny89illHeWc6u4HbBH3VJrl5XmqqVSKWctKX9wk+yVt+nqj50aAnLrMecMVjUbDmqIoc8uDLL75px2e6/nz51kTldK5EEin/cuBRwCMv8cDawoyWtmoILBlsVhgNpvZzwmAU9NfqVRi1B5+G5KQk9Mopr8TMEylUnVgjz+HN3JdPH2Cj2q1isnJSQboSV2EPx4tUujvdJ4053jeeTtB18Rn2+n66ZlOJBIwmUwNkoVERyDaByl8SPdPc5muXaniIJ2zSmNE95fXw6Zz1Wg0zJyFb5ql8y6Xy4xuQHJx0vshiiJTW7h58yZrqJSbIxqNhmXC7zdIHYh/huXGgrYTBHkptXaayBwOB2KxGMxms+w9oz6CVoYj7fC3FxcXEQ6H7/tY7R4vFAqxxkCle5bL5RCJRODz+Zryrvfu3YtkMrnpZcnkwqaz4adGfgo/NfJTmI3N4vjN4/jmrW8imo/Kbr9QjOMLKuC/9PXiUY0X71xZwU9nVyEl4QT8T8Nqd2zZPG/FwxHVYhHp730PieMvIP2P/4ieFgL+oiBgrasTC4ODWLmjJ8wHX8Ls6elBNBpFKpVipVVqDAoGg1CrVDiGV1E0TeMnvB2Iy4CjHcYdeKbwDFRRFdLldENpixQFRFFkGrG8NJHBYIDBYGC8XCXw1w4orBsHsVGMnigMlKlqxtGjDBgv28WPYSuZMWkMDg4yPiSfPers7EShUEAul2PlQblr12q1bXHdSCKMHMCkPE61Ws2yYLQwkR6TH2veBIIHTxvpnAfUqiCFQgHpdLrBQUwURXR2diKTycBoNDbQbEgqjK6VQLx0G+AuWFQ6f/rA0j7k7nGz3/EhCAISiUQdRYcHfWzhyVF9KOTmbruh9KzQ/nkqE78oJEoE0WX4+86fs/TcWo0Bvy/pedLPqVrDU4hImpHnq9LzQ+eg0+mYAQaAunnPLyzUajVrvqOyuk6na1hc0SKdB7RvtFNfr9fDZDIxh1E5XWCTydRWub6VbB1JFbayuW5lONKOHTCfkb+fY7V7PFrUNLtntBhqxbu2WCxtyf+1G/czP+4nhp3D+DeH/g1+5cCv4PvL38fzN5/HayuvoSo2PvdVVDFRDmDCr8HnyzvwSMqIT6fn8Wi51oC/5n9qy+Z5KzZ3iKKI/JUrSBx/AclvfQuVNsT2S729SB08iNs93VhIJGpZp0oFkAFF1WqVZV3kolwuQ6fV4Mnii3jdNY8v2X0N2wiigL3pvfjMgc/g5A9OolgqMs1e4G5pi7Jg9FHijRP4lwfxCOkjJi393kvQxxaQz05RBqqVDBKVbJWAarsZPJLb8nq9SCaTLAtHQDCRSMBgMCjykwnEtfPC4qke/L+XyzzTXJBm5/gyN90rKbje6BgeHsbVq1cbGuSAmqLA3r17ceLECQiCwBqrCCwRbYJvVpT7MPGlfMowSoM+sDSOxIuVAiz6easggwceqFHIcX6lmeJ7nf9073inRZ72QAvCYrHI7i//b3nbZOJGS4PGh+Yj/7zRdfFzip5tuWZVGhcl7jmNMS0q7HZ7g8MccUH5BkP++af5q1Kp4HQ64XQ6GT9fzk6epA3baTZrFgaDgSmKkOIOP0Zmsxkej6eB26oUzSgWtJBr9U5rZTjSjh0wjdn9HutejudwOJDJZBTvmc1mayltR3xhh8OhOI73Evc7PzYitGot3tX/Lryr/10IZUP4xq1v4PnZ5zGfnJfdvqrJ47wzj4877TDnBnAoZQACDryve8vmeSs2YZTW15F44etIvPACigpyaHxUbDbkD48jf+QISl1dCIVC6OvoQPDcOVay5VfO9PEhO+DZ2Vl5EfNYFAeEl/DfOtZw2dBo1Wwsm3A4NI5udMNqqbkfNftwE2UgEokAQF3GpFgsolgswufzQavVspW7lItYLpeZ1myraJZVpWwCaf4qifPzL02z2SzLYTWbzS0b7YBaw0kikYDX663jq/F8wPX1dQZepKDoXrKEO3bswMrKSh0FgPZF+yHjiXQ6zT7IdG0AGI1BFMW60j8FAWWz2dyy0a6dEAQB0WhUUfUhHo8zB0IaEx5I0FwxGAyMpwnUl6sJXNG1ZzIZWQvjYrHIKBzBOxQlns5C+3K73SyjrRQqlQr79u3D1NSULIDmFyg0x+lYPNWHFkVSUC0NQajxsikzSc8dcVtVKhUMBgPLtspxzqnKYLfbEVKwfC+Xy3A6nayxT24RS+ep1+sZGJd7F9EcanZdKpUKLpcLiUSC0WQo6Hn1+XyIRCKy85UqPVarFQcOHGDZyUqlwhoHiWOtVqvR09ODYrGIiYmJps1mrYCP3W5HT08Py75TEyk1O2o0GvT09NwTh1WJYtHT08Ok/+7HcORebLfD4fB9Havd43V2dgIAFhYWFO/ZwMAARFFsmy+sNI7tRjvNiA+6ac1r8uIXdv8CPr7r47gUuoTjN4/jO7e/g2xZvhclY4zhH42AWPltbAv8Kh7d1rsp+NRboPiHPKrZLFIvvYTECy8gc+o00CorpNMhtWsnwrt2Q/XIfuiMxhonKhRinKiVlRXcvHmzIZNJH62Ojg720mjgX0GEtfJVfNYZRULduGrszPTgUOQAdFUdRF2t+ctisbBVPP+BrFQqNWULoxHJZLIuW8mDXorHHnsM//iP/8hW9fR7yurt378fZ8+ebZox1el0LMvLNwEBd2XNdDod9uzZg3PnziEejzdkHvR6PZMbI9tlOk++/M7bQCuFSqXC9u3bMTc3p8hjI/k3ylzKcXxJy7eZCgdln41GY0OGjz9Hi8WCQ4cO4R//8R+Ry+UYeCK5P51Oh8cffxy3bt1i80iq/CAIAnM1U5KkA2q0CDKBaHbe09PTdWMmvf7p6Wn09PRAEATFeUYydKTiIKf40dnZiZ07d+LVV19lXFWe463T6fDYY49hdXUVwWCwYbyJCkEcxWZBILQVT1mn08Hn82FxcbHuueCfk+7ubhSLRabHKxcej4dp+xIw5s+FbMCpSZRffPHHIsMLUjORlv0py0nmKAT0gLuSgpRNdjgcCAQCsscSBAFOp5MtjJVCo9Fg165dOH/+vKKZxpEjRzA5OVn33pMeq6Ojo87mOZvN1pngEG91ZGSkZdNWO01JPBc4m83C5XI1HIvnsN5PKV6l2hjDkXb4y2S7fb/HupfjAWDWzHL3bHR0lG3zZvOFRbF1c+Bb2bQmCAL2+/Zjv28/PnPoM/iHhX/A8ZvHcT5wXnb7DnEI2122TcOn3gLFP4QhVqvInj2HxPHjSH33u6i2UBUAAOzciY6PfgS297wH0XweBeJE3bEH5uV7yNVMThPY769JpSWTyQYRc1GoYE31v/B1TwqAhItcVWF3dD92pAehElTQaGvZ3Ewmw1brwWCQNagJQq1px+VyMbBlNpuZsgB/TkajEYVCAZ2dndi1axcuXrxY95FUq9XYtWsXK7ELgiDbHW4wGBiYJrMA6fVTE2FnZydzNiPep0qlYoCxp6cHt27dqvuos7EQBGZwQAsCJYqFzWaD2WxuyQcEalkTJc4kubURSJcGlQWJFqNUdqdFxo4dO5DJZNgig45HVtgjIyNYXV1tOY+ITqDEvaVMJXErpUHqEnK8Wvo7ldhzuRwcDofiPKMFDwB2zjQGdM7kxpZOp5te+9TUlOIHQhAE1g3fLCqVCmZnZ9v60Gi1WnR1dSmet9FoZLzpUCjUMB+9Xi+zMe7u7kYikWjoFbDZbEin01Cr1WyuSa1+DQYDA7YDAwMIBoN1usBmsxk+n48tVCnzrLQfo9GIvr4+rK2tNczrjo4ORvGgJlzpdVHVyGq14plnnlE00/D7/XWmNXLHMhgMrGLDP49U+ubfoa2av9ptSmp1LMombkQpfiMMR+TOWY53S+d0v8dq93gA2hrHjeQLK0U7zYGbpWnNpDXhue3P4bntz2EhuYC/u/x3+Ob8N5Go3KVkfrjnaRw58OAz20qxBYp/iKK4sID48eOIfu15iIFAy+3zDjvWR8cwP9CPqteLZ/buhdNqhddqVeREBQIBBtJIVo0vjVerNfc0nU7HyvmFQgHRQhgvhL+A65pGsGksmnA48iT8ZTugRh1X0WQywWg0YnFxEYJQs5elj1GlUkEwGITX62VAmAw0+KhWqyiVSrhx4wYWFhZkS/oLCwvwer0ss+fxeJBMJtlHn9yISOaqUCiwJg2e9kDZwEwmg2AwCJ/PB5fLxfaj0WgQDAbhdrsRj8cZYJV2jtM2BAKk3GNqjiOgRoYgk5OTiMfjcDgcOHDgQENjlxJnUqvVoru7G0tLSwDuZqypfK3T6TAwMIBisYh8Ps9K1fwChMY9n89jbm4O2WwWAwMDddk+Xpas1TxKJBLIZDKsMZEHInTtdC5arRZWq7XBcIZvWGwG5AmoEaWBtJ+JbhIMBtHb24tyuYyhoSEMDAxgeXmZPR89PT2MwkPGN/39/ayxjxY42WwWq6ur7L5brdY6AxTSQk4kEi25vlS2JQBJ18DfN1qk5PN5DA0NYdu2bVhaWmLSc729vRAEgRm2kPHCzZs32Tbbt2+HSqVCJBKBxWJBKpVCd3c3cyzT6/VwuVyIRqNwu91IpVKoVqvw+XwN3Gy6P6Iowu/3w+/3IxwOs/2QQUI4HIbNZkMikYDH42kwQkgmk7Db7Wzu9/T0MN1eckUUhJqxBo2F0WiUtXen52pkZATbt2/H9PQ0c70bHR2FWq1metp9fX3o7e2VPRZvzNCMn0v7ascsg+5zswxvs2MBG1uKv1/DkXbPeSOP1e7xNmqb+412mgM3S9MaH/22fvy7J/4dfu3Ir+H4pa/jxeXvYCE/j0+86xPQaRr7K96q2ALFb/OoJJNIfudFJI4fR+7ChZbbl7RaRMfGENmzG6meHkAQoK9WG7QflThR1HRULBZhtVobuFWpVArlchlms5k1JSyJi/hS7I8R1zRm+7ypThyMPgYr1KjiLujjM6uAciMRAS0e6EmzjsRvnJubY0BM6qCWyWRw7do1OBwOJBIJZqFLmcR8Ps8yYmTTywNCOiaN3eLiIsv2SYFaqVRi5h5yuqcEAJPJJMveSQESASDKQE5MTDQ4dp0+fRpHjhzBo48+WscHlONMer1e7NmzB2tra6y5hC8PazQaDA4OIpvNMqqFKIoMNPJc1VKphLm5OQiC0CBhJIoiQqEQFhcXmZ6x0jyiBQjf3ETjwBt+WCwWZq9Lvyd1Esoq8uYW0jnE81Pj8XidaQbNIVosAbUSKs2RarWKfD6P1dVV2Gw2qNVqLC4uIhAIIJVK1SmhpNNpWK1W1gyqVqsZaKRzpnvXrikJZco1Gg2zqubvB4FAtbpmZ5xMJtnijmySqRQM1DLgfPa2UCjg2rVr8Pl8MBgM2LFjB06dOoULFy7UPUMLCwssY0aufSTHSIoNtDijpiU6H/75IJtxrVaLRx55BK+//jpCoRC7HloImM1mHDhwAPPz8+w94/PVN+zm83no9XrodLo6iULaD42xXq+H1WqVzaZGo1GMjo42NGzJHUvaSKT0Dm2n+Yv21W6GV+lYb0YpXqV644YjfLTDu92oY7V7vI3a5n7iXubHZguaT5qwCu8pPwtogTOnzmw52m3FmxtiuYzMqVNIPH8cqZdfhthixSgKAkq7dmG6w4/k2Bi0nE4rcO/aj0AjV5gPsnqNx+O4rL+Mb8a/jqpkc40oYltoL4azu2ASaqCAPrBsmzvgLRaLMd6kFNCQ3zxwV2KJL5FTFk6n0yEWi7EPOb8NSZiFw2GMjY3VldOlDRd+v581vymNQbVarbPJlnNImpmZqZP64a+Lzo+c35TK6ATML1++LOugRo5dANriAwaDQTidTlYGpwyfxWJh9BW/3w9BEBhwljabEWDO5/N13HL+npEBCPEylUIQhLrFBy046FgAWNaLB8T8OJZKJTgcDkaHkNuG9k0LKul4EziPxWJwOBxYWFhgHyX+vsbjcfT392N1dbXOrY22yeVydZKBBMqk8/VeorOzk7mRSSskxNHVarVM/1W28TUex8jICLLZLGZmZhjIJt58JpPB/Pw8RkdHmUkFPWv8eUejUZRKJdb8RQsueoYsFgtUKhX6+/vZsShjS4usZDLJzqenpwdut5tlzmi+aDQauN1udHd3I51Ot2zaokw/LZD5THGlUoHX64Ver2+aTR0fH2+rYaudRqJ2m782ohnvYSrFb0Ut2p0fm6FpjY/N2BwoF1ug+G0U+Rs3ajJq3/gGygqd23xk3G6Ed+/GXE831F4vMpkMnEaj7LZS7cdisYhXXnkF0WgULpcLx44dY0CKeK6ZTKahNK7X62tSQN0efDHyF7iemILUC7KzVMZg5D3o1WyDqIqjWr2b2QLuGj5QVpoaragjX1qOjcfjLLNJWUUKPmvGd7NLS3BEiaBSaDabhdlsriuhm0wm9Pb2Ynp6mmUppZk5yuAm7sjWGY1GlsXUarWMa0kd8QaDgY0hX/Ym2gNvhEH758cpn8/j/PnzzLGrXC4zEELmKadPn8Yv/dIvAQBOnz7NPoKUdTpy5Aj8fj+uX78On88HjUbTUB4ulUoIh8Po7e2VlcAC7jatEQ2AMpfS0jfNI7p2ogDw84jMD5LJZN29lC6cKpW7Nr9yUm7VahXJZBIej0dR7QAAK/9Xq1U2jjyloVAoIBQKscoFgXW6b3zDYjAYZG5tlPVXqVRMKYGaJ2me80FzmMaxlfqExWKB1+vF2tqaoryZ1+ttAN7S6oMoiuz6AdRxoWmeRyIRnDlzBuVyGT6fjz3/Go0GZrMZiUQCExMT+JEf+RHGOSZZPlqsWq1WjI6OYnJykj1zvMIIjTdQa37UarU4cOBAAw0lEolgZmaGKa9QhYPmkF6vh81mw86dO9HR0YHvfve77N1G94sUZ8bHx3Hjxg2mCkOLRXo+wuFw3bGCwSAzZ6FKgdlsbmgkKpfLsnSmdpq/+GY8pXPiM7xKFIu3qhR/P019m/lYDyLamR+bpWmNYrM3B/KxBYof8ijHYkh+45tIHD+O/PXrLbcvGgxY2bYNS9uHkPR4oL2TOaQPTzvaj1/5ylcwNTVV99GcmJjA2NgY3v3ud8NsNiOZTLLucQpBEODxeJAypPAbU7+BldJKw3GOZnMwRn4aH/3gx3HqxLeQuyNvJs3gabVaBk6p1JxMJht4pQTQCERTFo4Pul4CIVIgQh836vwfHh5mDR68sP++ffuYBTCBVun1G41Gtq9SqcRk4oBaiTWVSrEMI/E9pSYY/P7kSv48SKYsKZXnpaFWq5FOp2slLYnjGe0DuMtjI4DBNz+tra2hs7OTHc9kMrHFiRS0EXdTo9HIlsfJhZAc5AKBgKxUmtPprNOl5q9bOhYEzJQaqWjOO51OWSULp9MJk8mEeDwOQRAaJLxoP8ViEalUCj6fD8FgEPF4vK4Zz+fzsTmqUqkY1YQfG5JG48EiD/ZpbkppQHJB96Ojo4OZqshp9RKnnc6bQDl/3uFwmF0/T90gAKnVahGNRtm8XVtbqzs/+lgTz9jn8+HWrVt1dB6LxYKhoSFWtSGaiBx/PRgMsuZOWlDQgokaTMPhMPbs2QMAuHXrVsNY79+/n2WmSG5RyoG3WCzQaDRM/mtpaUl2vtKx6N2wvr5e927gjwUAr7zySkOj5auvvopDhw7h2LFjLZu/qBlPp9M1PSdacE9NTWFtbY0tHDo7OzE2NvaWlOIfpL7uZtDyfTOi3ebAzRIPU0ViCxQ/hCEWi0iRy9z3vge0KKmKGg0C3d1YGt6OQHc31Hp97QMjisyeFKg19ZBMlJL24+nTpzE1NdV4DFHE9evXIYo1mTS5rJsoijifPY/J9CTKQv05C6KIT8WSuB37GMb2PAarqsQcoOSCpy0IgsCyTlIN4lwux2Sg5BQjgLvcQj6TLAVbVJqljn5qLOOzQbOzsxgaGkK5XGZZOel+stksjHdk7JRADTW2abXaOvc3CgLJer2+aQZHjg4gDQKuN2/eZI1hFouFlbbC4TBOnDiBxx9/HJlMBoFAgIEguvZsNovbt2/D7/cDAMsAK52TXq+HVqttWa4n3rVcxGKxhvvUbBxojvMZe/q5IAhsDrhcLgbEiCtMLlpSUCndv0qlYha9Go0GDoejrjqQTCbrjiuX1aZ5RtxiOVthrVYLo9GoqKvMnxftn5QaeI43XU+lUqmTNOTPmzL1NMf5pkTp9ZPkoNQunK6fFnw3btxg9CC73V4nR3j27FlUq1WEQiG28JI2mZKlLlWGeCoSgUJSnXnttddw6dIldm78+Vy8eJEt4FKpFKxWa8P4pFIpXLp0iY1PuVyWdc00m81YX1/HzZs3odfrMTAwwI5VKBQwOzsLl8sFr9eLV155BSdPnmRzjH+HnDx5EgAYMG7V0EzZ+GbndOXKFaYYQtcWiUQQCATw1FNPPdBS/IMsoT8s5fo3Gg+iqW+j4mFqDtwCxQ9JiKKI/NVrSBw/XnOZa/FBBADD7t2wP/ccknv34My3vw0ADPwBdzm3NBF3796NS5cuKfJKDxw4gK9+9atNjzk1NVVXiqVjVVHFJecl3LTdbPg3jkoFvxuM4FvZX8D+8WegyUawsLCgCGIpCoUC+9A3CyoTNgvi25ZKJZap4z+ggiAwl6l8Pg+3213XXU9/n5+fZ+cttyImsN4qy0cZV9qXHMhs13q5VYiiyAAxZd4AMPAaj8dx5coV1mRGqg3AXa5tNptFMplk/Fwqh0szk6JYM+QgAK2UmeaVHpSCMpetro3fN503vwAi8FepVGC1WhvUN4hv3EoCrVqtMgqOxWJhtsWUHacMLE/hkYIQAi0OhwNarVZRkqxYLMq67/FB15HL5erkD/nMLLkakpIKye4RGDcajcz0gW8W5YMH2DRGPN2Jp/MUi0XMzs6yucZXYAjoEwilCo9ckynta3l5mfHUiXecSqWQzWZhtVoxNzfHfi8FvOVyGRMTE7DZbGyBzSvbqFQqpNNprK6usgUx3/TJu2aKooiFhQVGZ6APvE6nq6MzOBwOnD17tqGBlo5XKpVw9uxZPPnkk2wx1KyhmaQmeRoOP9empqawurrawHEvFApYXV3F5OQkDhw48EBK8Q+yhP4wlevvJ5Tmx2YLviJBSQaeMreZmgO3QPFDELGvfAXRv/orFG/OtdxW4/PB/mMfgP2556Dfvr32w2iUlXeVsqA6nQ47d+6E1+tV1H68detWy8wjcNculvafV+dx2nsaYUOj2cCefAG/Hwzjj0ufwK5D70aXRYW8xoalpaWWx6pWq6ypiWTUpHqlZBfbDqChTDmvZEGlcZJhIyOIc+fONVA1Ojo6WAMaP74U0iawVpHJZJiMmJze8UauqhOJBNxutyx1xGQyIRQKsQ+5UvayXC7j1q1bDAjq9foGTjU5vqXT6Qadah6okXtUq2hnPrbanr8vBPz5Zqt7Ccq8kvEGP4eIR03jRmPNUyMoU0xShbt375bly7ZrzRuPx1Eul+F2u9k++Y+RKIpYXV1lGU3SzKZQqVQsk89fY6tQqlJQ9pEoVtJnSKfTIZFIsLHjmyfpfAjs0zyiZlp+P6QRXiwWGRjm7weNNy0IbDab4vlks9mG6pk0qNFSFEVcuXKl7nx4CsoPfvAD2SZUujaSQJycnMT4+HjLcaZMPjXcEk2MFvOrq6sMKMsB5/n5eRw+fPhtp6/7MJXrfxiCmgPn5+dZUzHNV6PRyFwBN0Nz4BYofgiiOD/fFBALej2szzwD+3PPwfz4YxAkDUWlUgkul4vpffKlb+pKp7JxM+3HM2fOtH3O9OGPGCI45TmFvIz+8E8lU/i3kRj+78onsP3Qe9BtrX0kiPvbTiQSCcYtpVUofYxEUayjILQK4lQWCoUG0KfX67Fr1y5cuHCB8TP5bDJlrYxco6LcAuReolqtwuFwwOFwMIMCyqgBYM5pGxE0VnJBQAUAOxc5g49MJsMa9GhMpNdP2eNisQibzaYI1Obn5zfkuoiHSiBBes1k8KFWq5kUGh/FYhFarbbtDAaVwXltatoPXSNvFCHNghJnlvR5I5EIbDYbrFYrisUiIpEITCZT2+dDGRhq5CR6B3G2CTTSh4rPYNOCmfSIeQ77/US5XGbHktKdeLk7ekdJx4j+zmfCScWD/q5SqeqaUHkwTPuhIMoBIG8BD4D1ClDVTOpAqdFoEI/H2aKCVxXJZrNYXFxk4FJ63/mge9GKGkP3kBZx/HNG73hS5DGZTIqAP5fLIRwOY3h4+G2lr/tGjiWKb6+GvM0UglCT37x8+TJzASQaYTQaZTKGm2G8t0DxQxCO555D9C++2PBz48GDsD/347C95z1QW62K/56aiIxGY0M51mQyMY1R+tAqaT9SZ307UalWcMt6CxddFyEK9R9SXVXEv49E8cF0Bp+r/Dz6H30f+m13gXyxWGz7o0+rfI1Gg0Ag0JChcbvdbX/Iu7q6sHfvXpYpp4wnZco9Hg9OnDjByr38h00QhLqPPZ/5o6CPdrvnQ+VUUuzgI5/Pt9Vs1W7wx5IGASs6byWDD+KjkpQVdftT8E12RFXR6/UNWU8qO29UUCWBOKE8MCCwQ2BM7qVMGrbtBIEoKT+d9kvXRsofck5sglCzsB4aGlLM3gUCgbbUJ3w+H3Q6HW7cuIF0Ot3wfFgsFnR3d7NFCM8DpgwrgLYWlvTvmoFnnpKkROUirq0cgKVnjhb0vIIH0S2IUkX3TC7jzO+Xnl0C/vz5EOC22WzMkU6uslEoFLC0tIRKpVK3H6oy5XI5xGIxDA4OsrkhB4ypUtAqe8kbnSjRWaiJrxng58f5zS7FP8imvnst179dG/I2S4hiTfbQZrOxPhRavFKFMhgMYseOHW85MN4CxQ9B6IeHYdi1C/lr16Dt7ob9x38c9ud+HLo2Rct5XUOlcqzH42lZunjiiSdw5cqVlseroIKL7ou4bb3d8LvOchl/GAhhV7GE3y7/LDoOfADb7HcBMZVX/X4/1tbWWh6rr68PhUKBNW05nU72u1KpxLRzg8GgYoaGPkRdXV0YHBxUzJRfvXqVASe5khz/saNjUZaSz7yREkKr8Pv9SCaTio2PLpcLgTacCdsJp9PZsslSFGuyW9LsC2XQvF4vDhw4gMnJSdZoqWQE0tfXh0AgoNjcMzg4iHC4kW4jDR6AyWXliYpAx5VKu5HzGfFzCZTy90wK7psFz0mVUg6IWuF0OpFOp+H1ehua+sgGmOacUvZODuTJjU1XVxfLBkp1qsk8xOPxsHlN94uvcvDa3c3mLS2cyDhFuh8+M9+soZUWB0Tf4ucbjSEtZuj5kl43nSvdQ6WqDfUKEEjk6S1ENTAYDHC5XIjH4+jt7a3L+ut0OoTDYZhMJtbMJxcE0vv6+pj0nhxfmqT6Dhw40PTeiqLIFipUOeKvkXjhAJoCflLHeBDxIPV176Vc/3ZvyNsMQXQWWqRLFymFQmHT0Fnu3Q9xK96S8P7ar8L1x/8Nlr/5a2j/6c9C29vbsI0oiojH40zGin/xj46OwmQyIRKJMP4ZX45tp5nC7Xa35FgWtUW81vWaLCA+nMvj71fWsatYwu9WfgbWne+Boxxl0lzU7GEymeD3+1seS0qNoA8y/aHr1+l0MJlMAFDXXMU3T5nNZvZxIc6Z2+2GzWZj45JKperAr3Q/9DtqROO1eqmxh1bGPHiXC5/PhyeeeAJ6vR6xWAzJZBKpVArJZBKxWAx6vR7Dw8Oy2rt8yEnMSUOj0WD//v3sWJlMBvl8HplMhh3r8OHDOHLkCDQaDdbX1xEKhRCJRBAKhbC+vg6NRoPx8fE6niyfMefBCQA2H6lxkWgHdP8HBwdbZot5F7lm29C1kaYx3edkMgm9Xo+hoSEmTyZtJuPd+dqJdvi2g4ODzLGNADFpV5NRSqt7RrSHZkGc2ZmZGajVaqbUQmCRDGcWFxcB1Gsg81lamtetxpqvaPD3mv9/i8UCh8PBtJ2JMlEsFhltyePxoLe3l2XxpE18pO6g0WhYCZzuH10zNXuSRThlpwl48lUPu93OlCfoXAgQW61WOJ1O9PX1wWQysYUavU8IEPt8PrbQIk46vReIokYVlUOHDjGjEFoU0f+r1WocOnSILU6q1SoWFxdx/fp1LC4usjGgRRxRceh+A6ijDdCY0DufzonmD5+tf7OD/w4pPff8d0jpe9busUgCMRqNssqIIAiIRqNIJpPMdZBvyNPr9WxcvF4vstkspqenN4Q29FbF/YzjRgVPZ6GFqMViYUkInU7HNPTf6tjKFD8EEQqFMF2pIJxIoPz667KlnVblH6/XC5/P12D1a7FYcOTIkbZWwisrK8xQQe7BihgiOO09jZw61/C7j8eT+JexODQAPl/+aex6/6exw6nCxMQE0zSV6v0aDAZZrVvgbgMQAORyOTgcDqyvr9c1ExI3s1QqwefzYX19vUE7GKiBpK6uLqYuoaTpSV3qchxVAgyUCaSMiJR729HRAbvdjn379uGb3/ymoi7uT/zET8Dr9WJ1dRVnz55l2riCULNtHhsbw/bt23H16lVkMhnZ8j5xYYnKoDSOdP0qVc36mXiRND8OHTqEkZERhEIhWK1W1vVOodFoYLVa4XK5mIoFmZDw9ACSBSOpvWbNPdlstq6ULw2+PE1ght+Osp46nQ5dXV1wOp2KDaRqtRoXLlyAyWRijYD8ORP/kO69kt4x76JXLpfrxoia9kRRRFdXF+x2e9OxBpo/01JQJD0fmo83btxANBqF1WpldsZS2gst9uj85OY1UYmoGVVKaSCQSIBC7uNGJW2HwwGDwYD19fU6nV6tVgufz8eej2q1ikAg0NCw6Pf7sXfvXtYgSItg6fxQq9Xw+Xwwm80IBAJ1YJjcJwk0u91uZihC7yKr1cp+39HRAbfbrThfc7kcy8LzDoo0h2h+WCwWHDt2DNlsFhcvXqx7v5F28rFjxwAAMzMzsnrotGiiZlYC8dI5m8vlYDabodfrmZ679J7Z7XZG+XkQ0a6+7v3SGdot11MD5Nu1IW+z0EIeJmvqLVC8yaOd0g6AlttEo1GcPXsWpVKpThs0n8/j7NmzsNvt7GOsFMRNc7lcSKVSdS/TBccCztvPoyrUAy9jtYrPhaN4T6am2/v7pZ9EyHkI77ejqd7vo48+2lQ1gn7ncrkQi8VY8xut9CkjFAgE4PF44HQ662TC+CymSqWC0+lEsVjE9773PUVNz3e84x2wWCxMqgioL1lms1nYbDZs27YNwWAQjz76aJ1sG7mieTweDA4O4tlnn8Xp06exurrKPnpdXV1skTIzM4OpqSnGfeUb/6amptDV1cXcAwnQSDmsZrOZlf8JXPDbUONXJpNBMBhk1ss8fSAYDCIYDGJycpJ16ku3SSaTOH/+PHp7e1GpVOBwOGCz2RiPV6PRwGQysaxFOp1GX1+fIj1gdnYWAFjGS9okxFcBaKFAwJ+AGXXi63Q69PX1KdJiYrEYa/qQ40unUqm6LKvFYmlwtMvn89DpdCwLJ/3AEo+T7iN9kDs6OurmdDAYZNSTZs80OSsSMJKeMymo0L2nDI3U6lmn07EFZj6fV5zXdP2k5yzdhj52ZD1OzxZtQ+NSLpdhMBgQCARgMpnq6EWULe/o6MDg4CCsVmvDArWrqwujo6N116G0aKLnWqVSYXBwkIFwvV6Pjo4ORKNRdHZ2QhRFrK+vK1IjqKQvCILifKV3USgUgt1uZxlZqpLwtBi6v8PDw6y5kygjQO2dH41GceLECRQKBZjNZnb/Q6EQTpw4gcceewxGoxGlUokBP6mUnMFggN1uZ1xoJcD/oIFIK33djaAztFuuj0QiD41+7r3GZqKFPEzW1FugeBOHKLbWWiQjjVbbzM/PM21QnttKL8yJiQkMDw83LclSo10mk2Hl2AoqmLBM4IbxRsP23aUyvhAIYeROhuILpQ/htmEvRg01DjCdM32I9Ho9rFYrwuEwLl26VJf9kXLvgLtd7LFYrKHBhcYvl8sx3WUqiRKQIoBJMmvnz59vqul58eJFHDlyBK+++iqy2WwdP5P4iEeOHMHg4CDS6TSi0Wid7mc0GmUlwnA4jNnZWVa+569tdnYWDocDExMTDXqu9NGLx+M4e/YsHA4H0/MlgEMlW6DWiEglXwKK/LEoK0iuWG63W5ZzfvHiRczPz0OlumuYwo9zKpXC/Pw8enp6oFarGRjmpaL4MjLNpWbNPZTJJO4oBZX/tVotrFYrcrkc+2jxIM1kMsHpdDKwQZkfvV5fVzZ2OBwYGBjAzMwMMpkMDAYDaxakDP327duRzWYxMzPDuvs1Gg1raBIEAf39/VhZWalTPeDHiK5peXkZ2WyW6Q1LQRiVa5s907QAJI60VC2Ftnc6naxU3yxDMzo6iosXLzKrYJr7BBgeeeQRXL16lV2LlHvL74+uU9qISotwOj8C9Pxzxj/zzcDT+vp63aJEOq9p4dvX14e5uTmWLbfZbBBFse5ZBGpUmlAoxJr0SJ5Ozp5ZLlQqFcbHx3HixAlGg6Fxp3MfHx+HIAjsnS7tthdFUfZ9TdfG64Zfu3YNfX19mJ2dRS6XY7/jVUS2bdvGDDxaAf4HHUrPfTvfvHb0heXK9XwQ2BVF8S3JYIrim6t0sVHjuFFB1JmHwZp6CxRv4uC1FgHUierr9XrYbDbWjEYuVHwQCFhcXEQ4HGa8SmlGTafTIRqNYnl5GX19fYoPbHd3NzQaDbLZLAwGAzLI4Hv27yGka3Svezybw++FIrDfAbB/VP4gprV7MajPQxDMiMViMBgMWFxcZNq+BJbsdjsT5edLvpRRomadSqWCW7duMbDLc3xpLIhPFwwGGZeUzzpS9pJKsVRW5MvzlC0kTU8AjIZC3DybzYYjR44wbdFmJUKPx4PXX38dsViMlfbofhB4p7Ip3TM5OaVwOMyyzlLlDeI50jyQu3aSr6NsabVaxdWrV2U1VtfW1pDJZOo41vw8I91TAtJra2sNzU0EHjs7O9HT09N07lMmFLjLB+X3R/PA5XJhbm6OZW75CkA+n4fT6WTNNM3KiAcPHkQ6nWZOYXy5vru7GwcPHgRQW3wSLYaC7u3BgwexuLjYlPJRKpXYfVWy5yWpvWbPdCaTgcPhYI2P0vtaKpVY4+PMzAxCoVDTJsp3v/vd0Gq1DdbDer0ehw4dwr59+zA9Pc0WE/xih2T0ADBVCTkaBlFPkslkU43qXC7XslxNCw+r1dqUrmA2m5n1shxNizJlw8PDOHPmDFZXV9n8cLvddfbMrebQyMgIEokETp06hXg8LkuLicfjLcv19L42m80NSQqaJ9FoFHv37kUmk2H8XJp3KpUKXV1dePTRRwHUAH84HIbNZmMOfsSFlnJ432pJso3SF263XO/xeB54BvNBUBo2o07zw2JNvQWKN3HQareZlSllZpqVf4gnSR3W/AebMoJqtRrpdLrpA0vSRKlUCkulJZzynUJOI88f/lexOCi394elD2NWuxcdQgKFQi3TmMlkEIlEkM1m684nl8shk8kAqL2UKPspLQ8T1YL4oZQVlnIreXkwp9PJPpb8vsrlMtMFVQIiBPrC4TDGx8dx8OBBTE9PMzrB6OhoXTazWZYrHo+zMj5l/HiLVpVKxbKfdL/k9FypJN3d3Q2fz8coLTqdDtY7En3EAyfzFqIeUMmXAAQ18/FOWwSalpaWYDabWzZoECWDFhpKHGZ+0dFsX9TgRiCXgsAvz9+UaxCjCkAoFMLExETLMuKePXuYRTnNfafTiT179rAX9rPPPsuanmis+/r6sHPnTmaUAaBO6ormMmXmSfNZyZ6XMnl0r6SlX8py7d69G2fPnmW0B54SZTKZMD4+zhogT5w4oehUOT4+zp7Fvr6+hucjm80iEAjAYrGw7DnfQFosFhmAy+Vy7Lmj66ZxoIVLtVptqlEdiURQKBSavotofGhf0koKGW5kMhnMzc0p0rRIeeHKlSuMN07ZxVQqhStXrrBt2qGxkZkQPQM0jxcWFjA4OMgablu9r+k5VNqGru/pp59W7IGgOfsgOLwbFRulZdxuud7hcDzQDOaDojRsVlvlh8GaegsUb+Kg8mgzK1NqpGimx0idnfTBlr4gqBxcKpWaPrDbt29HoVDALcMtnHOfa+APG6pV/F8cfxgAPl/6KcxrRtGpTkGj0TEx+1KpVJfd4HmupApA5UepCgGVbKlZ5ubNmww8SjPF1Wq1AaDwslw8kG0X9NG2mUwGqVRKVhaKtpdbhefzeZaZNZlMDMBR0xtlz0itgjJ/9NHXarXseul3ci8/3qSBeJN8k6XRaGS8wuXlZQbSiFJCxy4UCshkMjCZTMjlcgyg8+OWy+UY7SASiTDwIQ2VSoVIJIJ4PN5UgYOyh7QA4jPFBHxEUayT7ZMuiIDaouDChQuMGkKAi6y5I5EIoysQMKLqgBQYUcPq448/DlEUEYvF4HQ68fjjj0Or1WJlZQWiKEKv19dRUwAwabFSqcQWchaLpcGUJZ1Oo1wuM8c34l9LKykajQY7duyA3W7HmTNnEA6H2TaUkaEeAfrv6dOnWXZIo9Gw7Xbs2IHXX38d2WyWccr5MQ2FQkzykK5JOt+JvkRAnuYubUtzQafTsfeVkpmIRqNBOp3G1NQUo7OQHvHq6ioSiQTGxsbgdDoRj8eRyWQa+MvU0EfZeClVwWazMapCNpvFysoKo4Txi4uVlRWcP38eJpOJWTjTgoKAI1FeyBKaGk+l9Kvz58/j8OHDdfq55Ean0+lgs9mYRjv/7pMuUkhf2GKxtAUyvF4v3G63LJ8eqAdqRC2iZ+uNALX7yThvVEPWvZTrH1QG80FSGjazrXIzytxmiC1QvInDZrOhXC43tTI1m83o7Oxktrhyeoxerxe3b9ck0pSybuVyGWtra00f2Lnbc3ip9BJmPDMN59pVKuMLwRBGi3fLyr9b+giWNcPwqdKoVsFe5gRK+QwfHwT8nE4nwuFwQ3abrqGzsxMHDhzAmTNnkE6n67ah/69Wa/bNxJ3m5dooi0OZHQAtQZ/b7cYrr7zSUGZ+9dVXcejQIdY53iwo06tWq2WpEcRVJU6tdDVPiwmTyQSv18s+dnLGDMPDw8hkMpiammJAhcB9sVjE2toauru7WWMWNVNK74cgCIxGQfOR55SKooiBgQEUi0WW7aYPOk97EUWRZdybgWJeRURqm83LmPFSU1KgVq1WkUqlsL6+DlEUGdWGH+uOjg7W2NQKGD377LN49dVXG+79mTNncOjQIfT29jIQT2PML/b4xqt8Pi9rqUy0KL1ej1u3btUpJtD8jEaj2LlzJ+x2O0qlEsuAEgjp6+uT1Z5V+tC2U2pNpVLI5XLI5/N1TZu0fT6fh8PhYCovVNHgnzNBEOD3+9HX14ebN28qztkdO3ZgZWWFLRyj0WhdhaxYLGJlZQXd3d1IJBJMfYW/rw6HAx6PB6lUqul1ra6uIhKJQBCEOq68RqOB2WxGKpXC3Nwc3G53S8pLOBxm713pQpcslckE6MaNG0wnms7bYDDAarVieHiYNV3yaic0jpVKBX6/n1GQWoEMuSzwwsICo3JNT0+zvoxIJFL3/SgUCvcE1O4347yRDVn3AnYfRAbzQVIaeJ1mOYqeSqXaNLbKmy22QPEmDipnV6vKVqYE+ogrLGefSC8XAg78h5g+uIIgYGVlBT09PbIPrMaiwR8H/hiL1sWG83w0l8fvB8Nwcfv9T6V/gnXNILyqTN22PEhqpixRKpXg8XiYEgSdB58NHhwcrKNayAXPEw6HwyyLRONI1BKS7bpx4wbLTvHAiEDfhQsXcPLkSQbM+HLsyZMnAaAlMKZGP8oyS6kRBMCVuKn8dRG3WMmYYc+ePSw7ytNRqOxNCykl6Tu6H9VqFX19fYzKQ4sQHlwePHgQKysrdeYNUgMLoJapp/vWbIxyuZyiXBSBYSUzCQLPBKqo5E8ZYBrr5eVlOJ1OBmSVgNH8/Dy+853vYHJykpk0UHaS7v2BAwdgMBjqGnhoG1JkIB1fmlNAvaUyUR+IPiN3z6jBlKeFeL1eloleX19HMplkGb6ZmRmmZGCxWNh24XAYJ06cwKFDh1ipVYmuEYlEkM/n2bjz40hau4VCASMjI1hfX2eZYHqGaH5S1rrZnB0eHsbKykpTehEPiqVl4nK5jEQiwf5NKxpCLpdTBCpGoxGJRKIl5UUQhLYslSORCPR6vez1p9NpFAoF7NmzB6Ojo1hbW2OqJrQNKc2MjIw0bYqmaFWuHxsbU6RyUQZ+eXm5LaC2EdSAe8nwthP3Anbf7Azmg6Q0UBKDbJX5ijLNwc1iq7zZYgsUb+IgzmwzK1Nq/GmmxxiLxdjLWarnCoBx8+hDJv0wRlVR/HnszxEWGh3GPppI4d9EY+Af8/+r9DOIaPrgVjXyjYH2DA7IjY7kpihbQt3marUaKysrGBkZkc1u8pHJZBhAIZBCwI2knXK5HN71rnchk8koNlvt27cPX/rSl+p4t8Bdg4xSqYSzZ8/iySefbJC+4kOqAiEXlE1sFqlUCrdv34Zara6jyBD4qlQquHr1KorFIgwGQ0P2joA3AYtmUS6XWembKhCUeaCfA3dNDeRoK4IgsPlL2ykFZclbnVOroMw0D1Kl5xOLxaDT6eB0OpsCo0uXLrGstXQOVyoVXL58GW63mwF+fhtaHJjNZoTDYTaX6Rzp/6vVKgNg1DHP31dqmFxfX2dqEc1KsS6Xq07FREnJwGazMQMdJekuqZ4xD/qq1SqjelCGlhqDBUGA1WqF1WpFPp/H/Pw8o5NQzwTNiWq1ihs3brAFgXSRolarkclk2EKHKhB8NpXm3cLCAmw2W9NSvHSOygUBXpWqZuYhPR9aBNFco9/xiy86liiKmJmZYQ149E4XBIH9fXp6Gv39/fB4PCybTk2qVqsVFoulrlKhFO2U62/cuMEy8kpjHYvFWr6LNpIasNF0hs1Srn+QWr2ieFenmRIfNIddLhfUavWmsVXebLEFijdxUJmdHiQ+6OVfKBQQiUSa6jGGQiGoVKo621N+P7S92Wxm7nJUblnULuKE/gSKqM/YqUXgN8MRfCh9N+NXFlX4zfLHkdf44FQ1f4m2CuKluVwuxqGlDzUvcXblyhVmLEAfHb5kTVmqVCrFOJz8woBk6fgsTTabZfqVGo0GNpsNe/bswdLSEgqFQoPcFADG9ysUCpicnMT4+HhTbh0dF0DdB12r1bLmQD6TKDc+ABCJRGC32xWNGaLRKOO5Epeb/5gSV7KdmJqaYh9PnsZgNpuRz+cxPT3NKATUVMWfO/2dpLiaBcl/bUTwtAU+eFpDq4UaNao2C6o6kIKClFNMgJYyzXy2lX8uKSPocrkUG0OTySTW1taYzrGcMg1xXaPRaFMlg2QyCbPZzChY/FhQdtPn87HFoJwmNo1PIBBAT0+PLF+2UChgYWEBkUiEaexKz0en07FFgcVika9aaTTIZDJs4UoAm4LoNLFYjJlyKJXiOzo6GC2EFnp8wx5VbeToOXzQu6dcbm6pTO8tyoDKPbNE5+jp6YFWq21ooCX1iFbZ23bK9aFQiFUYlcaaeN/NYqOpAQ9DQ9a9xoPU6n2YbJU3W2yB4k0cVGZPJpNNy+yieFc3VE6PkXdaAhob7Wg7t9uN2dnZGmgx6HFefR4/EH7QcF6Osoj/Egxgf+Hui7IgavFvy5+ETmuBXdiYjlb+uvhMryAIdaVPPvgPP/8RI3DMZ9vo5wRKiRdoMBiwbds2NtaFQgGzs7N12Wq5IJWMeDze1BmvWq2yDz7/ESWgqdFo6q5LmhHir4vX4pQzZqCGL8r0SMveiUSi7Q9NOBxm++TpJXyZtaOjA2azmWVnebqMIAgsQ9YKFJMayEYF3Rspn55oCWTAocQn52kgzYL4nqurq3U8PnJXJCc6vjGMjkdziwdgcveVP/9myjQEntuhEGQymTq6Bs/ZpsZLHrhLnzP6OX8sKsXT+dNilvZJ2Xv+nUZ9BtRASu9A/lhUbaAFKs1/fhuyjfZ6vcyOXK4U/8gjj0AURVy/fp1l8Clo7Ht6epDJZNg4STWhKetOC0FSeeGfM+KLZ7PZlmYqBIKpGiUFSe2W2dsp19OiotlY0zek3WMpUXDulRqwWTK8GxUbTQ1pFu3qND+MpiRvdmyB4k0cfJldiQtMmdNmXabSj48040EvZ8oGlFDCy8LLuKm62XBOO/IV/LfgOjq4LFhKNOIz5U/BqVVDLzTaDb/RIHAo1SGlZjS1Ws3MCWh8pKCHxgi4ywfkXzq8QUQwGEQ+n5ddxYdCobrSuRwwpkyoRqPB9773PQQCgTo913A4jGAwiAMHDsBsNsNisTCtVgJspNUaCoXqAKv0Y00/a6ccR+fAf6gpY0sf7HaCmgOVyqwEZL1eL6rVap10F318TSYTPB4Pe0krZdObNeHdaxAYp4URBWX8BUFAR0cHkyWTGkqIogiHw9GwAFMKshunYwNg4JUWE9RMKc0m82XnVk2fZAQix7vNZrPMVZAHPST3Rdl64jqnUilGpyHQSgBbFGtqN1TBICoABV2DXq9nDa3JZFKWhkGLKqpe0HNLzzWNNykxyIFQeufxVuDS+03b22w2bN++vWkpfnBwkJmy0FyhjLFer8fg4CDm5+eh1WoRCATq+PRGoxFut7vu3URVJz7jbLVaGc+W7kezZ5be6fdTZm+nXK/X6+FwOBjol859tVrNbLnbORZRcOSaEflzblZFezvHvVBD7meMHiRV4+0WW6B4kwe9IKX+9vRhIC5kMBhU7DK1WCwMlMjxMOklnUgkoPfq8b9y/wsRTaRhu2fTBfx2OAgDB9AiohX/tvx/oFNbhkaoKEpxSUNK45CGVquFxWJBPB5nChF8RimbzcLv9+Po0aO4fPkykslkw7EJCFksFvahlwsCjNlsVpFXSoYJVHqijx2/DzIuIN4kNVPxGcFcLgeLxQK3293UaWp4eJg140iDxo0a3MLhsKIxA/EPiTMuBVgE4kl9ggfcUvCt0WgasnI0PlRmpflI8mRSekC5XG7LUOPo0aN4/fXX60CjNOi4cmNEQU1OUj1sAKx0bbVa8cwzz+C1115TNELo6+urk39rFpSd5YPoBWazmTXv8XOaxpToF8PDw5idnVVs+hwcHEQ4HEYul6vjClNFJR6Pw2KxYGRkBJOTk8y4QqoGolarYbfbkU6nWYOuNGhOk8U5NQTy80elUsHn88HtdjOlEz6IkjQ4OMjAbz6fb3heCYj29PRgeXkZqVSqDoQaDAYYjUZ0dnYikUgwepl07lOGk6T0lErxtBh2uVxsHHj5O9JvJ6MhWojz9zUYDGJgYID1eaRSqQYzEbrv/f39zA5a6Zn1eDzo6+tDIBC4rzJ7O+X6rq4uiKKIhYUFdnzeTEWlUqGnp6etYxmNRszMzDCwxS/S4vE4RkZG2jLSebtHO9SQzaTi8cMW7aWItuItCSq704eMQBNpFZPjm8PhQDKZZPqwRqOR6cEmk0k4HA4GjOSCSn63CrfwxeIXGwCxIAL/OpLA50OBOkC8IrrxmfIvo1tbgkao/ZzkpJqFXq9nXfhKYTKZmJEGNaMQ1YHAHXEujxw5wrJYtFjgO8/37t0Lj8cDtVrNuJcE0IhLSM5YzUqN1WoVu3btYosIKpfSOanVauzZswezs7Ms+0tZMQLNZBXc1dXFFDHoegEwp6ldu3a1fPl5vV6Mj49Dr9cjHo8zkJHP5xGPx6HX1yykedoJgXT6YNPv6EMvLY/T33l1Aym4pLHku+z5krHRaGRVD95Q48yZM1hbW4PJZILb7YbJZMLa2hrOnDnDPqLNYnBwkDWBKYXdbmfWvhTSD4TT6YTf78fTTz+N3bt3w+v1wmazwev1Yvfu3Xj66afhcrnaao7kucS0EKLjEbVmeHiYZWR5ygRlbffs2YNDhw6hu7sbKlXNmS+TySCfz0OlUqG7uxsjIyPQarUwGo1M65juQy6Xg9FoZHq/3d3djLYA3K2c0M/cbjcAMIdGAjVUZSBQ2tvbW3evSZeajj06Oop8Pl+nsU2AtVwuo1AosEUh0Qr4+0Fzy2q1wul0svlMf7darWx+d3Z2wuv1skoP/yxShtPj8TCqFJXi/X5/nUEPz73s7+/H9u3bMTQ0hO3bt6O/v581ifFlZv4dQ0HvvNXVVUYvMplMrEmaFlROp7PlM3v48GHs3LkTJpOJLdJom1Ao1HaZncr1rfZDms8Gg4EtALu6umAwGOB0Ou+5pC/3fqBo9dyHQo3uqG/HUJqPwMaMUbv3/ochO3+vsZUp3sRBGWICLlK9SpWqZmEcj8eZpjF9PCmjodFokEgk6hpopNmJarWKW+ZbmNRNNhhyGKsq/H5wHe/I1TfOXav24w+qP4tebRaCcPdDQaWgYDAo28BF9sPBYFAxq0zXRVk6kibiMy/UYJRIJJi1Mlkv0weZrJd37NiBRCIBo9GIYDDIQJsg3LVDpjFpJXb+jne8A2azmWnVUnbVYDDg0KFD6OnpweTkpCz3kgAyZdualdEoe2swGFhJmYLGgPirzzzzDLOFJmMIAswGgwFnz56ta9Li5xABZKvVCp1Oh0AgUNfkp1bXDFKoUapVmZWarajcStQNnssdCASYoYZSp/rU1BT8fj9WVlZY4xUfdrsdPp+PmYrIZR0JqBSLRZYFlV6bTqdjgKRZBoccxJppaxOApb/z94zmejabRbFYhMfjQTwer1MfoFK2VquFx+PBU089hampKSwuLtZpEBMvXaPRoLe3l9E++Ayf2+1mHecrKyuMciQ9HvUsEKCkbCfNM+Ja0//LKUtYLBZYrVbE43FGEaFxl6pmRKNR9ryQqgbND37BH4vF6hR1iF5EijrpdBrbtm1jmUipxbnFYsHQ0FDLTFg73EtqfOvr62NNyDTWRAvJZDKMkyzt/6AEBjW90mJP6Zml32+EAkO75Xp+G3rXdXV1tX2sRCKBXC7XdIyy2WxbiikbYV7xsIYobl4Vjx+W2ALFmzx4Jzr+hU0/LxQKiMVisFqtDeCBPpDxeLyucYcPURBxyXkJN22N/OHOkgr/PbCMwVJ9KfSlykG87PoY9qkzKJfVTLkik8lArVajo6ODcf+kdA673Q6n04m1tbU6pQW+A5s+3NRwQo1KPEePuIaUwWlmvSyKIisl7dmzp6GbOxwOM5DNlxGVxM6PHTuGJ598EpOTk4jH43A4HDhw4AA0Gg1+8IMf1C0++PHmuYqhUAg7duxQBGE3b95ELpdj8jkE9jUaTZ3dbiQSwcjICIaHh2Udq6LRKOt+J1AO3HUFpGyeIAgYGhrCtm3bsLS0xLKNvb29EAQBkUiEvaRJ4oc+emR20dPTwxpAeb4qTx/R6XTIZDJYX19vcE+j+Wmz2Vhmje4L78RnsVjg9/uZ4QC500nHiP5eKpVgsVhYFpP2QxzYfD6PSCTCqDNyzT16vR4mk4kZRUiDuNW8kxuNM10j3f9gMMhoBOFwmN17ckujrnAKal7iqxiUydVqtejr65PtLtdoNIhGo0x9gnd9pHtBag+0wKF98HQX2jYejzdVlqCGP1qAyN174kkTtUGqKkP0hVgs1rJzfu/evUw5Q7r4tFgsbWXC2uFe0nXY7XZWdePPRxRFLC4uIpPJsOQET5+gY6TTaSwvL6Ovr6/pM0uxUQoM7bre3c+xaEzcbrfiGFFyw+12Kz73P+yKCFsqHm99bIHiTRw85UFaAqcPrCjW7IZJa5bnH5KwPGUz+cwFABRVRZzynkLQGGw49qPZKr4QWoKtWl8K+2L1fZh3PgV3OYpcsXZupVKJyfpYLBY4nU7cvHmTNbrQccna2Ww211FA5BppqPmHspDUGU3bJJNJiKJY1xWtVquxa9euhmuhUhK9cGw2W520kclkwtjYGJN4o5cHjaOc2LlGo2EZaj7a0c7lt1MCYdLMMGm48kCbv5cqlQp9fX0N+6GPPemnSucBZaPVajVrkCIAncvlsLy8zJpkduzYwSggBCCp05zKcfF4nAF2aYMn8eKBepUCaVCFJJPJMH4uf/9zuRyWlpZgs9nqQC2NBS0WTSYTWzjR9VN2lOZROp1mC6dmYTAYWLZLmgWmMaXmOMp88tQJ/l4RwCUervTayYXv5s2brGFOasxBzmjEGeQXzDxnkOYAAWLp9dOC1G63M3UEfg7Th9hgMDBFBBpHqbIE7a/ZvRdFERaLBQCYVTK9C6gCQSC1lcmBxWKpy4S9WdxL0gumZ0maTaZKTrVaZdJm0oW+KIpMx5lC6ZnlQ+n9cK/Rzn7u51jSxYXcGNFceBDmFQ9rvBkGHxs1h35YYgsUb+KgDxnxXaXNG/TBJxF7XteTF5aXGilUKhUkNAm87nkdaW2j8cXPxDP49VgEvAhVSVTjD1Q/D8vAI/Bm4shXGrPO9BGIx+MwGAwolUqswYlKkwaDgZWi6SWqdF30c76Rhsq4xAduBWYoWpWSyO6UzptKysTRNhgMbYmd84sAJW4dAY1mQXxI+ohKaQ8AYDQa4fF4mu6HmpqI/0iZakEQ6rilJpMJCwsLUKvVdQurVCrF+L3btm2DzWZrACF8OY72WSqVFEvxxIVt1RlNDUvEK6egvyeTSfj9fsVmPGqy5BeMcudDZflmQSoOND68qgivFMAvVqUAi45rNptbGkosLi42LaHOzMxgZGSkpbwTD3J55RVBqFd7MJvNjAIhbTaz2+2MjyzVMefVBQgQ0r2n+Sa993a7nTW4ySk50D1qdizKQJKd8xvNhPELZqVx3L9/P2ZmZloCZ3KyI4DIBz2DtCCgf/t2yeC9kcWFNLYUEbZUIzZDbIHiTRw6nY65RRG/jqcQELhspfhA9IZcLgdBELBuXsdr1tdQUtWXgbWigP9fOIwfS9db8MZFM/7A8Mt48vBjWF1eRLhUgsvlYtkRyl4mEgnk8/k6Xho1EVBmqlgsIpVKsQ+ww+FouK54PM469IlLLS2PEgcwGo3C5XIBqIEgOfoERbNSUjwex/LyMjKZDKNsUKaPspbt2J329/fDYDAwjiMf9HeDwYD+/v6m956aMKanp1llQBCEOhWJgYGBlhkAApZUspaONfE+KfMgV7Ljo1U5jhY/RIGRK8UbjUa4XK6mpgomk4llHqVmKdS0KAgC47a6XC5mN0zatcSlNxqNyGQyiudDUmTNIplM1lFXeBtukvozGo2s3E/nKR1Hg8EAn8+HRCLB6BK88kgymYTT6UQqlWJ8WCVjjj179rTkDEajUdYwJ22ypHPU6XTwer1IJpPMyluqhtLZ2cmaRJUWTgMDA2yxIydfR/feYDCwBZicksPIyAhEUcSNGzegUqnYAprmBq9kQPu9n0xYO9xLer8pAedDhw4xfXIlVQmv14uenh4A968usNlioxYXP+yKCFuqEW99bIHiTRzU/RuPxxlPkzi4ZChAndwkUyZtgCIupNlsxu3527isv4xJ8yQgwQDOCvBfA2vYW6iXZLpaHcBX3Z/CoW09GOzvRSiwBqPRyJr5BEFg4IVMKKgxhUr+lKkmaax4PI6DBw/i3LlzrPGGQhRFGI1GjI6O4sKFC4yXzNuaarVamEymuqapiYkJ1mhHoO/ll1/GkSNH6mgO9GIhHh9ldqkrl3jRfOkbAMvGt7I7dTqdGB4extTUVN35AXdd74aHh+vUHpQAJtkpSzPiBOR4ExKl/fDXQx9f3n2L9HKz2Sz6+/uZxijftEUObbQgaAZCisUi+x2pIvDnbbVa4XA40N/fj5s3byp+QN1uN65du9Z0rPnqQjQabWi0o8wwSUspUQP4TKhSEEe3p6eHNUdR85fVaoXL5UI8HofdbkcsFmOAm4AzAAZ8+vv7cfXqVUxPTzecs8/nQ29vL65cucLoEtJMqdvtZgsZv9/fdJFSurOApbkt17DpcrkwMDCAmzdvMnqRyWSqoxeNjo5icnKS/Ts++AqUyWRi2UDpc00VAv6aKfsu1ZCm/RFHnT9Wu0YqfLTKyrZa7LUDnMfHx3HixAnE43GYTCY2p+m9PD4+DpVKxdQFstks0y4ulUpYW1tDIpHA4cOHH0pgvBGLix92RYR2Fhc/7GP0ZscWKN7EYbfb0dPTw0CnXHOT3+9HOp2GIAgNXb8EaABgdPco/nf2f+NK5UrDcUYLJfzXQBB+SRn6f4vHsD70URzbVnupUce7x+PB6uoqUqlUXemzq6uLNSLp9XpWTuTBLJWGOjs74fP5MDU11fCh7u/vx759+3D16lXGKeVBaqlUQjqdhs1mg8fjwcTEBF599VWmHEH0imQyiVdffRVArRFvZmaGdXxTRtHlcjGVBgIfRFXgm4Soua8Vl0sQBDzxxBPIZDJYW1truP7Ozk488cQTEAShabZIq9VifX2dUQOkkmqVSgXr6+tMq1VpPxaLpc7RUI6qQs1EBBDlmmQikUhbPDaS2zObzYoGDkCtgc7tdit+QIPBINsfXS8FD6R4bWXph4K4v3q9ni0u5agBdJxW10WZU6XGNmpcs9vtCAQCjF9MFs9+v5/JmCk2vt7hyZfLZSwtLTU15qBzbrZIoQw2ny2mIFqD0WhseT+oWbPZwokk14hTKqWYiKKIfD6PYrHYVKWAgCk15knPWavVIhaLtd1s1G5WtlXGuRVwbkdVYiPVBTZjbMTi4oc9tsborY0tULyJg181kvQUARriAfIlqZ6eHgZeqCM/EonA6DXiczc+h6uVqw3H+NF0Br8VjtbpD2dFPf7e96/w5Ac+DrfNVEcxKJfLiMfjTIKLLyGTRTIJ2PMgjkr/1Gh36dIlzMzM1AFP2nZmZgZut5vJkhF9gILK51TOPn36NEqlElOJAMAAXzabxenTp2G1WvHKK68wXiNRCYLBIE6cOIGdO3cyQAzUK0fwPNx2gKHX68Wzzz6L69evY3FxkXXp9/X1YefOnezjd+bMGWbOQHqmq6urSCQS2LZtG0KhEGtMktIeKLO9urqKW7duKe7n0KFDLFNot9tlKQYOh4Px1jWamisVP4fo5+24UfHlv97eXlmlDyr/CYKg+AGlZhOeo8lTdShzy2tW8zQDg8HAFjFdXV1IJBLo7e1lWrw6nQ4WiwWRSKSuHKl0bfx1OZ3OOtUIv99f19i2vr6OPXv2yD6LHo+HGZuMjIzIbrO8vMwoCM2MOWiBUa1WFVUMqDmQQCZtT78vFArIZrOw2WyMWy23r0AgwNQFlBZOq6urrAeA1C6kyhL0rvD5fLDb7Q3qGwCwsrKCeDzOzkeONkYav62u/0FnZUdGRpiDnhyNa6PVBYDNx02+38XFVmyN0VsZW6B4k4fX68Xw8LBshnP//v1MEWFhYQEXLlyoywQuLi5C9Iv4VuBbiBSkhhwi/mUsgU8kknVMiltiF2489d/w8+98Z8MDSHJD0o81UPswxeNxJmpP2W1pUOn+2rVrTIFAup9SqYSJiQkGsEkqip37ncxtuVzG5OQk0uk0dDpdg7kCZahSqRReffVVZDIZlnGnMaIM3MzMTJ0ihDQ7Jc1WtnPfnnzySUUXrenpacRiMVSrVVb6p/J4sVjE9evXmSEIgQt+DMkI4/r16ywjKbefGzduYHx8HN/97ncRDAYbFiBmsxlHjx5FMBjEtWvXmHYun912OBzYtWtXW25UtJALBAKK9AC+/Kf0ATUajfB6vVhfX5flp2o0GibplcvlGGeYb0akzO7Q0BAuXLiAmZmZhvPxer3sfFpd2+joKK5fv96wn9u3b8Pn8+Gd73wngJoOs3SbcDgMr9eL7u5uXLp0CZVKBVevXq2bi2tra/D5fEyOjow5pJQoyvwmk0kEAgHF6gc14tH4UUUAAFtsqtVqRo0pl8sN17+wsIDR0dG21AWogkXuddKxNplMbOEaCASYZrj0+mlRRFJr0ueaGnGLxWLT6s+OHTs2NCvbTsZZbptoNMq22Wh1gYeVm3y/PPAfhtgao7cmtkDxJo9QKITZ2Vno9XoMDAwwQFMoFDA7OwuXy4VoNIpIJMJ0Z2mbWc0szlTOoFKtB3OmahW/G4rgndl6sPGy9mn0/9M/xXt6O2XPhZqNmn2sqaO9mSIANbNJG6iAu7xbkqOjLm6+o5+0dQuFAgKBQN120qDtSCoMAOPYUtaqXC4jFovVSWYpRSvuKR9KL7VEIsEyW3LlccqGieJd1y+eR8mDdwIfNAY0nrSf5eVldHV1weVysQ8yz3F1uVxwuVxYWVlBOBxmwEKtVqNarTJN2FKphHA43HbWrRk9oJ2w2+3o6OhAKBRilQEKOj+3210HrPjzJgkwq9XK+L1K9whoL6N45coVZqfOB1Ucrly5gj179jQ9VrFYRCwWY9el1WrZvc9ms1hcXITD4YDJZKrjL/NUBcqK37hxg5nImM1mds6hUAgnTpwAUHueeDksuea/QqGAW7duYW1tTfH6pRJwcg1AHo8HkUikTv5POk6k0HH79m1GFaHrz2QymJ+fR39/P/R6PaNDSY9FoHJtbQ3nzp1TvP5sNrthWdl25geAlttspLrARmfBN1vGeSu24q2ILVC8iUOJf0a/I+ev+fl5VCoV+Hy+WkazWsFpzWmcU59r2GdPqYT/EghjO9fUEhfNeMH7L/CRf/4b0GuVm1jkmo2kH+toNFrXjCbNAgN3gaUUEPP/hueL8n8AMCpGqVRiltblcrlOs5iCABEBSVKtoP0IgsC4tvRzOeBGYHwjPhL5fJ5lic1mM9snNcFR2Z+uUzpO9HNBEJiiSLVaRSaTYdtTBj4ej2NmZgY6nQ4HDhyQpQ9cu3YNV65cYePDU15I/eTKlSus6bFZ1o14qaJYs/2VUzK4l+ycTqdjiwY6J55SQ+CfN0ah66hWq0ilUsyQpdn5kE6u0rVdvXoVExMTdconPJWJqht0HkrHWlxcZFlNakzl730ul2Nl92b85WKxiKtXr6JQKNRVbagZNx6PY2JiAuPj42yRyi8G+epHoVDA4uIiSqXSfUnA7du3D//wD//Amvvk1Gl4Xju/iKOFLpn2OJ1Oxl2Wc0+02+24du1a0+ufnJys443Lza12srLt8oBbzaHp6WkcPXp0Q9QFNpqb/LBmnLdiKzY6tkDxJo52+GeLi4sIh8Os8a6IIr6D72BOmGvY36FcHn8QDMPBZYvOiLtw0vFhdLi7kMukoHc4FDMGfLORHH+ZTAKoK5/AGgUBG/oZATug3vmL/zckx8ZnSon7q9VqMTQ0hKmpKZbFloJwAoCk1kHH4o9HIJw3ZZB+rGg7Xme0VSiNI2Ux5aTAqPRPmXTKgPKZcroOAhFEVeHBPFEvSqUSIpEIfD4fAxN82Gw23Lp1i1FQlBwG0+k0bt26ha6uLgDKMmHLy8tszpIChPR47WTneNvYRCLB5NWIy26z2ZjcHHFWacxpTGmcg8Egs/KWO5/V1VUAYBx56f2w2WyYnZ1lgIzXOqb/UnVjdnYWfX19iscKBAKsoiMXNBdNJhOTrJMz5jAYDEilUg1ubrQPk8mEaDTKFl98lYWCfk6yan6/v2lGtZUEHDXSkjoNr5aTz+dhNBoZ6CV+u7Txkyy7Ozs72cJYrsHYarUiFAo1vf5kMsk0pJtZt7fKyrbzHm5nDoXDYSSTyQ1RF9hIbvLbVQ1jK7bijcQWKN7E0Q7/jLJAWq0WCSRwHMcRFsIN2/50MoV/G4mB9pQXtXje8CFkfYehzeVY40qzjIHH44HH48GNGzeQTqcb+IAWi4Vlvwgo8Bk+AHUUBgJf0iD5JtLRlWZueZBqNBpx5MgRvPrqq8hms3XqE+TAtWPHDly5cqXB5lgKyKmxj2/wo+ugTLTVam3r3jUbRwKfzcrDGo2GASM6J8pC0u/tdjvi8TjLKkuDsuOU3ZQL+iDzjYRyVAVqkiwWi7IyYS6XiznE3QtnUmnhwFM9KGPIZ83JSpiAO40JLWzo7yTr1er6AbR8zoDW1Q1q5Gx2LH6xIdVNJj1ev9+PcDisCJw8Hg9u3rzZ9FjZbBaZTIaNCb8IBe4uSomf3s49ayYBFwgEoNFo0Nvbi0gk0lBJcrvdrGHPbrczWUU55zdaxCi5J3o8HszOzra8fpPJxCgvzazbm0U77+F25hA/hverLrBR3OS3uxrGVmzFvcYWKN7E0Q7/jEqL8+V5fEf3HeSFeh1djSji30Vi+OnUXee6KWzDKe9HobL4oMZd3m0wGGROWs04ccQx5QFoKpVCPp9nndbS0ilwNwOs0WjQ19fHeIVAfXObINSMKdbX1wGgATxTVonOj3SISaeYgIfNZsORI0fQ0dGBmZkZxlPmwTBPE7Db7UxCjDdFoY+P0+lkHN9m/LtWmZedO3cyiTCl8rDNZmP7Jd4s0UnMZjN8Ph/K5TIikQg7HzmqCmXAm80hAub5fJ6BJBqXcrnMqCXEUSaQTgAulUoxmTCLxdI2Z7LZwkGv1yOTyTDOuJR7e+vWLTidTmi1WsY/VZKbo983u34ALZ8zGlc5YEzjTcYlzfbT6pwrlQq6urowNDSkCJyo8tHKGc9oNEKv19dVFChoIUh2ze3yXJW48vS+0mq1TWXr+GMpOb9Jr1/qntju9fv9fly6dInxjmnc5azblaKd93A7c4gfw/tVF7hXbrLS++rNUMPYiq14mGMLFG/iaMfdpq+vDxcqF/CS+BJEoT6jaq9U8AfBMMbztWxBTtThJfOPIeo5yj7sfIZqeXm5acaA+MtUrqYPLQG1SqWCRCIBs9nMtJP50i0BX4vFgt7eXiQSCdYAR4CYyuMOh4NJvxF/WMqXJV1YoKZDfPDgQVkpJOJvUve9tCseqIGZjo4OOJ1ORRBqMBhagjmyi242jisrK+ju7m6qP02OdyTvJSdtptfrGciXZr0J2JLCRDP3uMHBQQSDQXYOfJYcuNsgZTAYEAqFGPeVz7Dn83lYLBZ0d3djYWGhJWeyWCxiYmJCceHw6KOPMp1rXmqPssHZbBb5fJ7x2Mk6WMph9Xg86Ovrw/r6uuL5dHV1QRTFptsMDw8jGo2yMZKTCNTr9RgeHkYwGGz6vFarVQZE5M6ZnM9UKpUicKpWq0xqr5mD2sjICC5fvswaY+W0g0mnuNkcaYfnKn1fydE++vr6IIoiwuFwS+e3+71+j8eDSqUCm83GnrNcLgeVSgWXywW1Wt2WdXs772F+Dik5FUrHUGlx0U7ci/NZs/cVv+iXi3tVw9iKrXjYYwsUb+IQhObuNnqjHt+tvIR/wD80ONQNFYv4L4Eweu+U0K9pduGs7f1Iq2zQ3AGHBIjVajWzVW7GiVtYWEAkEoHVaoVOp2sofRYKBcRiMeZGJ3WoAmrlxf379yMajWJ4eBhqtRrr6+vso9fR0YFyuYxEIgGLxcJk1OgDTudN4Jn/8KrVauzatavhmKlUirmySa1+yVyBspzlchm7d+9W1JglMKekCzw2NtZW5mXv3r3MvMBsNrNxFEURJpMJY2NjAGqKH6FQqO5YxKWkY/AcV36MKDweD9uPXCm+r68PU1NTCIfDbMHALx5oYUAax8TjpePk83kGplOpVEvO5MjICGZmZpouHCYmJhhlRc6emebf9u3bcenSJSQSCVkXscOHD8PlcjW9/tHR0bqxlttm9+7dEEURJ0+eZNdPlQTiOo+Pj2PXrl1Ip9OK+xkbG4Pf78eJEycUz5mcz2js5YCTSqWqc1CTc7IcHx9nbnUzMzOsesI3B1arVQwMDOCRRx7BxMREWzxXpaxjq/eVyWTCzp070dHRgRMnTiAWi9XZRefzeRgMhjd0/XLjuHv3biwsLMDn88lyiklZpVUWtJ3rojmkJEXIS/9tRLR7Tq0UY3bu3LlhahhbsRVvh9gCxZs8lNxtNC4b/jj4V1iNNhpyPJXN4XeDYVhEEWnBgtIz/zeilW2ozM6inEoxEX1BuGsX7PV6kUqlWvLmKKtAGULpNlRGd7lcDfJVlKHx+/0IBoMsw9PT01O3H8qaeb1e1uTFZyaNRiOMRiN6enra8oCn0mlXVxdWVlYadHi7u7thMBhYU5eSxiyBuWb6wjdu3GiLn2mxWBT1p/ft28d4hbQNudvxGtXUsU/nIG1qpJ93dnZibGwMU1NTWFtbYxln+jmV1E0mE9M8lvK2Sd1Cq9WiWCwyrjfNA3I8y+fz6OjoaNmQ1WrhsLa2hmq1Crvdjlwu10AzMBqNyGQycLlceOaZZ5q6iAFoi8PZaptjx44BAJNBo0qCwWDAoUOH2O9b7YeO1+qcWwWpQZw+fRqJRILdb4vFgkOHDrH9HDx4EOl0GsFgsE7vW61Wo7OzEwcPHmzbRauVSkE7+/F6vW2ddzvX32wcHQ4H5ubm2PtK2vjYLr8daM9lLBQKKYLeN4OP2+qcPB4PXn/99ZZVK7fb3bRK0k6VYCu24u0SW6D4IQief3Y7mMT/uHgFr97+XUAXadj24/Ek/lUsDjWA6PBPwPXjvwtYvPDNzODynWYzq9Val1XK5/Pw+XzI5/MteXOtsgpqtRqLi4vI5/N1LmH0ss3n85iZmWnJB9Rqtejo6MCtW7dYCZ0yXMVikZ1zOx8b4lSm02nWKMefUzqdZmXOZh+1VCpVpy8spwssimKdxqpSx3s6ncbs7Cx0Oh0GBgYYqM3n80x/GkBTjeqxsTFYLBbmHshL0hFgs1gs8Hg8dWBIbrxJ09flctVl1Il2Qhl7fjHFH4uqAtRw1IwzSc1WzRYOfAbcbrfLViXIqrmvrw/Dw8OKrmatzudetjl27BiefPJJTE5OIh6Pw+Fw4MCBA3ULxHb2MzIy0vKcW0UoFGLKGh0dHeznpJscCoUYCH3qqacaFkVdXV11gLfVeberUtDOfoLBIDo6OhqqG/x5txPNxjEej28Iv72d8aGGtVbSfxvdsNbsnOLx+D1Vre5HDWMrtuLtElug+CGJi0tx/MkrM3h16STM3X8D6OqpCVpRxG+FI/hAOousawym5/4fuPqOAAD72BC3jmx8ybyBtHFbZQz6+/vr+IBSG1fKEofDYQbIpBat6XQaa2trGBwcZJxPOf5dR0cHUqkUa1wiMwsCZAaDoS0+INCeE5/ZbMbS0lLTjxplianszJ8Tr3zg8XgQDAaZGQeNkcVigVqtxsDAAFZWVpDNZhuAvc1mY/xtAE01qldXV5kkXblcbmhG1Ov12L59O9PQpftDYGZ9fR3JZBJDQ0PsZ9LsP9EVtFot40Dz+roAGMeY+N78OTRryGoGVqxWK7s3JPXF3zOeewrUMtp9fX1N50E7HM52ttFoNKy5836inXNWCl41QCqlRvODB2HtNnYpXT9/PHpms9ksA49S0NfOfpTm9b2CR6VxbJd324rfzkuSKV3Xm9Ww1ix7ze9fbp/tKlRYLJb7VsN4M6Kda9+Krdjo2ALFD0H88788je9Oh9Hj+iaMvT9ARfJecJcr+EIwhF1VHfDe34Pp0U8A6ru3ll7YGo0GkUiElcgpO+n3+xGJRLBv376mGQPiQ37729/G6upqAzXCbDZj27ZtuHDhAvR6PWuU4qkKOp0O+XweHo8HqVRK0Qq4u7sbExMTLGtsNBrZNiS3try83NZHph0nPqDW1EagWU5jNhQKIZvNsiwsz3Ol66TGnps3byKTydQ1GJKu7O7duzE/P9+SPgC01j3du3cvU2ngbY61Wi18Ph8OHDjQkr+7tLQEp9OJ1dVVRg3h74fJZILT6WRZYDkpMZJuk+ORS6NdsLJ79+6mnFGee7qZ4kEYIbwREHY/jV10PJ1Oh6WlpQZ5M+nxNoPaQTu823b47e2AdAKgrSQL76Vh7X7n0b0oVDgcjvtSw9jo2DIT2Yq3KrZA8SaPUCgET/YGnuj6U1yypxp+P1Yo4o8CIXTs/mng3b8FWHwN21ADnJy1bC6Xw+LiIrxeL8xmc8uMQTQabTC/AO7KmwF3rZxFUawDT1Sm5xt2AHkrYLLDlbq+AbUMRyaTYdrKraIdJ754PN5Sz5ZArzRbSiCU3OUIoPKGI7RtuVzG7du3N0z31GKxNC2Nt8PfJRk7+pDzQVlZt9vNzBeoY50H4FI1kGbRbpPQRnFvH2Q8KCOEjdKpvZfjZTIZZDIZlMvlBmvyfD4Ps9mMQqGwqdQOWvFu23k+2gHpRNFaXl6WtW6nKk27DWsbMY/uRaGCrnczyK5tmYlsxVsZW6B4E4coirh+/RoWNZ/HJXPj738kncFnil74Pv63QM9Bxf1otVrEYrGm1rKxWAxarRYul6upDNLExAQAoLOzk5XTSec0mUzi5s2bAMAc2/gucpLu0ul0CIVCEEVlK+DZ2dmWrm9kd9sqeCe+ZvqpdN5KWRVBuOsmp5QtValUWFlZgUajgcPhUKSPkAPgRuieNsvytMPfJXBKWsc8mCcecSQSgd/vRyr1/7V399Ft1ecdwL+SrDdLlt9ky04cJ05I5BCSkDmx4yaMAqGUMQ6DtIMuZaG0p1uXrAkw2uylpZyeLvRlL6EtlLYMSiHLCRuspDtQSEpzCuTVkDdIHOc9dmRbfpEtybL8ors/2L2VbEu6iq6le6Xv55ycUkuWf/rpXunR7z6/5/EjEolIx5H4RUGv10+qBpLuxiVAmdzbTMlkI4RU69Sm8hymes1MJhMCgQBGR0dht9snvYf4/X4IgoBAIICTJ0+qqtpBuvntcoL0eClaYlqVz+eD3W6Hw+FIOl6ljiO5Xz7VlJLAZiKUbQyKVWxgYAB9ff1wFy7FkfGjMbd9eXAUq0vW4T3bMtxkn4eSBI8jtsNN1I1rdHQUfr8fZWVlcVcM2tvb0dfXB5vNJjVviCa2VhXLdU1VSktcWfT5fCgtLY2bqtDX1yeNK17XNzEdIxk59VPl1KotLy+X5jLeaqnYWlpsczxxzq1WKwKBAMxmc9K6sADS3hUuJ3gKBoNSsCAGuSIxWIi+XVw9FoNhu90uVRERxyPmRU9V7ULuxi5ROrm3mZTJ1IBUVwHlSLTCK57r0aX+JhIEAWfPnlVltYN08tvlBOlyUrTEhYNkr72Sx5HcL59qwWYilG0MilVMXNFsrnoA/ee+hjfNo7BEBGzWXYtZ134R/Tojxv6/nm4iwWBQClQTtZYNBoMJH0fcNJZsVaWoqEhKJ4iueqDT6VBUVASr1Zq0bJkgCCgqKkIoFEIwGJxyVVbuyqTcFRPgD7VqzWaz9PfE1rLiY/h8vrirpUajEQMDAwnnEQBqa2vh8XgS5m/LGY9Op5PVmtvj8cTd1CgGu2Lpquj0GPH/i5d/w+EwAoFATI53JBKB3W6PGc/evXsnleTr7e1Fd3c3brzxxqQbl7Ilnc09mUxpUHoVMNkl62uuuUaqGz5VF0az2Sxd5SkvL0+aB6+WagfRXy7kNt2YipiilajFtdikJ5lUj6Nkx2y63fMyKdNpQUQTMShWMXEVY2x8HPdUfQWh7p+jsfgzKC9ajHG9CSP//0acbBXDbrfHlFOL11rWbrcnfRw5rVXtdjtsNhsGBweltskGgwFFRUVwOBwxG7biPY5YO/ny5cvwx6mtPHFlMt36qcAf6gJ7PJ5JtYPr6urQ0dEh5dVOtVrqcrlw7tw5DA8PS4G8SBAEhEIhWK1WzJ07F7W1tWmNR1x9S5Z/V19fj87OzrhNBebOnYuLFy9KTTEmfliKVxlmzJiBgoKCKWsrz58/HxUVFRAEAS0tLejo6IDBYJjUnKGjowMtLS247bbbVPehnMmNTUpQahVQziXry5cvo7CwUDqvJwZ9YnMc8cvVVNRY7UD8cpHo/JATpMtpcS33tU/lOJJ7zKrty2c8mT6HiCZiUKxi4irGhQsX4Bk3YvHoAwh2R3C+5zysVqtU3ivZKkZNTY3UEjVZa1kg/spD9OMkaq0qttWdNWvWlPnCclIVqqurUVlZiba2toS1lcWVSaXqpyarHSyuzgWDQSl3ULyPzWbD9ddfj0gkIu1on7iiJggC5syZI1WVSGc8paWlsnbOu93uuB/qOp0OdXV1OHTokHQJeOLrOjIyAofDgfLychw+fDhu3eSysjIUFBTgwoUL0Ol0MRskCwoKpK6JFy5ckNJn1CIbG5uUoMQqoJxL1mIw6/P54p7XTqdTyjvWWrWDROeHHHJStOS+9nJXr1MpJSeHGkqgZeMcIorGoFjFdDodKisrcezYMemSudhMoa+vD2azWVYDi+iWqMlayyZbeUjWWjW6rW5PTw8cDofU7a2np0d2W12xVJLD4Zi0KlteXg69Xo/u7m7Mnz8/pY0Z8VZMolfL4tUOPnXqFFavXh2zyiV+0Ec3QmhoaIDf74fX68Xw8LAUPIqrrQ0NDTGVK652PEePHoXf75c+IIaHh2NWp8T7iSX4Em1qXLlyJd5++20MDQ1JVw/EElNGoxErV65EW1tb0vqyVVVVCIVCMZuxoo9nsRNdb2+vaoJirW9sSncVUO4l69raWuk8nuq8vv7669Ha2qqpagdKNd1Q8rUXHytey+jKykrFSsmJ1FICTYubAym3MChWseimG2JgKDZJiA4MxQYWkUgk7i79ZC1R3W63tFoWDAalBhmRSARXrlyRVh7Exzlw4IB06dNgMMDpdMbc3tTUhJMnT+LSpUvSykNtbW3MRis5pZIqKytRUFAAj8cjPU51dTVGR0fR09OD9vb2lDZmyKmfKn6AixvExLQP8XGSrc5VVFTgk5/8ZNKNZolEjweIH/CKl6vj1UYVWy+Lx8tUmxp7enpw0003AQD2798vlWjT6/VwOBxYuXIlFixYgLfffjvheHp6emJScMRc6+hOdOLPE23YyrRUN/coUVVDLjmrd+mu8Mm9ZF1VVYXy8vKE57V4LikV0Ci1einnvE90fqSyqS2d837imIGpS1b6/X7FNqSprQSa1jYHUm5hUKxi4ht2ZWXllC2Dw+Gw9MbX1dU1Za5ndD3XROWtxBUTsTZwX19fTIA1MjIirTyUlZVh9uzZUp6oxWLB7NmzpdbEANDX14cLFy6gp6dHGo/YKERO9QGxVFJ3dzc6Ojpi8qDb29sxc+ZMWCwWBAIB2Rsz5NRPHRgYgMfjkfIjxdXN6upq6HQ6aYNHslWudC9ry2kGIDYNSVQb1Wq1wmg0ypqfxsZGNDQ04NSpUxgcHITD4UB9fT0MBoP0eiRrTmC326UKGwBiKnSIFQysViucTqeseciEVDb3pNsOOBVy/pYSK3ypXLI+ffp0wvNayYBGqdVLNdVNjhYvUBffixOVrDx9+nTSzcpyxq3WEmha2hxIuYVBsYpFf1iLG8yiiW98p0+fxqFDhxAOh2Gz2aRv+l6vF7t37wbwh5XieOWtBgYGpGB5fHxcKrkmrkoYDAa0t7fj/PnzeP/992MqCwSDQXz44Yfwer248cYb0dfXh927d08aT09Pz6TxJCqV5PP50NXVhUgkIlXPEBt7XLhwAVVVVbDb7bJWueTUTxU7w4kNTsQc3qGhIZw/fx4ulyulDR7pXB6W0wyguLgYkUgkYW1Us9ksVfuQs3HFYDBg0aJFVzUeMU/U5XJJH+omk0kKnMTmJmJOtVrIXSlNdgzJaQcsl5zVOwCKrPDJvWR9+vRpWee1EgGNUquXyR5n4cKFim3siv5bU7VTjx5zokA9uqFIvNVrscJNuuNWcwk0NaTXqJ0a8sBzDYNiFZPzYa3X63HixAmEw+GYwMhsNkv1gA8ePIj58+cnbHgwPDyM/v5+qTJE9Eqp0WjE2NgY+vv78cEHHySsLHD48GF4vd60x2O326VV74mb+sTScn19faiurpa1ytXR0ZFwNURsGT06OorCwkLp74m1hoeGhjA4OIiioiK5L19ab1hymgFYLBYp6I1XG9VisUgbpNLZuJJKcwKr1Qqz2SylTohfnsROitHl3NRA7kppsmNIqRU1uat3Yqk8JcaTbIW3vLwcb7zxhuzzOp2ARqnVSzmPo1Td5FTG3NPTk7T8XbLVa53u4/KWyWqdyyklxxJo2qSWPPBcw6BYxeR8WFutVvj9fthstklBpnhpu6+vD+3t7QkbIIyMjEh5ogAmtWcGPl6V6OjoSFhZoK2tDWNjY7LHEy9wbG1tleoRT8xBFQQBBoMBY2NjOH36dNJVrpkzZ+Lo0aMJV0MuXbokXY6cqumI+POOjg5ZjSTSfcOS0wxAfC7JaqOKG6TSyfOU25ygo6MDoVAIc+fOhc/nk648iKX6iouLEQqFVFV8X85KqZxjSKkVNTmrd1euXAEAqYqJEuNJtMJ76dIlqXFPOu8zcii1einncXp7e7F06dK06ybLHbPP55NV/i5Z6Uuj0Qi3242PPvoorXGzBJo2qS0PPJcwKFYxOR/WTqcTbW1tMBqNU25sMplMGBoaknI84zEajRgfH8fY2Fjc9sxi9YZ4b/xWqxX9/f3S400lejyJAsfBwUEIgiCtOEZ3WTMYDDAYDAiHwxgcHMSiRYsSrnLJyRsUy6WVlJQgFApNquUsVk1INo+AMm9YcpoBDAwMSCv5iWqjihuk0snzlNucQMzxLi8vR3Fx8aQxiS2j1bbylGylNJO5p3JW78Qvqpla4ZPTuEfO+0y0eF+IlVq9lPs4Npst7TxouX+rp6cnpfJ3iVaB6+rqUFRUlNa4WQJNe9SaB54rGBSrXLIP61AoBIPBIAVy0cGc0WhEQUGBtEqXyOjoqJT7OdVKqVg5IBkxcE7W4GN0dFQKHM1msxQwiYFjWVmZtAFQvC26y5rYUMLhcEjzFG+Vy+fzJV0NEVc+gY8/KCZ+uQiHw7LmUak3LDnNAKxWa8yHZ6LaqDpd4prIychtTjAxx3tiPmQqTQwyLd1jSKnnJWf1TmxvLrZBn/h6XM14En1JTaVxT7p/S6nVy1QeJ926yXL/lk6nS6n8XbJV4HTzt1kCTXvUnAeeCxgUa0CiNz6xxW5XV5e0OS467WFoaAgul0tqzBGPyWSKWSGeuFIK/CG/NlG3NrHF88DAQNwGHxUVFfD5fOjv78f4+Dh6e3ulANRqtSIcDktd8cQvAfEaSog1j4H4GzPkrIbU1tYiEomgp6dH+jIx1biTzaNSb1hymwG43W4cPHhQ1gdaOnmecsdTU1ODixcvanblKZ1jSKnnJedviQ1wLl68KB2f0dVA9Hq9rMY+omRXN1asWJG0cY+c80PO32psbFRkrlN9zZQ8P+L9rfLy8pTK38lZBU53QxpLoGkL88CnF4NijYj3xqfT6VBWVobu7m6MjY1JK5viiq94e7Jv+haLBaWlpfD5fIhEIrBYLFJwLTbNKC4uRmFhIS5evJiwW1tdXV3CBh/XXXcdTp06hUAgMKmSQTAYhF6vh8fjwbJly7Bv376EDSXkrGDLWQ1ZuHAhXC5XwnGLDU4SUeoNS+4KTqY+0OSOR6/XZ2Xlabp3YWdyRU3u3+rr68Px48elahBmsxmjo6Po7e2V3dgHkHd14/Tp01ixYgX27NmD/v7+SZtsLRaLrPNDzt9qbW2F2+1Oe67V+JqJK9JyAvV0r+6kgiXQtFPJgXng0ysng+JvfetbePzxx2N+5na7cerUKQAfV1p45JFHsGPHDoTDYdx222146qmn4HK5sjFcWRIVnweAOXPmoLu7W0qjEDfDVVZWAoCslcmamhqMjY1hbGwMwWBQ2iBls9mkfFK32y2Ve4vXrU0MxA4cOIDe3l4pUBMbfBQXF2Pfvn2IRCKTNuwZDAYEg0H09/fjpptugsViwb59+xAIBKQc1qKiIjQ3N6OxsVH2/MkJHsVxJ2pwkoySb1hymwFk6gNNbgCe6ZUnJXdhy2nMMfH1iO5mqJRkr73T6cSpU6fgcDgwPj6OUCgkNfYpKyuDwWCIaeyT6LnJvbqxePFirFixAvv378fAwIC0Mm2327FixQpZ50cqf0uJYyiTx6Lcv5VKoJ7uKnAq5PwtrQSOqdJSJQfmgU+vnAyKAWDRokVS7UwAMZfDH3roIfzv//4vXn75ZRQXF2Pjxo2455578O6772ZjqEnJKT7vcrlQVVUV04nN4XDI3tgkrnR0dnbC6/UiEolAEASpvmxFRYX0BvFJGd3axAYfOp1Ouk9tba3UZW10dFRajZ44joKCAoTDYYyMjKCurg6hUAjnzp2TVqTmzp2Lurq6lOdRTvCYqMGJHNl6w8rUh6fcADxTgbqSu7Cv9oMxG935UmnsU1JSokgDi87OTnR3d6Oqqkr6Miz+b3d3N7xer6wNpHKvpLhcLkWOoUyugsr5W1pNV9BS4JgKrVVyYB749MrZoFjMy5poYGAAzz77LLZv346bb74ZAPDcc89h4cKF2L9/P1auXJnpoSaUavH5icFWqhubxBNJzB+e+HMg+Rt/9JgrKyulMXd1dcHv92PevHnSz8SamyJxY5/JZEIwGMSxY8cwNDSEGTNmTHqcq3mzkhM8xmtwIvfxlXrDSqUZQCbJDcCnO1BXchd2qs0ypvv1SPbaR9ey1eniN/YRu/Cl28DCYDDg0qVLk+Ya+Ph1SHUDqdwrKUodQ2pbcdVauoLWAke5tFrJQatfrLQgZ4PitrY2zJgxAxaLBc3Nzdi6dStqa2vR0tKC0dFRrFmzRrpvfX09amtrsW/fPlUFxdkoPh+JROK2Fo1+c4j3xi9nzO3t7SgpKcHAwMCUuckGgwHFxcW4fPmy5t6sAGXesKLn0el0SrnN4urMxNdDq9K5HKvUpsZsNMtIRM545NSyFc/dEydOJDyOot9DxPtEn/uDg4MoLS2F3+9XfANpPl/6zWSgng6tBo5yaLmSg9a+WGlFTgbFTU1NeP755+F2u+HxePD444/jhhtuwIkTJ9DZ2QmTyTTpAHe5XOjs7Ez4uOFwOCYNYXBwcDqGL8lW8fl4rUWVKpjv9/vhdDqlsmcTa97q9Xo4nU4EAgFNvlkB6b9hifNoNBqlLwfR1QXU/vzlSPdyrFKbGrPVLCOd8citZQsAPT09MJlMcY8j8T2ku7tb+mIs0uv1qKysxKxZs3D8+HFFN5B2d3dLFW8ikQiGh4dhs9l46VdltBw4JqP1Sg5a+WKlJTkZFN9+++3Sfy9ZsgRNTU2YPXs2du7cmVaL2a1bt07awDed1Fh8XqmC+WIdzmAwKLWNjf5grK2tVeRDOJvSecMKh8MIBoMIBoMYGxuLWU0PBALSPKn5+SeSyuXYeKvJSm1qVFuzjFTPoURfhsVzLNlxNDIyIuVGTwx8BEGQyhQqtYF0/vz5OHjwIDo7O6UNvWVlZbj++ut56VdltB44JsJKDjRRTgbFE5WUlGDBggU4c+YMbr31VoyMjMDn88UELF1dXVPmIEf7+7//ezz88MPS/x8cHMSsWbOma9jTUnx+ugMMuY8zsQ6n+HNxJ7/RaMTJkyfz9s3KZDIhEAhgZGQERUVFkyp0+P1+CIIg1ZDWklQux4ppIlOtJjudTkUuxafaLEMNzTvk1rLt7+9HIBDA6Ogo7HZ73OPo4sWLEAQhbuqUnBQLuWkPXq8XbW1tMJvNUr1lccNeW1sbysrKFA+Mc7VqQibkcuDIdB6aKC+C4kAggLNnz+L+++9HQ0MDjEYj9uzZg7Vr1wIAWltbcenSJTQ3Nyd8HLHzWqYoXXw+0eVqpQKMVMacqA6nIAh8s8LkVbtcIPdy7Llz53Dy5MmEq8lKbGpMpVlGurn7cih1DkVLVCFjfHwc/f39KC0tjZs6JaZYdHV1TZliIVanSTbX4hcisXFPKBSa1LhH6fzUXK2akCm5HDiykgNNlJNB8d/93d/hzjvvxOzZs3HlyhU89thjMBgM+NznPofi4mJ88YtfxMMPP4yysjI4HA787d/+LZqbm1W1yQ6YvkoG0xlgpDrmeIF8vr9ZjYyMwG63IxgMTrkZ0Ww2S5e9tUbu5djTp08nXU1evXp12qlDco81AFLuvtlslr68hcPhrDSCSHYOAfKOI3EVMNnrMTIyEvf5pZIrL5Y7jNe4p729XbH81FytmpBJuf5ezEoOFC0ng+L29nZ87nOfQ29vLyoqKrB69Wrs379fOrj/7d/+DXq9HmvXro1p3qFGSlcyUCrAkNPkQEuF99VGDHrtdrtUpSN6M6LD4ZDupzVyLsdGIhEMDg7K2timxC5suceamAvr8XhicmGXLl2alUYQyYjHkc1mw+Dg4JTHkfjFSk5JNrnVaeIZHh5Gf39/0sY9w8PDVzFrsXK5akKm5fp7MSs5kCgng+IdO3YkvN1iseDHP/4xfvzjH2doROlRqpKBnN3Dcv6WnMuRSr3J5OubVfQly1mzZk0ZhGj1kqWcy7FihQW5m3uU2IUtp/52W1sbTCYT5syZE7M5dDpyYZU49uUcR3JSQ0pKSqRqMOlUpxkZGZHduCdduVw1IRty/b2YlRwIyNGgOBfJOWHjrd5GX64WL/dGd79KJcCIvhwp5lgLgjDl5ch0xpzqc9eqeM8/+pJlT08PHA4HCgsLMTIygp6eHk1fspRzOdbtduPo0aOyN/cotZEq3rEWvepYWVkZ89gOh2PSqqNaNnbJPY4AJCzrqFQ1GJPJJKtxjxIbSHO5akK25PJ7MRHAoDhnJFq9FS9XDwwMSJdQJ9YqlbN7eOImmd7e3rQ2yeT7Bphkzz+XL1kme25OpxMdHR2yNvdk4jhKZdVxdHRUkfEo9bzkHkeJ7qNUNRiLxYLS0lL4fL64jXtKSkomrURfjVyumkBE04NBcQ5ItpmksbERVqsVra2tMBgMsFgs0geR3++Hz+eD2+1OeileyU0y+b4BRu7zz+VLlsmem5zNPT09PRk5juSuOnZ2duLMmTNpj0fp80POcZToPkpVgykuLkZNTQ3GxsbiNu6pqanJeBUPIiKAQbHmpdKiFpi8SzyV4EqpTTL5vgEm1eefy5csEz03OavJ77zzTkaOIzmrjuJmtHTHM13nh5zjaLqrwUQ/TrzGPdmq4kFExKBYI+LlKMq5rOvxeAAAs2fPjlvJIBQKZWyTTL5vgMn355+KRKuXPp8vY/MoZ9UxejNaOuNR6/ExXVVlJjbuUWMVDyLKDwyKNSBRbmEkEkm6iU4MUisqKlBcXDzpPoIgoLe3N2ObZPJ9A0y+P/9UxVu9zOQ8yll1THUzmpyNsdP9vFKlxaoyuZyCRETKYlCscslyCxcuXIiCggIMDg5Kq8ATN9FNbFE7cRNLOBzO6CaZfN8Ak+/PXymZnsdkq46pbEaTszFWrceHUuk8mUwLyuUUJCJSDoNiFZOTW9jR0QGLxYLTp09LH5RioBoIBODz+bBgwQLYbLa0W9Smukkm3kpYvm+AiX7+TqdzUu3YXH/+SsnGcaTEZrSRkREcPHgw4cbYfD4/skUtZfRIWWp8XdU4JvoYg2IVk5tbaDAYAEDaTCeK3lxXX1+fsA6p0ptkkpWTyucNMOI8dnZ24tSpU4hEItJter1emp9cff5KydZGqnQ2o7ndbrS2tib8otva2gq3252350c25Ht5yFylxtdVjWOiP2BQrGJycgt7e3uh0+lQW1s7qY1rUVGRtInOZDJlbJOM3HJS+b4BJl5Qw2BHPrUdR3JSLOR80V28eLGqnlcuy/fykLlKja+rGsdEsRgUq5ic3ELxsm1xcTFKSkoSbqJzuVzTvkkmlXJS+boBRpyjSCSC+vr6KVvv5nJJOqXJPY4ydcky0Xi6urpkb6JT6nyl+PK9PGSuUuPrqsYx0WQMilVMTs6k0+lEIBCQvYluujfJpFpOSskNMFrJ04qeI71eP+k1Y0m21CU7jjJ9yTLeeFLdRMcNYtNLreXvKD1qfF3VOCaajEGxisnJUbz++uvR2tqqmk052SonpaU8LTWX3MpFarpkme+bTNWG52JuUuPrqsYx0WT6bA+AEhNzFKurqxEKhdDb24tQKITq6mo0NTWhsrIS9fX1KCwshNfrxfDwsLTxzev1ZnxTTvRK2FSmo5yUGPR4PB4UFhaivLwchYWF8Hg8OHDgALxer2J/SwnZmKN8NfGSpdlshl6vly5ZDg0NxXR8nG7iF121nK/5judiblLj66rGMdFkXCnWgGQ5k2rabJTpcmNazNPiamHmTMcly3TTdMTz9eTJk/B4PBgZGYHJZEJ1dTUWLlyouisbuYznYm5S4+uqxjHRZAyKNSJZbqFaNq2JK2FdXV1TlhsTV7aVGpcW87SyVUosHyl9yVJLaTqUHM/F3KTG11WNY6LJGBTnEDVtyomukTzVz5Wi1TwtNa3u5zIlu8MplZsc/TglJSXS43R2dmJwcJBlmTKM52JuUuPrqsYxUSwGxXRVIpEI2tvbEQgEYLfbUVNTA71eL6UzCIIAt9uNQCAgXR622+3o7e1VNJ1hOlriqqF0Vy5QQzUQpS5ZKpWmo8V0n3yg1nNRDeeQlqnxdVXjmOgPGBRTylpbW3Hw4EH09fVhfHwcBoMBZWVlaGxshMvlQk9PD4xGI9rb2zE0NIRIJAK9Xo/CwkLF0xmUztNSS+kurVNLmoFSlyyVStPRYrpPvlDbuaiWc0jr1Pa6AuocE32MQTGlpLW1Fbt370Y4HIbNZpMu/Xq9XuzevRsrVqxAMBhEMBjE2NgYzGYzDAYDxsfHEQgEpHbQSqUzKJmnpabSXVqmtnlU4pKlUmk6Wk33ocxS2zlElC8YFJNskUgEBw8eRDgcRklJCfT6jyv6mc1mGI1G+Hw+nDhxAuFwGKOjoygqKpKC0YKCAhgMBvj9fgiCAJPJpNi4lAh6eFlbGWqdx3QvWSqVpjMd6T6UW9R6DhHlAwbFJFt7ezv6+vpgs9mkgFgkpkf4fD4UFBRk/M063aCHl7WVoeZ5TOeSpVJpOizLRMmo+RwiynUMikm2QCCA8fFxGI1GCIKA8fFxKV/YYDBIl34LCwuh0+kwNDQUkz4hBqs2mw0jIyOKjy+doIeXtZWh9XmMt7FJqTQdlmXKffE2Icul9XOISMsYFOeQ6d6pbLfbYTAYEAqFMDo6itHRUQiCAJ1OB6PRiIKCAhQUFMBms6GoqAgDAwMYGhpCOByGXq+H3W6Hw+EAANVdHuZlbWVoeR6TbWxSqpwSyzLlrkSbkN1ut6zH0PI5RKR1DIpzhNfrnfYOWTU1NbDb7ejq6oLBYIDRaIROp4MgCAiHwxgaGoLL5UJtbS26urowa9asSR3tenp6VHl5mJe1laHVeZS7sUmpckosy5R7km1CBiArMNbqOUSUCxgU5wCv14u9e/eiu7s7poNcb28vuru7ceONNyoSGOt0OpSVlaG7u1taBdHr9YhEIhgfH5duX7hwIfx+P3p6euBwOFBYWIiRkRH09PSo9vIwL2srQ4vzmOrGJqXKKbEsU+6Qswn54MGDmD9/ftJUCi2eQ0S5Qn6iE6mSIAhoaWlBR0cHIpEILBYLbDYbLBYLIpEIOjo60NLSokgnuYGBAQDAnDlzYLPZMDY2JuW/2Ww2zJkzB8DHOW9NTU2orq5GKBRCb28vQqEQqqurVV1KSLysrbVxq43W5jGVjU1EU5GzCbmvrw/t7e2yHk9r5xBRruBKscb5fD5cuHABOp0ONpstpgSazWaD3+/HhQsX4PP5UFpaKusx4+UmiwGwy+WCy+WC3++XUjWKiooAfLw6HQ6H4XK5NHl5mJe1laGleeTGJkpX9CbkqZhMJgwNDSEQCMh+TC2dQ0S5gkGxxokrCHa7fcpVLqvVimAwiN7eXllBcaLNRhM3gEzMaRseHo7ZAKLVy8NaHbfaaGUeubGJ0iVuQk50DBkMBtjt9pQeVyvnEFGuYPqExslJixAEQdb9xM1GHo8HhYWFKC8vR2FhITweDw4cOICRkRFp5WLi44kbQJxOJzeAkKaIG5t4XNPVqqmpQVlZGYLBYMy+DuDjfOOhoSGUlZWhpqYmSyMkIjkYFGuc0+mE1WrF8PDwlB/ooVAIVqsVTqcz4eNM3GxkNpuh1+ulzUZDQ0NobW2F2+1GYWEhvF4vhoeHEYlEMDw8DK/Xyw0gpEnixiYe13S19Ho9GhsbYTab4fP5Yo4hn88Hs9mMxsbGlOoVE1HmMX1C40pKSjBnzhy0trYiGAxK3eQEQcDY2BgEQcCcOXOSXoKTu9lo8eLFsmusTnfdZCJRusealmsH8zxTB7HcmlineGhoCAaDARUVFSnVKSai7GFQrHE6nQ4NDQ3o7e3FlStXpEBYp9OhoKAAM2bMQENDQ9IPyVQ2G8nZRJesEQKRUpQ61rS4sYnnmbq43W7Mnz8/rY52RJQ9DIpzhMlkQmFhIcbHx6WgWGy9LEeqm40SbQCR2wiBKF1KH2ta2tjE80yd9Ho9amtrsz0MIroK/PqqcWIusCAIWLhwIRYsWIB58+ZhwYIFWLhwYcztiSi12UhObrKc8RAlk8/HWj4/dyKi6cKgWOOic4H1ej0sFgvsdjssFgv0er3sxgNKbTZiIwTKlHw+1vL5uVPqBEGAz+dDV1cXfD4fvywRxcH0CY1TsvGAEpuN2AiBMiWfj7V8fu6UGuadE8nHoFjjlG48kO5mIzZCoFSkUzkhn4+1q3nurFKRf5h3TpQaBsUaJ+YCezweVFRUxHzIibnA1dXVKTUeSGez0XSMh3JTuitY+XyspfrcuVqYfybmnYvHiJh3Lh4TTqeTX46I/h+DYo0Tc4EHBgbg9XrhcDhgMpkwMjKCwcHBjDceUNt4JuJqmToosYKl9mNtOqXy3NW6WshzcXqlkneulYorRNONQXEOUFvjAbWNR8TVMnVQcgVLrcdaJsh57mpdLeS5OP2Yd06UOgbFOUJtjQfUNh61rpblI6VXsNR2rGVSsueuxtVCnouZkc8590RXi0FxDlFb4wG1jEetq2X5ajpWsNRyrGVDoueuttVCnouZk88590RXi3WKKeflQ01XLdUhjV7BmgpXsJSjtrnOh3NRLZSqPU+UT7hSTDlPbatlStNafiZXsDJHbXOd6+ei2uRzzj3R1WBQTDkvl3PrtJifqfWqEVqqmqC2uc7lc1Gt8jnnnihVDIop56lttUwpWs7P1OoKltZW5QF1zXWunotql88590SpYFBMOU9tq2VKUWNlgVRobQVLi6vyIrXMda6ei0SUGxgUU15Q02qZUnIhP1MrK1haXpUXqWWuc/FcJKLcwKCY8oZaVsuUwvzMzNH6qrza5Nq5SES5gUEx5RW1rJYpgfmZmZMLq/Jqk0vnIhHlBtYpJtIo1iHNHLXV+yUiIuUxKCbSMDE/s7q6GqFQCL29vQiFQqiurlb1xi+tEVflBwYGJjVGEVflnU4nV+WJiDSM6RNEGsf8zOnHqglERLmPQTFRDmB+5vRj1QQiotzGoJiISCauyhMR5S4GxUREKeCqPBFRbuJGOyIiIiLKewyKiYiIiCjvMSgmIiIiorzHoJiIiIiI8h6DYiIiIiLKewyKiYiIiCjvMSgmIiIiorzHoJiIiIiI8h6DYiIiIiLKewyKiYiIiCjvMSgmIiIiorzHoJiIiIiI8h6DYiIiIiLKewyKiYiIiCjvMSgmIiIiorzHoJiIiIiI8h6DYiIiIiLKewyKiYiIiCjvMSgmIiIiorzHoJiIiIiI8l5BtgegZYIgAAAGBwezPBIiIiIimooYp4lxWzwMitPg9/sBALNmzcrySIiIiIgoEb/fj+Li4ri364RkYTPFFYlEcOXKFRQVFUGn02V7OLINDg5i1qxZuHz5MhwOR7aHk9M415nDuc4cznXmcK4zh3OdGdmYZ0EQ4Pf7MWPGDOj18TOHuVKcBr1ej5qammwP46o5HA6e+BnCuc4cznXmcK4zh3OdOZzrzMj0PCdaIRZxox0RERER5T0GxURERESU9xgU5yGz2YzHHnsMZrM520PJeZzrzOFcZw7nOnM415nDuc4MNc8zN9oRERERUd7jSjERERER5T0GxURERESU9xgUExEREVHeY1BMRERERHmPQXGO2rp1K1asWIGioiJUVlbiz/7sz9Da2hpzn+HhYWzYsAHl5eWw2+1Yu3Yturq6sjRi7Xr66aexZMkSqRB5c3MzXn/9del2zvP0eeKJJ6DT6bB582bpZ5xvZXzrW9+CTqeL+VdfXy/dznlWVkdHBz7/+c+jvLwcVqsVixcvxuHDh6XbBUHAN7/5TVRXV8NqtWLNmjVoa2vL4oi1ac6cOZOOa51Ohw0bNgDgca2k8fFxfOMb30BdXR2sVivmzZuHb3/724iu76C245pBcY7au3cvNmzYgP379+Ott97C6OgoPvWpTyEYDEr3eeihh7Br1y68/PLL2Lt3L65cuYJ77rkni6PWppqaGjzxxBNoaWnB4cOHcfPNN+Ouu+7Chx9+CIDzPF0OHTqEZ555BkuWLIn5OedbOYsWLYLH45H+vfPOO9JtnGfl9Pf3Y9WqVTAajXj99dfx0Ucf4V/+5V9QWloq3ed73/sennzySfzkJz/BgQMHYLPZcNttt2F4eDiLI9eeQ4cOxRzTb731FgDgs5/9LAAe10r67ne/i6effho/+tGPcPLkSXz3u9/F9773Pfzwhz+U7qO641qgvNDIl7eZAAAN7ElEQVTd3S0AEPbu3SsIgiD4fD7BaDQKL7/8snSfkydPCgCEffv2ZWuYOaO0tFT4+c9/znmeJn6/X5g/f77w1ltvCTfeeKOwadMmQRB4XCvpscceE5YuXTrlbZxnZX39618XVq9eHff2SCQiVFVVCd///veln/l8PsFsNgv/+Z//mYkh5qxNmzYJ8+bNEyKRCI9rhd1xxx3Cgw8+GPOze+65R1i3bp0gCOo8rrlSnCcGBgYAAGVlZQCAlpYWjI6OYs2aNdJ96uvrUVtbi3379mVljLlgfHwcO3bsQDAYRHNzM+d5mmzYsAF33HFHzLwCPK6V1tbWhhkzZmDu3LlYt24dLl26BIDzrLTXXnsNy5cvx2c/+1lUVlZi2bJl+NnPfibdfv78eXR2dsbMd3FxMZqamjjfaRgZGcGLL76IBx98EDqdjse1wj7xiU9gz549OH36NADg6NGjeOedd3D77bcDUOdxXZCVv0oZFYlEsHnzZqxatQrXXXcdAKCzsxMmkwklJSUx93W5XOjs7MzCKLXt+PHjaG5uxvDwMOx2O1599VVce+21OHLkCOdZYTt27MD777+PQ4cOTbqNx7Vympqa8Pzzz8PtdsPj8eDxxx/HDTfcgBMnTnCeFXbu3Dk8/fTTePjhh/EP//APOHToEL761a/CZDJh/fr10py6XK6Y3+N8p+d//ud/4PP58MADDwDg+4fStmzZgsHBQdTX18NgMGB8fBzf+c53sG7dOgBQ5XHNoDgPbNiwASdOnIjJByRlud1uHDlyBAMDA/iv//ovrF+/Hnv37s32sHLO5cuXsWnTJrz11luwWCzZHk5OE1dzAGDJkiVoamrC7NmzsXPnTlit1iyOLPdEIhEsX74c//zP/wwAWLZsGU6cOIGf/OQnWL9+fZZHl7ueffZZ3H777ZgxY0a2h5KTdu7ciZdeegnbt2/HokWLcOTIEWzevBkzZsxQ7XHN9Ikct3HjRvz617/G22+/jZqaGunnVVVVGBkZgc/ni7l/V1cXqqqqMjxK7TOZTLjmmmvQ0NCArVu3YunSpdi2bRvnWWEtLS3o7u7GH/3RH6GgoAAFBQXYu3cvnnzySRQUFMDlcnG+p0lJSQkWLFiAM2fO8LhWWHV1Na699tqYny1cuFBKVxHndGIVBM731bt48SJ2796NL33pS9LPeFwr69FHH8WWLVtw3333YfHixbj//vvx0EMPYevWrQDUeVwzKM5RgiBg48aNePXVV/Hb3/4WdXV1Mbc3NDTAaDRiz5490s9aW1tx6dIlNDc3Z3q4OScSiSAcDnOeFXbLLbfg+PHjOHLkiPRv+fLlWLdunfTfnO/pEQgEcPbsWVRXV/O4VtiqVasmlcw8ffo0Zs+eDQCoq6tDVVVVzHwPDg7iwIEDnO+r9Nxzz6GyshJ33HGH9DMe18oaGhqCXh8bZhoMBkQiEQAqPa6zsr2Ppt1XvvIVobi4WPjd734neDwe6d/Q0JB0n7/+678Wamtrhd/+9rfC4cOHhebmZqG5uTmLo9amLVu2CHv37hXOnz8vHDt2TNiyZYug0+mEN998UxAEzvN0i64+IQicb6U88sgjwu9+9zvh/PnzwrvvviusWbNGcDqdQnd3tyAInGclHTx4UCgoKBC+853vCG1tbcJLL70kFBYWCi+++KJ0nyeeeEIoKSkRfvWrXwnHjh0T7rrrLqGurk4IhUJZHLk2jY+PC7W1tcLXv/71SbfxuFbO+vXrhZkzZwq//vWvhfPnzwuvvPKK4HQ6ha997WvSfdR2XDMozlEApvz33HPPSfcJhULC3/zN3wilpaVCYWGhcPfddwsejyd7g9aoBx98UJg9e7ZgMpmEiooK4ZZbbpECYkHgPE+3iUEx51sZ9957r1BdXS2YTCZh5syZwr333iucOXNGup3zrKxdu3YJ1113nWA2m4X6+nrhpz/9acztkUhE+MY3viG4XC7BbDYLt9xyi9Da2pql0Wrbb37zGwHAlPPH41o5g4ODwqZNm4Ta2lrBYrEIc+fOFf7xH/9RCIfD0n3UdlzrBCGqtQgRERERUR5iTjERERER5T0GxURERESU9xgUExEREVHeY1BMRERERHmPQTERERER5T0GxURERESU9xgUExEREVHeY1BMRERERHmPQTERkcbt3bsXOp1O+vfee+9le0hERJrDoJiISON+8YtfxPz/F154IUsjISLSLrZ5JiLSsFAoBJfLBb/fD7vdjkAggNLSUng8HpjN5mwPj4hIM7hSTESkYa+++ir8fj8A4MknnwQA9Pf3Y9euXdkcFhGR5jAoJiLSMDFVYsmSJfjCF74At9sd83MiIpKHQTERkUZ5PB7s3r0bAPD5z38+5n/feOMNeL3epI/R29uLr33ta3C73bBarXC5XLj11lvx6quvAgCef/55aQPfhQsX4j7O8PAwfvSjH+GWW25BVVUVTCYTKisrsWbNGjz77LMYGxtL89kSEU0v5hQTEWnUD37wAzz66KPQ6/W4dOkSZs6cifPnz2PevHkQBAHbtm3DV7/61bi/f/z4cdx6663o6uqa8vYvf/nLaG5uxhe+8AUAwPnz5zFnzpxJ9zt69CjuuusuXLx4Me7fWrFiBXbt2gWXy5XakyQiyhAGxUREGrV06VIcO3YMN998M/bs2SP9fPXq1Xj33XfR0NCAw4cPT/m7Pp8PixYtwpUrVwAA999/P/7iL/4CFRUVOHPmDLZt24Z9+/ahqakJBw4cADB1UHzmzBksX74cAwMDcDgc2LBhAxobGzFr1iz09vbitddewzPPPIOxsTE0NTXh97//PYxG4/RMCBFROgQiItKcDz74QAAgABD+4z/+I+a2p59+Wrrtww8/nPL3N2/eLN3n3//93yfdPjY2Jtx1113SfQAI58+fn3S/T3ziEwIAYdmyZYLX653yb73++uuCXq8XAAg//elPU3+yREQZwJxiIiINEjfSWa1WrF27Nua2P//zP4fJZIq5X7RwOIznn38ewMdpDZs2bZp0H4PBgGeeeQYWiyXuGH7/+99LjUJ+8YtfwOl0Tnm/T3/60/jMZz4DANLfJSJSGwbFREQaMzY2hu3btwMA7rzzTjgcjpjby8rK8Cd/8icAgJdeegmRSCTm9sOHD8Pn8wH4w8a8qbhcLtx2221xb3/ttdcAAG63G4sXL0445j/+4z8GABw6dIib7ohIlRgUExFpzG9+8xtpc1y8oFb8eXt7O95+++2Y206cOCH9d0NDQ8K/tXz58ri3ifnKra2tMW2mp/q3ceNGAMDo6Cj6+vqSPEMiosxjUExEpDFiSkR5eTk+/elPT3mfP/3TP0VJSUnM/UX9/f3Sf1dUVCT8W4lu7+7uljPcSYaGhq7q94iIplNBtgdARETyDQwMSGkLvb29Uu5wIq+88gqeeuop2Gw2RccyPj4O4OMqGC+++KLs35s5c6ai4yAiUgKDYiIiDdm5cyeGh4dT+p1AIIBXXnkF999/PwCgtLRUus3r9WLBggVxfzdRA5Dy8nLp8a+77rqUxkREpDYMiomINERMhaiursa//uu/Jr3/o48+ivb2drzwwgtSULxo0SLp9paWFqxatSru78ercwwAy5Ytw3vvvYdz586hs7MTVVVVcp8GEZHqsHkHEZFGRHer27hxI374wx8m/Z3Nmzdj27ZtMV3vhoeHUVVVhYGBAaxYsQIHDx6c8ne7urowZ84caWV6YvOON998U6pOsWXLFmzdujX9J0lElCXcaEdEpBEvvPACxHUMse5vMuL9IpGIlPdrsVjwl3/5lwA+LpG2bdu2Sb8XiUTwV3/1VwlTNT71qU+hsbERAPD9738fO3fuTDiW48ePY9euXbLGTUSUaVwpJiLSiGuuuQZnz55FZWUlPB4P9Prk6xqRSAQ1NTXweDxYtGiRVI6tr68PixYtQmdnJ4CP2zyvW7cups3ze++9h8bGRmkl+cKFC5g9e3bM4589exaNjY1SmbU777wT9957L+bPnw+DwYDu7m588MEH2LVrF/bv349HHnkEP/jBD5ScFiIiRTCnmIhIA959912cPXsWAHD33XfLCogBQK/X4+6778ZTTz2FDz/8EC0tLWhoaEBZWRneeOMN3HrrrfB6vfjlL3+JX/7ylzG/+8ADD+CGG26QguKputvNmzcP+/btw9q1a3HixAns2rUr4WrwxEYjRERqwfQJIiINiK41PLGtczLR949+nKVLl+Kjjz7CI488gvnz58NsNsPpdOKmm27C9u3b8dxzz2FwcFC6f3Fx8ZSPv2DBAhw5cgTbt2/H2rVrUVtbC6vVCpPJhOrqanzyk5/EP/3TP6GlpQXf/OY3Uxo7EVGmMH2CiIji+tKXvoRnn30WNTU1uHz5craHQ0Q0bbhSTEREUwqFQvjVr34FAFi5cmWWR0NENL0YFBMR5amzZ88i3sXC8fFxfOUrX0FPTw8AYP369ZkcGhFRxjF9gogoTz3wwAM4ePAg7rvvPjQ1NaGyshKhUAjHjh3Dz372M7z//vsAgDVr1uDNN9+ETqfL8oiJiKYPq08QEeWxkydP4rHHHot7+6pVq7Bjxw4GxESU87hSTESUp1pbW/Hf//3f2L17Ny5cuACv14vR0VGUl5dj+fLluPfee3HffffJLv9GRKRlDIqJiIiIKO/x6z8RERER5T0GxURERESU9xgUExEREVHeY1BMRERERHmPQTERERER5T0GxURERESU9xgUExEREVHeY1BMRERERHmPQTERERER5b3/A+bc+182HDlfAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, ax = subplots(figsize=(8,8))\n",
+        "ax.scatter(age, y, facecolor='gray', alpha=0.5)\n",
+        "for lam in np.logspace(-2, 6, 5):\n",
+        "    gam = LinearGAM(s_gam(0, lam=lam)).fit(X_age, y)\n",
+        "    ax.plot(age_grid,\n",
+        "            gam.predict(age_grid),\n",
+        "            label='{:.1e}'.format(lam),\n",
+        "            linewidth=3)\n",
+        "ax.set_xlabel('Age', fontsize=20)\n",
+        "ax.set_ylabel('Wage', fontsize=20);\n",
+        "ax.legend(title='$\\lambda$');\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f18b58db",
+      "metadata": {
+        "id": "f18b58db"
+      },
+      "source": [
+        "The `pygam` package can perform a search for an optimal smoothing parameter."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "id": "968e1e42",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:36.979615Z",
+          "iopub.status.busy": "2024-06-04T23:19:36.979395Z",
+          "iopub.status.idle": "2024-06-04T23:19:37.195601Z",
+          "shell.execute_reply": "2024-06-04T23:19:37.195005Z"
+        },
+        "id": "968e1e42",
+        "outputId": "60e46c5d-1bc9-4188-9f94-f44eaddc703c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 731
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[38;2;0;255;0m100%\u001b[39m \u001b[38;2;0;255;0m(11 of 11)\u001b[39m |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
+      "source": [
+        "gam_opt = gam.gridsearch(X_age, y)\n",
+        "ax.plot(age_grid,\n",
+        "        gam_opt.predict(age_grid),\n",
+        "        label='Grid search',\n",
+        "        linewidth=4)\n",
+        "ax.legend()\n",
+        "fig\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bba7cf02",
+      "metadata": {
+        "id": "bba7cf02"
+      },
+      "source": [
+        "Alternatively, we can fix the degrees of freedom of the smoothing\n",
+        "spline using a function included in the `ISLP.pygam` package. Below we\n",
+        "find a value of $\\lambda$ that gives us roughly four degrees of\n",
+        "freedom. We note here that these degrees of freedom include the\n",
+        "unpenalized intercept and linear term of the smoothing spline, hence there are at least two\n",
+        "degrees of freedom."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "id": "891e3b75",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:37.198482Z",
+          "iopub.status.busy": "2024-06-04T23:19:37.198260Z",
+          "iopub.status.idle": "2024-06-04T23:19:37.214885Z",
+          "shell.execute_reply": "2024-06-04T23:19:37.214249Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "891e3b75",
+        "outputId": "6c4a815a-691c-4857-8469-e86c52ce1429",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(4.000000100000307)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ],
+      "source": [
+        "age_term = gam.terms[0]\n",
+        "lam_4 = approx_lam(X_age, age_term, 4)\n",
+        "age_term.lam = lam_4\n",
+        "degrees_of_freedom(X_age, age_term)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7688d014",
+      "metadata": {
+        "id": "7688d014"
+      },
+      "source": [
+        "Let’s vary the degrees of freedom in a similar plot to above. We choose the degrees of freedom\n",
+        "as the desired degrees of freedom plus one to account for the fact that these smoothing\n",
+        "splines always have an intercept term. Hence, a value of one for `df` is just a linear fit."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "54715029",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:37.218081Z",
+          "iopub.status.busy": "2024-06-04T23:19:37.217683Z",
+          "iopub.status.idle": "2024-06-04T23:19:37.501731Z",
+          "shell.execute_reply": "2024-06-04T23:19:37.500504Z"
+        },
+        "id": "54715029",
+        "outputId": "162035d3-8c0f-4b7b-90a7-f36fb3cca13e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 710
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, ax = subplots(figsize=(8,8))\n",
+        "ax.scatter(X_age,\n",
+        "           y,\n",
+        "           facecolor='gray',\n",
+        "           alpha=0.3)\n",
+        "for df in [1,3,4,8,15]:\n",
+        "    lam = approx_lam(X_age, age_term, df+1)\n",
+        "    age_term.lam = lam\n",
+        "    gam.fit(X_age, y)\n",
+        "    ax.plot(age_grid,\n",
+        "            gam.predict(age_grid),\n",
+        "            label='{:d}'.format(df),\n",
+        "            linewidth=4)\n",
+        "ax.set_xlabel('Age', fontsize=20)\n",
+        "ax.set_ylabel('Wage', fontsize=20);\n",
+        "ax.legend(title='Degrees of freedom');\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1cec1eac",
+      "metadata": {
+        "id": "1cec1eac"
+      },
+      "source": [
+        "### Additive Models with Several Terms\n",
+        "The strength of generalized additive models lies in their ability to fit multivariate regression models with more flexibility than linear models. We demonstrate two approaches: the first in a more manual fashion using natural splines and piecewise constant functions, and the second  using the `pygam` package and smoothing splines.\n",
+        "\n",
+        "We now fit a GAM by hand to predict\n",
+        "`wage` using natural spline functions of `year` and `age`,\n",
+        "treating `education` as a qualitative predictor, as in (\\ref{Ch7:nsmod}).\n",
+        "Since this is just a big linear regression model\n",
+        "using an appropriate choice of basis functions, we can simply do this\n",
+        "using the `sm.OLS()`  function.\n",
+        "\n",
+        "We will build the model matrix in a more manual fashion here, since we wish\n",
+        "to access the pieces separately when constructing partial dependence plots."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "id": "b3cd8b66",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:37.505073Z",
+          "iopub.status.busy": "2024-06-04T23:19:37.504839Z",
+          "iopub.status.idle": "2024-06-04T23:19:37.519804Z",
+          "shell.execute_reply": "2024-06-04T23:19:37.519297Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "b3cd8b66"
+      },
+      "outputs": [],
+      "source": [
+        "ns_age = NaturalSpline(df=4).fit(age)\n",
+        "ns_year = NaturalSpline(df=5).fit(Wage['year'])\n",
+        "Xs = [ns_age.transform(age),\n",
+        "      ns_year.transform(Wage['year']),\n",
+        "      pd.get_dummies(Wage['education']).values]\n",
+        "X_bh = np.hstack(Xs)\n",
+        "gam_bh = sm.OLS(y, X_bh).fit()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7a1668c8",
+      "metadata": {
+        "id": "7a1668c8"
+      },
+      "source": [
+        "Here the function `NaturalSpline()` is the workhorse supporting\n",
+        "the `ns()` helper function.  We chose to use all columns of the\n",
+        "indicator matrix for the categorical variable `education`, making an intercept redundant.\n",
+        "Finally, we stacked the three component matrices horizontally to form the model matrix `X_bh`.\n",
+        "\n",
+        "We now show how to construct partial dependence plots for each of the terms in our rudimentary GAM. We can do this by hand,\n",
+        "given grids for `age` and `year`.\n",
+        " We simply predict with new $X$ matrices, fixing all but one of the features at a time."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "id": "ea8d6bc5",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:37.523112Z",
+          "iopub.status.busy": "2024-06-04T23:19:37.522834Z",
+          "iopub.status.idle": "2024-06-04T23:19:37.644337Z",
+          "shell.execute_reply": "2024-06-04T23:19:37.643251Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "ea8d6bc5",
+        "outputId": "fcd1922d-da8d-4e7d-8554-bf22241be9c5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 726
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "age_grid = np.linspace(age.min(),\n",
+        "                       age.max(),\n",
+        "                       100)\n",
+        "X_age_bh = X_bh.copy()[:100]\n",
+        "X_age_bh[:] = X_bh[:].mean(0)[None,:]\n",
+        "X_age_bh[:,:4] = ns_age.transform(age_grid)\n",
+        "preds = gam_bh.get_prediction(X_age_bh)\n",
+        "bounds_age = preds.conf_int(alpha=0.05)\n",
+        "partial_age = preds.predicted_mean\n",
+        "center = partial_age.mean()\n",
+        "partial_age -= center\n",
+        "bounds_age -= center\n",
+        "fig, ax = subplots(figsize=(8,8))\n",
+        "ax.plot(age_grid, partial_age, 'b', linewidth=3)\n",
+        "ax.plot(age_grid, bounds_age[:,0], 'r--', linewidth=3)\n",
+        "ax.plot(age_grid, bounds_age[:,1], 'r--', linewidth=3)\n",
+        "ax.set_xlabel('Age')\n",
+        "ax.set_ylabel('Effect on wage')\n",
+        "ax.set_title('Partial dependence of age on wage', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5ae3a3bb",
+      "metadata": {
+        "id": "5ae3a3bb"
+      },
+      "source": [
+        "Let's explain in some detail what we did above. The idea is to create a new prediction matrix, where all but the columns belonging to `age` are constant (and set to  their training-data means). The four columns for `age` are filled in with the natural spline basis evaluated at the 100 values in `age_grid`.\n",
+        "\n",
+        "* We made a grid of length 100 in `age`, and created a matrix `X_age_bh` with 100 rows and the same number of columns as `X_bh`.\n",
+        "* We replaced every row of this matrix with the column means of the original.\n",
+        "* We then replace just the first four columns representing `age` with the natural spline basis computed at the values in `age_grid`.\n",
+        "\n",
+        "The remaining steps should by now be familiar.\n",
+        "\n",
+        "We also look at the effect of `year` on `wage`; the process is the same."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "812a47a1",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:37.648530Z",
+          "iopub.status.busy": "2024-06-04T23:19:37.647173Z",
+          "iopub.status.idle": "2024-06-04T23:19:37.756140Z",
+          "shell.execute_reply": "2024-06-04T23:19:37.755581Z"
+        },
+        "id": "812a47a1",
+        "outputId": "86891dd8-8e52-4764-91ab-e49fd70c3711",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 726
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "year_grid = np.linspace(2003, 2009, 100)\n",
+        "year_grid = np.linspace(Wage['year'].min(),\n",
+        "                        Wage['year'].max(),\n",
+        "                        100)\n",
+        "X_year_bh = X_bh.copy()[:100]\n",
+        "X_year_bh[:] = X_bh[:].mean(0)[None,:]\n",
+        "X_year_bh[:,4:9] = ns_year.transform(year_grid)\n",
+        "preds = gam_bh.get_prediction(X_year_bh)\n",
+        "bounds_year = preds.conf_int(alpha=0.05)\n",
+        "partial_year = preds.predicted_mean\n",
+        "center = partial_year.mean()\n",
+        "partial_year -= center\n",
+        "bounds_year -= center\n",
+        "fig, ax = subplots(figsize=(8,8))\n",
+        "ax.plot(year_grid, partial_year, 'b', linewidth=3)\n",
+        "ax.plot(year_grid, bounds_year[:,0], 'r--', linewidth=3)\n",
+        "ax.plot(year_grid, bounds_year[:,1], 'r--', linewidth=3)\n",
+        "ax.set_xlabel('Year')\n",
+        "ax.set_ylabel('Effect on wage')\n",
+        "ax.set_title('Partial dependence of year on wage', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1cd84b43",
+      "metadata": {
+        "id": "1cd84b43"
+      },
+      "source": [
+        "We now fit the model (\\ref{Ch7:nsmod})  using smoothing splines rather\n",
+        "than natural splines.  All of the\n",
+        "terms in  (\\ref{Ch7:nsmod})  are fit simultaneously, taking each other\n",
+        "into account to explain the response. The `pygam` package only works with matrices, so we must convert\n",
+        "the categorical series `education` to its array representation, which can be found\n",
+        "with the `cat.codes` attribute of `education`. As `year` only has 7 unique values, we\n",
+        "use only seven basis functions for it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "id": "88ff3def",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:37.759390Z",
+          "iopub.status.busy": "2024-06-04T23:19:37.759130Z",
+          "iopub.status.idle": "2024-06-04T23:19:37.788899Z",
+          "shell.execute_reply": "2024-06-04T23:19:37.788370Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "88ff3def"
+      },
+      "outputs": [],
+      "source": [
+        "gam_full = LinearGAM(s_gam(0) +\n",
+        "                     s_gam(1, n_splines=7) +\n",
+        "                     f_gam(2, lam=0))\n",
+        "Xgam = np.column_stack([age,\n",
+        "                        Wage['year'],\n",
+        "                        Wage['education'].cat.codes])\n",
+        "gam_full = gam_full.fit(Xgam, y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "eedda235",
+      "metadata": {
+        "id": "eedda235"
+      },
+      "source": [
+        "The two `s_gam()` terms result in smoothing spline fits, and use a default value for $\\lambda$  (`lam=0.6`), which is somewhat arbitrary. For the categorical term `education`, specified using a `f_gam()` term,  we specify `lam=0` to avoid any shrinkage.\n",
+        "We produce the partial dependence plot in `age` to see the effect of these choices.\n",
+        "\n",
+        "The values for the plot\n",
+        "are generated by the `pygam` package. We provide a `plot_gam()`\n",
+        "function for partial-dependence plots in `ISLP.pygam`, which makes this job easier than in our last example with natural splines."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "id": "29dbc933",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:37.792129Z",
+          "iopub.status.busy": "2024-06-04T23:19:37.791875Z",
+          "iopub.status.idle": "2024-06-04T23:19:37.909465Z",
+          "shell.execute_reply": "2024-06-04T23:19:37.908764Z"
+        },
+        "id": "29dbc933",
+        "outputId": "1ecf3c5f-994a-4d84-9734-c6cfbc3a93ff",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 726
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, ax = subplots(figsize=(8,8))\n",
+        "plot_gam(gam_full, 0, ax=ax)\n",
+        "ax.set_xlabel('Age')\n",
+        "ax.set_ylabel('Effect on wage')\n",
+        "ax.set_title('Partial dependence of age on wage - default lam=0.6', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "bcaa9a68",
+      "metadata": {
+        "id": "bcaa9a68"
+      },
+      "source": [
+        "We see that the function is somewhat wiggly. It is more natural to specify the `df` than a value for `lam`.\n",
+        "We refit a GAM using four degrees of freedom each for\n",
+        "`age` and  `year`. Recall that the addition of one below takes into account the intercept\n",
+        "of the smoothing spline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "id": "cdb535b5",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:37.912957Z",
+          "iopub.status.busy": "2024-06-04T23:19:37.912683Z",
+          "iopub.status.idle": "2024-06-04T23:19:37.946458Z",
+          "shell.execute_reply": "2024-06-04T23:19:37.945953Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "cdb535b5"
+      },
+      "outputs": [],
+      "source": [
+        "age_term = gam_full.terms[0]\n",
+        "age_term.lam = approx_lam(Xgam, age_term, df=4+1)\n",
+        "year_term = gam_full.terms[1]\n",
+        "year_term.lam = approx_lam(Xgam, year_term, df=4+1)\n",
+        "gam_full = gam_full.fit(Xgam, y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a6a22de8",
+      "metadata": {
+        "id": "a6a22de8"
+      },
+      "source": [
+        "Note that updating `age_term.lam` above updates it in `gam_full.terms[0]` as well! Likewise for `year_term.lam`.\n",
+        "\n",
+        "Repeating the plot for `age`, we see that it is much smoother.\n",
+        "We also produce the plot for `year`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "id": "2acadc27",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:37.950913Z",
+          "iopub.status.busy": "2024-06-04T23:19:37.950529Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.082439Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.081725Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "2acadc27",
+        "outputId": "1d794d50-7b19-4253-f40e-c62b14e7df1e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 747
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Partial dependence of year on wage')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, ax = subplots(figsize=(8,8))\n",
+        "plot_gam(gam_full,\n",
+        "         1,\n",
+        "         ax=ax)\n",
+        "ax.set_xlabel('Year')\n",
+        "ax.set_ylabel('Effect on wage')\n",
+        "ax.set_title('Partial dependence of year on wage', fontsize=20)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "16258b84",
+      "metadata": {
+        "id": "16258b84"
+      },
+      "source": [
+        "Finally we plot `education`, which is categorical. The partial dependence plot is different, and more suitable for the set of fitted constants for each level of this variable."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "id": "a2b8e481",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.085348Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.085138Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.186025Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.185091Z"
+        },
+        "id": "a2b8e481",
+        "outputId": "4d753ac0-5fd2-43d8-d667-19eaaeeba9f6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 722
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, ax = subplots(figsize=(8, 8))\n",
+        "ax = plot_gam(gam_full, 2)\n",
+        "ax.set_xlabel('Education')\n",
+        "ax.set_ylabel('Effect on wage')\n",
+        "ax.set_title('Partial dependence of wage on education',\n",
+        "             fontsize=20);\n",
+        "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3a21bb76",
+      "metadata": {
+        "id": "3a21bb76"
+      },
+      "source": [
+        "### ANOVA Tests for Additive Models\n",
+        "In all of our models, the function of `year` looks rather linear. We can\n",
+        "perform a series of ANOVA tests in order to determine which of these\n",
+        "three models is best: a GAM that excludes `year` ($\\mathcal{M}_1$),\n",
+        "a GAM that uses a linear function of `year` ($\\mathcal{M}_2$), or a\n",
+        "GAM that uses a spline function of `year` ($\\mathcal{M}_3$)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "id": "1a76b516",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.189290Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.188978Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.226707Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.226124Z"
+        },
+        "id": "1a76b516",
+        "outputId": "88fdccbf-fda0-46e5-d2d6-d3ae36d191fc",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearGAM(callbacks=[Deviance(), Diffs()], fit_intercept=True, \n",
+              "   max_iter=100, scale=None, terms=s(0) + l(1) + f(2) + intercept, \n",
+              "   tol=0.0001, verbose=False)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ],
+      "source": [
+        "gam_0 = LinearGAM(age_term + f_gam(2, lam=0))\n",
+        "gam_0.fit(Xgam, y)\n",
+        "gam_linear = LinearGAM(age_term +\n",
+        "                       l_gam(1, lam=0) +\n",
+        "                       f_gam(2, lam=0))\n",
+        "gam_linear.fit(Xgam, y)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e0de32e6",
+      "metadata": {
+        "id": "e0de32e6"
+      },
+      "source": [
+        "Notice our use of `age_term` in the expressions above. We do this because\n",
+        "earlier we set the value for `lam` in this term to achieve four degrees of freedom.\n",
+        "\n",
+        "To directly assess the effect of `year` we run an ANOVA on the\n",
+        "three models fit above."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "id": "7a68bf04",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.229152Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.228941Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.236919Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.236171Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "7a68bf04",
+        "outputId": "d3cc4126-4018-45b3-f5e9-cf48563ca2b5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       deviance           df  deviance_diff   df_diff          F    pvalue\n",
+              "0  3.714362e+06  2991.004005            NaN       NaN        NaN       NaN\n",
+              "1  3.696746e+06  2990.005190   17616.542840  0.998815  14.265131  0.002314\n",
+              "2  3.693143e+06  2987.007254    3602.893655  2.997936   0.972007  0.435579"
+            ],
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+              "summary": "{\n  \"name\": \"anova_gam(gam_0, gam_linear, gam_full)\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"deviance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11354.792743250995,\n        \"min\": 3693142.929660017,\n        \"max\": 3714362.3661545385,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3714362.3661545385,\n          3696745.82331494,\n          3693142.929660017\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"df\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0800350044283564,\n        \"min\": 2987.0072542227567,\n        \"max\": 2991.0040054967076,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2991.0040054967076,\n          2990.005190412961,\n          2987.0072542227567\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deviance_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9909.146367653324,\n        \"min\": 3602.8936549229547,\n        \"max\": 17616.542839598376,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3602.8936549229547,\n          17616.542839598376\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"df_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4135920907892883,\n        \"min\": 0.9988150837466492,\n        \"max\": 2.9979361902042,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.9979361902042,\n          0.9988150837466492\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.399658148060597,\n        \"min\": 0.9720066141568753,\n        \"max\": 14.26513064881494,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.9720066141568753,\n          14.26513064881494\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pvalue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3063649435115579,\n        \"min\": 0.002313761023881404,\n        \"max\": 0.43557921917359377,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.43557921917359377,\n          0.002313761023881404\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 43
+        }
+      ],
+      "source": [
+        "anova_gam(gam_0, gam_linear, gam_full)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ed03248f",
+      "metadata": {
+        "id": "ed03248f"
+      },
+      "source": [
+        " We find that there is compelling evidence that a GAM with a linear\n",
+        "function in `year` is better than a GAM that does not include\n",
+        "`year` at all ($p$-value= 0.002). However, there is no\n",
+        "evidence that a non-linear function of `year` is needed\n",
+        "($p$-value=0.435).  In other words, based on the results of this\n",
+        "ANOVA, $\\mathcal{M}_2$ is preferred.\n",
+        "\n",
+        "We can repeat the same process for `age` as well. We see there is very clear evidence that\n",
+        "a non-linear term is required for `age`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a05df53b",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.239488Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.239277Z",
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+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3.850247e+06</td>\n",
+              "      <td>2990.000704</td>\n",
+              "      <td>125196.137317</td>\n",
+              "      <td>0.999884</td>\n",
+              "      <td>101.270106</td>\n",
+              "      <td>1.681120e-07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3.693143e+06</td>\n",
+              "      <td>2987.007254</td>\n",
+              "      <td>157103.978302</td>\n",
+              "      <td>2.993450</td>\n",
+              "      <td>42.447812</td>\n",
+              "      <td>5.669414e-07</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       deviance           df  deviance_diff   df_diff           F  \\\n",
+              "0  3.975443e+06  2991.000589            NaN       NaN         NaN   \n",
+              "1  3.850247e+06  2990.000704  125196.137317  0.999884  101.270106   \n",
+              "2  3.693143e+06  2987.007254  157103.978302  2.993450   42.447812   \n",
+              "\n",
+              "         pvalue  \n",
+              "0           NaN  \n",
+              "1  1.681120e-07  \n",
+              "2  5.669414e-07  "
+            ]
+          },
+          "execution_count": 38,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gam_0 = LinearGAM(year_term +\n",
+        "                  f_gam(2, lam=0))\n",
+        "gam_linear = LinearGAM(l_gam(0, lam=0) +\n",
+        "                       year_term +\n",
+        "                       f_gam(2, lam=0))\n",
+        "gam_0.fit(Xgam, y)\n",
+        "gam_linear.fit(Xgam, y)\n",
+        "anova_gam(gam_0, gam_linear, gam_full)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0bf66bc5",
+      "metadata": {
+        "id": "0bf66bc5"
+      },
+      "source": [
+        "There is a (verbose) `summary()` method for the GAM fit."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ee9db82c",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.267342Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.267144Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.271357Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.270406Z"
+        },
+        "id": "ee9db82c",
+        "outputId": "5a4c78bc-48f7-4d42-c733-680a034d21d2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "LinearGAM                                                                                                 \n",
+            "=============================================== ==========================================================\n",
+            "Distribution:                        NormalDist Effective DoF:                                     12.9927\n",
+            "Link Function:                     IdentityLink Log Likelihood:                                 -24117.907\n",
+            "Number of Samples:                         3000 AIC:                                            48263.7995\n",
+            "                                                AICc:                                             48263.94\n",
+            "                                                GCV:                                             1246.1129\n",
+            "                                                Scale:                                           1236.4024\n",
+            "                                                Pseudo R-Squared:                                   0.2928\n",
+            "==========================================================================================================\n",
+            "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
+            "================================= ==================== ============ ============ ============ ============\n",
+            "s(0)                              [465.0491]           20           5.1          1.11e-16     ***         \n",
+            "s(1)                              [2.1564]             7            4.0          8.10e-03     **          \n",
+            "f(2)                              [0]                  5            4.0          1.11e-16     ***         \n",
+            "intercept                                              1            0.0          1.11e-16     ***         \n",
+            "==========================================================================================================\n",
+            "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+            "\n",
+            "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
+            "         which can cause p-values to appear significant when they are not.\n",
+            "\n",
+            "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
+            "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
+            "         are typically lower than they should be, meaning that the tests reject the null too readily.\n"
+          ]
+        }
+      ],
+      "source": [
+        "gam_full.summary()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1fbf126f",
+      "metadata": {
+        "id": "1fbf126f"
+      },
+      "source": [
+        "We can make predictions from `gam` objects, just like from\n",
+        "`lm` objects, using the `predict()`  method for the class\n",
+        "`gam`.  Here we make predictions on the training set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cf0a024d",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.274135Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.273728Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.283548Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.283095Z"
+        },
+        "id": "cf0a024d"
+      },
+      "outputs": [],
+      "source": [
+        "Yhat = gam_full.predict(Xgam)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "70afdc16",
+      "metadata": {
+        "id": "70afdc16"
+      },
+      "source": [
+        "In order to fit a logistic regression GAM, we use `LogisticGAM()`\n",
+        "from `pygam`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0fe9f031",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.286193Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.285994Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.372533Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.372014Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "0fe9f031",
+        "outputId": "52d2b594-7ef4-4fbe-9688-49c236f97efd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "LogisticGAM(callbacks=[Deviance(), Diffs(), Accuracy()], \n",
+              "   fit_intercept=True, max_iter=100, \n",
+              "   terms=s(0) + l(1) + f(2) + intercept, tol=0.0001, verbose=False)"
+            ]
+          },
+          "execution_count": 41,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gam_logit = LogisticGAM(age_term +\n",
+        "                        l_gam(1, lam=0) +\n",
+        "                        f_gam(2, lam=0))\n",
+        "gam_logit.fit(Xgam, high_earn)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bdc73505",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.375490Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.375048Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.476110Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.475521Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "bdc73505",
+        "outputId": "09c0c584-4243-41ea-c5cf-cdba35a651cf"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, ax = subplots(figsize=(8, 8))\n",
+        "ax = plot_gam(gam_logit, 2)\n",
+        "ax.set_xlabel('Education')\n",
+        "ax.set_ylabel('Effect on wage')\n",
+        "ax.set_title('Partial dependence of wage on education',\n",
+        "             fontsize=20);\n",
+        "ax.set_xticklabels(Wage['education'].cat.categories, fontsize=8);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9aed5a52",
+      "metadata": {
+        "id": "9aed5a52"
+      },
+      "source": [
+        "The model seems to be very flat, with especially high error bars for the first category.\n",
+        "Let's look at the data a bit more closely."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "44f57a30",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.478694Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.478494Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.491764Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.491357Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "44f57a30",
+        "outputId": "73a3bb00-0739-4197-e97e-5ded0e8d0771"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>education</th>\n",
+              "      <th>1. &lt; HS Grad</th>\n",
+              "      <th>2. HS Grad</th>\n",
+              "      <th>3. Some College</th>\n",
+              "      <th>4. College Grad</th>\n",
+              "      <th>5. Advanced Degree</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>high_earn</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>False</th>\n",
+              "      <td>268</td>\n",
+              "      <td>966</td>\n",
+              "      <td>643</td>\n",
+              "      <td>663</td>\n",
+              "      <td>381</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>True</th>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>7</td>\n",
+              "      <td>22</td>\n",
+              "      <td>45</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "education  1. < HS Grad  2. HS Grad  3. Some College  4. College Grad  \\\n",
+              "high_earn                                                               \n",
+              "False               268         966              643              663   \n",
+              "True                  0           5                7               22   \n",
+              "\n",
+              "education  5. Advanced Degree  \n",
+              "high_earn                      \n",
+              "False                     381  \n",
+              "True                       45  "
+            ]
+          },
+          "execution_count": 43,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pd.crosstab(Wage['high_earn'], Wage['education'])\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1828779e",
+      "metadata": {
+        "id": "1828779e"
+      },
+      "source": [
+        "We see that there are no high earners in the first category of\n",
+        "education, meaning that the model will have a hard time fitting.  We\n",
+        "will fit a logistic regression GAM excluding all observations falling into this\n",
+        "category. This provides more sensible results.\n",
+        "\n",
+        "To do so,\n",
+        "we could subset the model matrix, though this will not remove the\n",
+        "column from `Xgam`. While we can deduce which column corresponds\n",
+        "to this feature, for reproducibility’s sake we reform the model matrix\n",
+        "on this smaller subset.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3b061ffd",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.494311Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.494116Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.498822Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.498423Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "3b061ffd"
+      },
+      "outputs": [],
+      "source": [
+        "only_hs = Wage['education'] == '1. < HS Grad'\n",
+        "Wage_ = Wage.loc[~only_hs]\n",
+        "Xgam_ = np.column_stack([Wage_['age'],\n",
+        "                         Wage_['year'],\n",
+        "                         Wage_['education'].cat.codes-1])\n",
+        "high_earn_ = Wage_['high_earn']\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6e3b8e93",
+      "metadata": {
+        "id": "6e3b8e93"
+      },
+      "source": [
+        "In the second-to-last line above, we subtract one  from the codes of the category, due to a bug in `pygam`. It just relabels\n",
+        "the education values and hence has no effect on the fit.\n",
+        "\n",
+        "We now fit the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "220e26ac",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.501236Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.501049Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.540965Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.540424Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "220e26ac",
+        "outputId": "80f1aa1b-edc4-4bbb-a143-4c2aaf8d2823"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "LogisticGAM(callbacks=[Deviance(), Diffs(), Accuracy()], \n",
+              "   fit_intercept=True, max_iter=100, \n",
+              "   terms=s(0) + s(1) + f(2) + intercept, tol=0.0001, verbose=False)"
+            ]
+          },
+          "execution_count": 45,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gam_logit_ = LogisticGAM(age_term +\n",
+        "                         year_term +\n",
+        "                         f_gam(2, lam=0))\n",
+        "gam_logit_.fit(Xgam_, high_earn_)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9838bbdc",
+      "metadata": {
+        "id": "9838bbdc"
+      },
+      "source": [
+        "Let’s look at the effect of `education`, `year` and `age` on high earner status now that we’ve\n",
+        "removed those observations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "94209d95",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.543510Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.543313Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.635421Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.634852Z"
+        },
+        "id": "94209d95",
+        "outputId": "4e403bf8-061e-4197-8882-19ef462507a0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, ax = subplots(figsize=(8, 8))\n",
+        "ax = plot_gam(gam_logit_, 2)\n",
+        "ax.set_xlabel('Education')\n",
+        "ax.set_ylabel('Effect on wage')\n",
+        "ax.set_title('Partial dependence of high earner status on education', fontsize=20);\n",
+        "ax.set_xticklabels(Wage['education'].cat.categories[1:],\n",
+        "                   fontsize=8);\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b86a257f",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.637945Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.637732Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.738536Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.737576Z"
+        },
+        "id": "b86a257f",
+        "outputId": "f337d205-ddd9-4efb-a26d-8b88cf6c2ef2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, ax = subplots(figsize=(8, 8))\n",
+        "ax = plot_gam(gam_logit_, 1)\n",
+        "ax.set_xlabel('Year')\n",
+        "ax.set_ylabel('Effect on wage')\n",
+        "ax.set_title('Partial dependence of high earner status on year',\n",
+        "             fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "075082df",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.741522Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.741306Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.853209Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.852962Z"
+        },
+        "lines_to_next_cell": 2,
+        "id": "075082df",
+        "outputId": "b5faa705-7528-490b-a568-04d61f2cdc8b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, ax = subplots(figsize=(8, 8))\n",
+        "ax = plot_gam(gam_logit_, 0)\n",
+        "ax.set_xlabel('Age')\n",
+        "ax.set_ylabel('Effect on wage')\n",
+        "ax.set_title('Partial dependence of high earner status on age', fontsize=20);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "94f2d878",
+      "metadata": {
+        "id": "94f2d878"
+      },
+      "source": [
+        "## Local Regression\n",
+        "We illustrate the use of local regression using  the `lowess()`\n",
+        "function from `sm.nonparametric`. Some implementations of\n",
+        "GAMs allow terms to be local regression operators; this is not the case in `pygam`.\n",
+        "\n",
+        "Here we fit local linear regression models using spans of 0.2\n",
+        "and 0.5; that is, each neighborhood consists of 20% or 50% of\n",
+        "the observations. As expected, using a span of 0.5 is smoother than 0.2."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ecef97c8",
+      "metadata": {
+        "execution": {
+          "iopub.execute_input": "2024-06-04T23:19:38.854567Z",
+          "iopub.status.busy": "2024-06-04T23:19:38.854494Z",
+          "iopub.status.idle": "2024-06-04T23:19:38.953710Z",
+          "shell.execute_reply": "2024-06-04T23:19:38.953441Z"
+        },
+        "lines_to_next_cell": 0,
+        "id": "ecef97c8",
+        "outputId": "efc14d54-21d5-434b-d5f4-d1181b0770ec"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "lowess = sm.nonparametric.lowess\n",
+        "fig, ax = subplots(figsize=(8,8))\n",
+        "ax.scatter(age, y, facecolor='gray', alpha=0.5)\n",
+        "for span in [0.2, 0.5]:\n",
+        "    fitted = lowess(y,\n",
+        "                    age,\n",
+        "                    frac=span,\n",
+        "                    xvals=age_grid)\n",
+        "    ax.plot(age_grid,\n",
+        "            fitted,\n",
+        "            label='{:.1f}'.format(span),\n",
+        "            linewidth=4)\n",
+        "ax.set_xlabel('Age', fontsize=20)\n",
+        "ax.set_ylabel('Wage', fontsize=20);\n",
+        "ax.legend(title='span', fontsize=15);\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "163ef0c0",
+      "metadata": {
+        "id": "163ef0c0"
+      },
+      "source": [
+        "  \n",
+        "\n"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax = plot_gam(gam_logit_, 1)\n",
-    "ax.set_xlabel('Year')\n",
-    "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of high earner status on year',\n",
-    "             fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "075082df",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.741522Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.741306Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.853209Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.852962Z"
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
-   ],
-   "source": [
-    "fig, ax = subplots(figsize=(8, 8))\n",
-    "ax = plot_gam(gam_logit_, 0)\n",
-    "ax.set_xlabel('Age')\n",
-    "ax.set_ylabel('Effect on wage')\n",
-    "ax.set_title('Partial dependence of high earner status on age', fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94f2d878",
-   "metadata": {},
-   "source": [
-    "## Local Regression\n",
-    "We illustrate the use of local regression using  the `lowess()` \n",
-    "function from `sm.nonparametric`. Some implementations of\n",
-    "GAMs allow terms to be local regression operators; this is not the case in `pygam`.\n",
-    "\n",
-    "Here we fit local linear regression models using spans of 0.2\n",
-    "and 0.5; that is, each neighborhood consists of 20% or 50% of\n",
-    "the observations. As expected, using a span of 0.5 is smoother than 0.2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "ecef97c8",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-04T23:19:38.854567Z",
-     "iopub.status.busy": "2024-06-04T23:19:38.854494Z",
-     "iopub.status.idle": "2024-06-04T23:19:38.953710Z",
-     "shell.execute_reply": "2024-06-04T23:19:38.953441Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+  ],
+  "metadata": {
+    "jupytext": {
+      "cell_metadata_filter": "-all",
+      "main_language": "python",
+      "notebook_metadata_filter": "-all"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
     }
-   ],
-   "source": [
-    "lowess = sm.nonparametric.lowess\n",
-    "fig, ax = subplots(figsize=(8,8))\n",
-    "ax.scatter(age, y, facecolor='gray', alpha=0.5)\n",
-    "for span in [0.2, 0.5]:\n",
-    "    fitted = lowess(y,\n",
-    "                    age,\n",
-    "                    frac=span,\n",
-    "                    xvals=age_grid)\n",
-    "    ax.plot(age_grid,\n",
-    "            fitted,\n",
-    "            label='{:.1f}'.format(span),\n",
-    "            linewidth=4)\n",
-    "ax.set_xlabel('Age', fontsize=20)\n",
-    "ax.set_ylabel('Wage', fontsize=20);\n",
-    "ax.legend(title='span', fontsize=15);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "163ef0c0",
-   "metadata": {},
-   "source": [
-    "  \n",
-    " \n"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
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diff --git a/Figure.png b/Figure.png
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