Make the names of arguments of overriding methods consistent

Author: Armavica

pytensor/link/c/type.py

@@ -73,7 +73,7 @@ class Generic(CType, Singleton):
     def filter(self, data, strict=False, allow_downcast=None):
         return data
 
-    def is_valid_value(self, a):
+    def is_valid_value(self, data, strict: bool = True) -> bool:
         return True
 
     def c_declare(self, name, sub, check_input=True):

pytensor/sparse/type.py

@@ -98,31 +98,28 @@ def clone(
             shape = self.shape
         return type(self)(format, dtype, shape=shape, **kwargs)
 
-    def filter(self, value, strict=False, allow_downcast=None):
-        if isinstance(value, Variable):
+    def filter(self, data, strict: bool = False, allow_downcast=None):
+        if isinstance(data, Variable):
             raise TypeError(
                 "Expected an array-like object, but found a Variable: "
                 "maybe you are trying to call a function on a (possibly "
                 "shared) variable instead of a numeric array?"
             )
 
-        if (
-            isinstance(value, self.format_cls[self.format])
-            and value.dtype == self.dtype
-        ):
-            return value
+        if isinstance(data, self.format_cls[self.format]) and data.dtype == self.dtype:
+            return data
 
         if strict:
             raise TypeError(
-                f"{value} is not sparse, or not the right dtype (is {value.dtype}, "
+                f"{data} is not sparse, or not the right dtype (is {data.dtype}, "
                 f"expected {self.dtype})"
             )
 
         # The input format could be converted here
         if allow_downcast:
-            sp = self.format_cls[self.format](value, dtype=self.dtype)
+            sp = self.format_cls[self.format](data, dtype=self.dtype)
         else:
-            data = self.format_cls[self.format](value)
+            data = self.format_cls[self.format](data)
             up_dtype = ps.upcast(self.dtype, data.dtype)
             if up_dtype != self.dtype:
                 raise TypeError(f"Expected {self.dtype} dtype but got {data.dtype}")
@@ -209,8 +206,8 @@ def values_eq(self, a, b):
             and abs(a - b).sum() == 0.0
         )
 
-    def is_valid_value(self, a):
-        return scipy.sparse.issparse(a) and (a.format == self.format)
+    def is_valid_value(self, data, strict: bool = True):
+        return scipy.sparse.issparse(data) and (data.format == self.format)
 
     def get_shape_info(self, obj):
         obj = self.filter(obj)

pytensor/tensor/type.py

@@ -1,7 +1,7 @@
 import logging
 import warnings
 from collections.abc import Iterable
-from typing import TYPE_CHECKING, Literal, Optional
+from typing import TYPE_CHECKING, Any, Literal, Optional
 
 import numpy as np
 import numpy.typing as npt
@@ -630,8 +630,8 @@ def c_code_cache_version(self):
 
 
 class DenseTypeMeta(MetaType):
-    def __instancecheck__(self, o):
-        if type(o) is TensorType or isinstance(o, DenseTypeMeta):
+    def __instancecheck__(self, instance: Any) -> bool:
+        if type(instance) is TensorType or isinstance(instance, DenseTypeMeta):
             return True
         return False
 

pytensor/tensor/variable.py

@@ -3,7 +3,7 @@
 import warnings
 from collections.abc import Iterable
 from numbers import Number
-from typing import TypeVar
+from typing import Any, TypeVar
 
 import numpy as np
 
@@ -1117,8 +1117,8 @@ def __deepcopy__(self, memo):
 
 
 class DenseVariableMeta(MetaType):
-    def __instancecheck__(self, o):
-        if type(o) is TensorVariable or isinstance(o, DenseVariableMeta):
+    def __instancecheck__(self, instance: Any) -> bool:
+        if type(instance) is TensorVariable or isinstance(instance, DenseVariableMeta):
             return True
         return False
 
@@ -1132,8 +1132,8 @@ class DenseTensorVariable(TensorType, metaclass=DenseVariableMeta):
 
 
 class DenseConstantMeta(MetaType):
-    def __instancecheck__(self, o):
-        if type(o) is TensorConstant or isinstance(o, DenseConstantMeta):
+    def __instancecheck__(self, instance: Any) -> bool:
+        if type(instance) is TensorConstant or isinstance(instance, DenseConstantMeta):
             return True
         return False