Fix type hints here and there

pytensor/__init__.py

@@ -108,7 +108,7 @@ def as_symbolic(x: Any, name: str | None = None, **kwargs) -> Variable:
 
 
 @singledispatch
-def _as_symbolic(x, **kwargs) -> Variable:
+def _as_symbolic(x: Any, **kwargs) -> Variable:
     from pytensor.tensor import as_tensor_variable
 
     return as_tensor_variable(x, **kwargs)

pytensor/graph/basic.py

@@ -1302,8 +1302,8 @@ def clone_node_and_cache(
 
 
 def clone_get_equiv(
-    inputs: Sequence[Variable],
-    outputs: Sequence[Variable],
+    inputs: Iterable[Variable],
+    outputs: Reversible[Variable],
     copy_inputs: bool = True,
     copy_orphans: bool = True,
     memo: dict[Union[Apply, Variable, "Op"], Union[Apply, Variable, "Op"]]

pytensor/tensor/random/op.py

 import warnings
 from collections.abc import Sequence
 from copy import copy
-from typing import cast
+from typing import Any, cast
 
 import numpy as np
 
@@ -218,6 +218,7 @@ class RandomVariable(Op):
 
         from pytensor.tensor.extra_ops import broadcast_shape_iter
 
+        supp_shape: tuple[Any]
         if self.ndim_supp == 0:
             supp_shape = ()
         else:

pytensor/tensor/random/utils.py

@@ -147,7 +147,9 @@ def explicit_expand_dims(
     return new_params
 
 
-def compute_batch_shape(params, ndims_params: Sequence[int]) -> TensorVariable:
+def compute_batch_shape(
+    params: Sequence[TensorVariable], ndims_params: Sequence[int]
+) -> TensorVariable:
     params = explicit_expand_dims(params, ndims_params)
     batch_params = [
         param[(..., *(0,) * core_ndim)]

pytensor/tensor/shape.py

@@ -144,14 +144,14 @@ class Shape(COp):
 _shape = Shape()
 
 
-def shape(x: np.ndarray | Number | Variable) -> Variable:
+def shape(x: np.ndarray | Number | Variable) -> TensorVariable:
     """Return the shape of `x`."""
     if not isinstance(x, Variable):
         # The following is a type error in Python 3.9 but not 3.12.
         # Thus we need to ignore unused-ignore on 3.12.
         x = ptb.as_tensor_variable(x)  # type: ignore[arg-type,unused-ignore]
 
-    return cast(Variable, _shape(x))
+    return cast(TensorVariable, _shape(x))
 
 
 @_get_vector_length.register(Shape)  # type: ignore
@@ -195,7 +195,7 @@ def shape_tuple(x: TensorVariable) -> tuple[Variable, ...]:
             # TODO: Why not use uint64?
             res += (pytensor.scalar.ScalarConstant(pytensor.scalar.int64, shape_val),)
         else:
-            res += (symbolic_shape[i],)  # type: ignore
+            res += (symbolic_shape[i],)
 
     return res
 

pytensor/tensor/type.py

@@ -138,7 +138,7 @@ class TensorType(CType[np.ndarray], HasDataType, HasShape):
             shape = self.shape
         return type(self)(dtype, shape, name=self.name)
 
-    def filter(self, data, strict=False, allow_downcast=None):
+    def filter(self, data, strict=False, allow_downcast=None) -> np.ndarray:
         """Convert `data` to something which can be associated to a `TensorVariable`.
 
         This function is not meant to be called in user code. It is for