Fix type hints in reshape.py

pytensor/tensor/reshape.py

 from collections.abc import Iterable, Sequence
 from itertools import pairwise
+from typing import TypeAlias
 
 import numpy as np
 from numpy.lib._array_utils_impl import normalize_axis_tuple
@@ -9,15 +10,20 @@ from pytensor.gradient import DisconnectedType
 from pytensor.graph import Apply
 from pytensor.graph.op import Op
 from pytensor.graph.replace import _vectorize_node
+from pytensor.scalar import ScalarVariable
 from pytensor.tensor import TensorLike, as_tensor_variable
 from pytensor.tensor.basic import expand_dims, infer_static_shape, join, split
 from pytensor.tensor.extra_ops import squeeze
 from pytensor.tensor.math import prod
-from pytensor.tensor.shape import ShapeValueType, shape
 from pytensor.tensor.type import tensor
 from pytensor.tensor.variable import TensorVariable
 
 
+ShapeValueType: TypeAlias = (
+    int | np.integer | ScalarVariable | TensorVariable | np.ndarray
+)
+
+
 class JoinDims(Op):
     __props__ = (
         "start_axis",
@@ -81,16 +87,11 @@ class JoinDims(Op):
 
         out[0] = x.reshape(output_shape)
 
-    def L_op(
-        self,
-        inputs: Sequence[Variable],
-        outputs: Sequence[Variable],
-        output_grads: Sequence[Variable],
-    ) -> list[Variable]:
+    def L_op(self, inputs, outputs, output_grads):
         (x,) = inputs
         (g_out,) = output_grads
 
-        x_shape = shape(x)
+        x_shape = x.shape
         packed_shape = [x_shape[i] for i in self.axis_range]
         return [split_dims(g_out, shape=packed_shape, axis=self.start_axis)]
 
@@ -163,7 +164,7 @@ class SplitDims(Op):
             raise ValueError("SplitDims axis must be non-negative")
         self.axis = axis
 
-    def make_node(self, x: Variable, shape: Variable) -> Apply:  # type: ignore[override]
+    def make_node(self, x, shape):
         x = as_tensor_variable(x)
         shape = as_tensor_variable(shape, dtype=int, ndim=1)
 
@@ -205,16 +206,11 @@ class SplitDims(Op):
     def connection_pattern(self, node):
         return [[True], [False]]
 
-    def L_op(
-        self,
-        inputs: Sequence[Variable],
-        outputs: Sequence[Variable],
-        output_grads: Sequence[Variable],
-    ) -> list[Variable]:
+    def L_op(self, inputs, outputs, output_grads):
         (x, _) = inputs
         (g_out,) = output_grads
 
-        n_axes = g_out.ndim - x.ndim + 1  # type: ignore[attr-defined]
+        n_axes = g_out.ndim - x.ndim + 1
         axis_range = list(range(self.axis, self.axis + n_axes))
 
         return [join_dims(g_out, axis=axis_range), DisconnectedType()()]
@@ -372,7 +368,7 @@ def _analyze_axes_list(axes) -> tuple[int, int, int]:
 
 def pack(
     *tensors: TensorLike, axes: Sequence[int] | int | None = None
-) -> tuple[TensorVariable, list[ShapeValueType]]:
+) -> tuple[TensorVariable, list[TensorVariable]]:
     """
     Combine multiple tensors by preserving the specified axes and raveling the rest into a single axis.
 
@@ -458,7 +454,7 @@ def pack(
     n_before, n_after, min_axes = _analyze_axes_list(axes)
 
     reshaped_tensors: list[Variable] = []
-    packed_shapes: list[ShapeValueType] = []
+    packed_shapes: list[TensorVariable] = []
 
     for i, input_tensor in enumerate(tensor_list):
         n_dim = input_tensor.ndim
@@ -492,7 +488,7 @@ def pack(
 def unpack(
     packed_input: TensorLike,
     axes: int | Sequence[int] | None,
-    packed_shapes: list[ShapeValueType],
+    packed_shapes: Sequence[ShapeValueType],
 ) -> list[TensorVariable]:
     """
     Unpack a packed tensor into multiple tensors by splitting along the specified axes and reshaping.