Make arrays_are_shapes keyword explicit in broadcast_shape_iter

aesara/tensor/extra_ops.py

 from collections.abc import Collection
-from typing import Tuple
+from typing import Iterable, Tuple, Union
 
 import numpy as np
 
@@ -1482,17 +1482,20 @@ def broadcast_shape(*arrays, **kwargs):
     return broadcast_shape_iter(arrays, **kwargs)
 
 
-def broadcast_shape_iter(arrays, **kwargs):
+def broadcast_shape_iter(
+    arrays: Iterable[Union[TensorVariable, Tuple[TensorVariable, ...]]],
+    arrays_are_shapes: bool = False,
+):
     """Compute the shape resulting from broadcasting arrays.
 
     Parameters
     ----------
-    arrays: Iterable[TensorVariable] or Iterable[Tuple[Variable]]
+    arrays
         An iterable of tensors, or a tuple of shapes (as tuples),
         for which the broadcast shape is computed.
         XXX: Do not call this with a generator/iterator; this function will not
         make copies!
-    arrays_are_shapes: bool (Optional)
+    arrays_are_shapes
         Indicates whether or not the `arrays` contains shape tuples.
         If you use this approach, make sure that the broadcastable dimensions
         are (scalar) constants with the value ``1`` or ``1`` exactly.
@@ -1500,7 +1503,6 @@ def broadcast_shape_iter(arrays, **kwargs):
     """
     one = aesara.scalar.ScalarConstant(aesara.scalar.int64, 1)
 
-    arrays_are_shapes = kwargs.pop("arrays_are_shapes", False)
     if arrays_are_shapes:
         max_dims = max(len(a) for a in arrays)
 
@@ -1560,6 +1562,7 @@ def broadcast_shape_iter(arrays, **kwargs):
 
 
 class BroadcastTo(Op):
+    """An `Op` for `numpy.broadcast_to`."""
 
     view_map = {0: [0]}