Make code compatible with Numpy >= 2.1. (#21032)

Starting with 2.1, the first argument of `np.reshape` is positional only.

Removed keyword `a` and for consistency did the same with other backends.

Author: hertschuh

keras/src/backend/jax/image.py

@@ -465,7 +465,7 @@ def affine_transform(
     # transform the indices
     coordinates = jnp.einsum("Bhwij, Bjk -> Bhwik", indices, transform)
     coordinates = jnp.moveaxis(coordinates, source=-1, destination=1)
-    coordinates += jnp.reshape(a=offset, shape=(*offset.shape, 1, 1, 1))
+    coordinates += jnp.reshape(offset, shape=(*offset.shape, 1, 1, 1))
 
     # apply affine transformation
     _map_coordinates = functools.partial(

keras/src/backend/numpy/image.py

@@ -600,7 +600,7 @@ def affine_transform(
     # transform the indices
     coordinates = np.einsum("Bhwij, Bjk -> Bhwik", indices, transform)
     coordinates = np.moveaxis(coordinates, source=-1, destination=1)
-    coordinates += np.reshape(a=offset, newshape=(*offset.shape, 1, 1, 1))
+    coordinates += np.reshape(offset, newshape=(*offset.shape, 1, 1, 1))
 
     # apply affine transformation
     affined = np.stack(

keras/src/backend/torch/image.py

@@ -424,7 +424,7 @@ def affine_transform(
     # transform the indices
     coordinates = torch.einsum("Bhwij, Bjk -> Bhwik", indices, transform)
     coordinates = torch.moveaxis(coordinates, source=-1, destination=1)
-    coordinates += torch.reshape(a=offset, shape=(*offset.shape, 1, 1, 1))
+    coordinates += torch.reshape(offset, shape=(*offset.shape, 1, 1, 1))
 
     # Note: torch.stack is faster than torch.vmap when the batch size is small.
     affined = torch.stack(