[JAX] Update JAX users in preparation for a change that makes iterati…

…on over a JAX array return JAX arrays, instead of NumPy arrays.

See jax-ml/jax#8043 for context as to why we are making this change.

The upshot for most users is that the values returned by iteration over a JAX array are now themselves JAX arrays, with the semantics of JAX arrays, which sometimes differ from the semantics of NumPy scalars and arrays. In particular:

* Unlike NumPy scalars 0-dimensional JAX arrays are not hashable. This change updates users to call `.tolist()` or `np.asarray(...)` when the output of iterating over a JAX array is hashed, used as a dictionary key, or passed to `set(...)`. In some instances, we can just call `numpy` functions instead of `jax.numpy` functions to build the array in the first place.
* This change confuses Pandas and PIL when a JAX array is converted to a Pandas dataframe or a PIL image. For now, cast JAX arrays to a NumPy array first before passing them into those libraries.
* We now need to use `numpy.testing.assert_array_equal` instead of `numpy.testing.assert_equal` to compare JAX arrays.

PiperOrigin-RevId: 406150402

distrax/_src/distributions/distribution.py

@@ -280,7 +280,7 @@ def to_batch_shape_index(
     A new index that is only applied on the batch shape.
   """
   try:
-    new_index = [x[index] for x in jnp.indices(batch_shape)]
+    new_index = [x[index] for x in np.indices(batch_shape)]
     return tuple(new_index)
   except IndexError as e:
     raise IndexError(f'Batch shape `{batch_shape}` not compatible with index '