Make xarray_jax.JaxArrayWrapper fully compliant with the xarray.named…

…array._typing._array_function protocol from more recent versions of xarray (see https://github.com/pydata/xarray/blob/693f0b91b4381f5a672cb93ff8113abd1dc4957c/xarray/namedarray/_typing.py#L114).

This is required for more recent versions of xarray to recognise it as a duck-typed array. Otherwise xarray will try to convert it to a numpy array in some situations, in particular reductions which now go via NamedArray. This causes problems if done to a jax tracer.

The only change required to fulfill the protocol was to add `real` and `imag` properties to JaxArrayWrapper.

PiperOrigin-RevId: 595985462
Change-Id: I4c844b5fd7d7787f0e35cc210979d679ae821044

graphcast/xarray_jax.py

@@ -404,11 +404,14 @@ class JaxArrayWrapper(np.lib.mixins.NDArrayOperatorsMixin):
   """Wraps a JAX array into a duck-typed array suitable for use with xarray.
 
   This uses an older duck-typed array protocol based on __array_ufunc__ and
-  __array_function__ which works with numpy and xarray. This is in the process
-  of being superseded by the Python array API standard
-  (https://data-apis.org/array-api/latest/index.html), but JAX and xarray
-  haven't implemented it yet. Once they have, we should be able to get rid of
+  __array_function__ which works with numpy and xarray. (In newer versions
+  of xarray it implements xarray.namedarray._typing._array_function.)
+
+  This is in the process of being superseded by the Python array API standard
+  (https://data-apis.org/array-api/latest/index.html), but JAX hasn't
+  implemented it yet. Once they have, we should be able to get rid of
   this wrapper and use JAX arrays directly with xarray.
+
   """
 
   def __init__(self, jax_array):
@@ -464,6 +467,14 @@ def ndim(self):
   def size(self):
     return self.jax_array.size
 
+  @property
+  def real(self):
+    return self.jax_array.real
+
+  @property
+  def imag(self):
+    return self.jax_array.imag
+
   # Array methods not covered by NDArrayOperatorsMixin:
 
   # Allows conversion to numpy array using np.asarray etc. Warning: doing this