issues: 2099591300

What happened?

Vectorized indexing can fail for array types that strictly follow the array API standard.

What did you expect to happen?

Vectorized indexing to all work.

Minimal Complete Verifiable Example

```Python import numpy.array_api as nxp

da = xr.DataArray( nxp.reshape(nxp.arange(12), (3, 4)), dims=["x", "y"], coords={"x": [0, 1, 2], "y": ["a", "b", "c", "d"]}, )

da[[0, 2, 2], [1, 3]] # works

ind_x = xr.DataArray([0, 1], dims=["x"]) ind_y = xr.DataArray([0, 1], dims=["y"])

da[ind_x, ind_y] # works

da[[0, 1], ind_x] # doesn't work

---

TypeError Traceback (most recent call last) Cell In[157], line 1 ----> 1 da[[0, 1], ind_x]

File ~/Documents/Work/Code/xarray/xarray/core/dataarray.py:859, in DataArray.getitem(self, key) 856 return self._getitem_coord(key) 857 else: 858 # xarray-style array indexing --> 859 return self.isel(indexers=self._item_key_to_dict(key))

File ~/Documents/Work/Code/xarray/xarray/core/dataarray.py:1472, in DataArray.isel(self, indexers, drop, missing_dims, **indexers_kwargs) 1469 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "isel") 1471 if any(is_fancy_indexer(idx) for idx in indexers.values()): -> 1472 ds = self._to_temp_dataset()._isel_fancy( 1473 indexers, drop=drop, missing_dims=missing_dims 1474 ) 1475 return self._from_temp_dataset(ds) 1477 # Much faster algorithm for when all indexers are ints, slices, one-dimensional 1478 # lists, or zero or one-dimensional np.ndarray's

File ~/Documents/Work/Code/xarray/xarray/core/dataset.py:3001, in Dataset._isel_fancy(self, indexers, drop, missing_dims) 2997 var_indexers = { 2998 k: v for k, v in valid_indexers.items() if k in var.dims 2999 } 3000 if var_indexers: -> 3001 new_var = var.isel(indexers=var_indexers) 3002 # drop scalar coordinates 3003 # https://github.com/pydata/xarray/issues/6554 3004 if name in self.coords and drop and new_var.ndim == 0:

File ~/Documents/Work/Code/xarray/xarray/core/variable.py:1130, in Variable.isel(self, indexers, missing_dims, **indexers_kwargs) 1127 indexers = drop_dims_from_indexers(indexers, self.dims, missing_dims) 1129 key = tuple(indexers.get(dim, slice(None)) for dim in self.dims) -> 1130 return self[key]

File ~/Documents/Work/Code/xarray/xarray/core/variable.py:812, in Variable.getitem(self, key) 799 """Return a new Variable object whose contents are consistent with 800 getting the provided key from the underlying data. 801 (...) 809 array x.values directly. 810 """ 811 dims, indexer, new_order = self._broadcast_indexes(key) --> 812 data = as_indexable(self._data)[indexer] 813 if new_order: 814 data = np.moveaxis(data, range(len(new_order)), new_order)

File ~/Documents/Work/Code/xarray/xarray/core/indexing.py:1390, in ArrayApiIndexingAdapter.getitem(self, key) 1388 else: 1389 if isinstance(key, VectorizedIndexer): -> 1390 raise TypeError("Vectorized indexing is not supported") 1391 else: 1392 raise TypeError(f"Unrecognized indexer: {key}")

TypeError: Vectorized indexing is not supported ```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.
• [X] Recent environment — the issue occurs with the latest version of xarray and its dependencies.

Relevant log output

No response

Anything else we need to know?

I don't really understand why the first two examples work but the last one doesn't...

Environment

main branch of xarray, numpy 1.26.0