def test_set_subset_varm(adata, subset_func):
    init_hash = joblib.hash(adata)
    orig_varm_val = adata.varm["o"].copy()

    while True:
        subset_idx = slice_subset(adata.var_names)
        if (adata[:, subset_idx]).shape[1] > 2:
            break
    subset = adata[:, subset_idx]

    internal_idx = _normalize_index(
        subset_func(np.arange(subset.varm["o"].shape[0])), subset.var_names
    )

    assert subset.is_view
    subset.varm["o"][internal_idx] = 1
    assert not subset.is_view
    assert np.all(adata.varm["o"] == orig_varm_val)

    assert init_hash == joblib.hash(adata)
def __getitem__(self, index):

        img1 = self._read_img(self._image_list[index][0])
        img2 = self._read_img(self._image_list[index][1])

        if self._flow_list:  # it will be empty for some dataset when split="test"
            flow = self._read_flow(self._flow_list[index])
            if self._has_builtin_flow_mask:
                flow, valid_flow_mask = flow
            else:
                valid_flow_mask = None
        else:
            flow = valid_flow_mask = None

        if self.transforms is not None:
            img1, img2, flow, valid_flow_mask = self.transforms(img1, img2, flow, valid_flow_mask)

        if self._has_builtin_flow_mask or valid_flow_mask is not None:
            # The `or valid_flow_mask is not None` part is here because the mask can be generated within a transform
            return img1, img2, flow, valid_flow_mask
        else:
            return img1, img2, flow