def test_set_obs(adata, subset_func):
    init_hash = joblib.hash(adata)

    subset = adata[subset_func(adata.obs_names), :]

    new_obs = pd.DataFrame(
        dict(a=np.ones(subset.n_obs), b=np.ones(subset.n_obs)),
        index=subset.obs_names,
    )

    assert subset.is_view
    subset.obs = new_obs
    assert not subset.is_view
    assert np.all(subset.obs == new_obs)

    assert joblib.hash(adata) == init_hash
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