def test_negative_scalar_index(adata, index: int, obs: bool):
    pos_index = index + (adata.n_obs if obs else adata.n_vars)

    if obs:
        adata_pos_subset = adata[pos_index]
        adata_neg_subset = adata[index]
    else:
        adata_pos_subset = adata[:, pos_index]
        adata_neg_subset = adata[:, index]

    np.testing.assert_array_equal(
        adata_pos_subset.obs_names, adata_neg_subset.obs_names
    )
    np.testing.assert_array_equal(
        adata_pos_subset.var_names, adata_neg_subset.var_names
    )
def __init__(self, root, split="train", pass_name="clean", transforms=None):
        super().__init__(root=root, transforms=transforms)

        verify_str_arg(split, "split", valid_values=("train", "test"))
        verify_str_arg(pass_name, "pass_name", valid_values=("clean", "final", "both"))
        passes = ["clean", "final"] if pass_name == "both" else [pass_name]

        root = Path(root) / "Sintel"
        flow_root = root / "training" / "flow"

        for pass_name in passes:
            split_dir = "training" if split == "train" else split
            image_root = root / split_dir / pass_name
            for scene in os.listdir(image_root):
                image_list = sorted(glob(str(image_root / scene / "*.png")))
                for i in range(len(image_list) - 1):
                    self._image_list += [[image_list[i], image_list[i + 1]]]

                if split == "train":
                    self._flow_list += sorted(glob(str(flow_root / scene / "*.flo")))