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 _read_16bits_png_with_flow_and_valid_mask(file_name):

    flow_and_valid = _read_png_16(file_name).to(torch.float32)
    flow, valid_flow_mask = flow_and_valid[:2, :, :], flow_and_valid[2, :, :]
    flow = (flow - 2 ** 15) / 64  # This conversion is explained somewhere on the kitti archive
    valid_flow_mask = valid_flow_mask.bool()

    # For consistency with other datasets, we convert to numpy
    return flow.numpy(), valid_flow_mask.numpy()