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 update_state(self, y_true, y_pred, sample_weight=None):
    sample_weight = _apply_mask(y_true, sample_weight, self._masked_tokens,
                                self._dtype)
    num_classes = tf.shape(y_pred)[-1]
    y_true = tf.reshape(y_true, [-1])
    y_pred = tf.reshape(y_pred, [-1, num_classes])
    sample_weight = tf.reshape(sample_weight, [-1])
    super().update_state(y_true, y_pred, sample_weight)