def test_not_set_subset_X(matrix_type, subset_func):
    adata = ad.AnnData(matrix_type(asarray(sparse.random(20, 20))))
    init_hash = joblib.hash(adata)
    orig_X_val = adata.X.copy()
    while True:
        subset_idx = slice_subset(adata.obs_names)
        if len(adata[subset_idx, :]) > 2:
            break
    subset = adata[subset_idx, :]

    subset = adata[:, subset_idx]

    internal_idx = _normalize_index(
        subset_func(np.arange(subset.X.shape[1])), subset.var_names
    )
    assert subset.is_view
    subset.X[:, internal_idx] = 1
    assert not subset.is_view
    assert not np.any(asarray(adata.X != orig_X_val))

    assert init_hash == joblib.hash(adata)
def _try_run_local_init_op(self, sess):
    """Tries to run _local_init_op, if not None, and is ready for local init.

    Args:
      sess: A `Session`.

    Returns:
      A tuple (is_successful, msg), where is_successful is True if
      _local_init_op is None, or we ran _local_init_op, and False otherwise;
      and msg is a `String` with the reason why the model was not ready to run
      local init.
    """
    if self._local_init_op is not None:
      is_ready_for_local_init, msg = self._model_ready_for_local_init(sess)
      if is_ready_for_local_init:
        logging.info("Running local_init_op.")
        sess.run(self._local_init_op, feed_dict=self._local_init_feed_dict,
                 options=self._local_init_run_options)
        logging.info("Done running local_init_op.")
        return True, None
      else:
        return False, msg
    return True, None