def test_layers_view():
    X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    L = np.array([[10, 11, 12], [13, 14, 15], [16, 17, 18]])
    real_adata = ad.AnnData(X)
    real_adata.layers["L"] = L
    view_adata = real_adata[1:, 1:]
    real_hash = joblib.hash(real_adata)
    view_hash = joblib.hash(view_adata)

    assert view_adata.is_view

    with pytest.raises(ValueError):
        view_adata.layers["L2"] = L + 2

    assert view_adata.is_view  # Failing to set layer item makes adata not view
    assert real_hash == joblib.hash(real_adata)
    assert view_hash == joblib.hash(view_adata)

    view_adata.layers["L2"] = L[1:, 1:] + 2

    assert not view_adata.is_view
    assert real_hash == joblib.hash(real_adata)
    assert view_hash != joblib.hash(view_adata)
def _restore_checkpoint_and_maybe_run_saved_model_initializers(
    sess, saver, path):
  """Restores checkpoint values and SavedModel initializers if found."""
  # NOTE: All references to SavedModel refer to SavedModels loaded from the
  # load_v2 API (which does not require the `sess` argument).

  # If the graph contains resources loaded from a SavedModel, they are not
  # restored when calling `saver.restore`. Thus, the SavedModel initializer must
  # be called with `saver.restore` to properly initialize the model.

  # The SavedModel init is stored in the "saved_model_initializers" collection.
  # This collection is part of the MetaGraph's default_init_op, so it is already
  # called by MonitoredSession as long as the saver doesn't restore any
  # checkpoints from the working dir.
  saved_model_init_ops = ops.get_collection("saved_model_initializers")
  if saved_model_init_ops:
    sess.run(saved_model_init_ops)

  # The saver must be called *after* the SavedModel init, because the SavedModel
  # init will restore the variables from the SavedModel variables directory.
  # Initializing/restoring twice is not ideal but there's no other way to do it.
  saver.restore(sess, path)