def test_set_varm(adata):
    init_hash = joblib.hash(adata)

    dim0_size = np.random.randint(2, adata.shape[1] - 1)
    dim1_size = np.random.randint(1, 99)
    orig_varm_val = adata.varm["o"].copy()
    subset_idx = np.random.choice(adata.var_names, dim0_size, replace=False)

    subset = adata[:, subset_idx]
    assert subset.is_view
    subset.varm = dict(o=np.ones((dim0_size, dim1_size)))
    assert not subset.is_view
    assert np.all(orig_varm_val == adata.varm["o"])  # Checking for mutation
    assert np.all(subset.varm["o"] == np.ones((dim0_size, dim1_size)))

    subset = adata[:, subset_idx]
    subset_hash = joblib.hash(subset)
    with pytest.raises(ValueError):
        subset.varm = dict(o=np.ones((dim0_size + 1, dim1_size)))
    with pytest.raises(ValueError):
        subset.varm = dict(o=np.ones((dim0_size - 1, dim1_size)))
    # subset should not be changed by failed setting
    assert subset_hash == joblib.hash(subset)
    assert init_hash == joblib.hash(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)