def METHOD_NAME(saved_model_path,
                                        drop_collections=None):
  """Experimental: Create a ModuleSpec out of a SavedModel from TF1.
  Warning: Deprecated. This belongs to the hub.Module API and TF1 Hub format.
  For TF2, TensorFlow Hub ships plain SavedModels, removing the need for
  conversions like this.
  Define a ModuleSpec from a SavedModel. Note that this is not guaranteed to
  work in all cases and it assumes the SavedModel has followed some conventions:
  - The serialized SaverDef can be ignored and instead can be reconstructed.
  - The init op and main op can be ignored and instead the module can be
    initialized by using the conventions followed by
    `tf.train.MonitoredSession`.
  Note that the set of features supported can increase over time and have side
  effects that were not previously visible. The pattern followed to avoid
  surprises is forcing users to declare which features to ignore (even
  if they are not supported).
  Note that this function creates a ModuleSpec that when exported exports a
  Module (based on a modified copy of the original SavedModel) and not a
  SavedModel.
  THIS FUNCTION IS DEPRECATED.
  Args:
    saved_model_path: Directory with the SavedModel to use.
    drop_collections: Additionally list of collection to drop.
  Returns:
    A ModuleSpec.
  """
  saved_model_handler = saved_model_lib.load(saved_model_path)
  checkpoint_filename = saved_model_lib.get_variables_path(saved_model_path)
  drop_collections = (set(_ALWAYS_DROPPED_COLLECTIONS) |
                      (set(drop_collections) if drop_collections else set()))
  _drop_collections(saved_model_handler, drop_collections)
  return native_module._ModuleSpec(saved_model_handler, checkpoint_filename)  # pylint: disable=protected-access