parameters: (save_path)
return_statement: return object_graph_proto
docstring: Retrieves information about the objects in a checkpoint.
  
    Example usage:
  
    ```python
    object_graph = tf.contrib.checkpoint.object_metadata(
        tf.train.latest_checkpoint(checkpoint_directory))
    ckpt_variable_names = set()
    for node in object_graph.nodes:
      for attribute in node.attributes:
        ckpt_variable_names.add(attribute.full_name)
    ```
  
    Args:
      save_path: The path to the checkpoint, as returned by `save` or
        `tf.train.latest_checkpoint`.
  
    Returns:
      A parsed `tf.contrib.checkpoint.TrackableObjectGraph` protocol buffer.
    Raises:
      ValueError: If an object graph was not found in the checkpoint.
docstring_summary: Retrieves information about the objects in a checkpoint.
docstring_tokens: Retrieves information about the objects in a checkpoint .
function: def object_metadata(save_path):
    """Retrieves information about the objects in a checkpoint.
  
    Example usage:
  
    ```python
    object_graph = tf.contrib.checkpoint.object_metadata(
        tf.train.latest_checkpoint(checkpoint_directory))
    ckpt_variable_names = set()
    for node in object_graph.nodes:
      for attribute in node.attributes:
        ckpt_variable_names.add(attribute.full_name)
    ```
  
    Args:
      save_path: The path to the checkpoint, as returned by `save` or
        `tf.train.latest_checkpoint`.
  
    Returns:
      A parsed `tf.contrib.checkpoint.TrackableObjectGraph` protocol buffer.
    Raises:
      ValueError: If an object graph was not found in the checkpoint.
    """
    reader = py_checkpoint_reader.NewCheckpointReader(save_path)
    try:
      object_graph_string = reader.get_tensor(base.OBJECT_GRAPH_PROTO_KEY)
    except errors_impl.NotFoundError:
      raise ValueError(
          f"The specified checkpoint \"{save_path}\" does not appear to be "
          "object-based (saved with TF2) since it is missing the key "
          f"\"{base.OBJECT_GRAPH_PROTO_KEY}\". Likely it was created with the "
          "TF1 name-based saver and does not contain an object dependency graph.")
    object_graph_proto = (trackable_object_graph_pb2.TrackableObjectGraph())
    object_graph_proto.ParseFromString(object_graph_string)
    return object_graph_proto
function_tokens: def object_metadata ( save_path ) : reader = py_checkpoint_reader . NewCheckpointReader ( save_path ) try : object_graph_string = reader . get_tensor ( base . OBJECT_GRAPH_PROTO_KEY ) except errors_impl . NotFoundError : raise ValueError ( f"The specified checkpoint \"{save_path}\" does not appear to be " "object-based (saved with TF2) since it is missing the key " f"\"{base.OBJECT_GRAPH_PROTO_KEY}\". Likely it was created with the " "TF1 name-based saver and does not contain an object dependency graph." ) object_graph_proto = ( trackable_object_graph_pb2 . TrackableObjectGraph ( ) ) object_graph_proto . ParseFromString ( object_graph_string ) return object_graph_proto