def freeze_graph_with_def_protos(
    input_graph_def,
    input_saver_def,
    input_checkpoint,
    output_node_names,
    restore_op_name,
    filename_tensor_name,
    clear_devices,
    initializer_nodes,
    variable_names_blacklist=''):
  """Converts all variables in a graph and checkpoint into constants."""
  del restore_op_name, filename_tensor_name  # Unused by updated loading code.

  # 'input_checkpoint' may be a prefix if we're using Saver V2 format
  if not saver_lib.checkpoint_exists(input_checkpoint):
    raise ValueError(
        'Input checkpoint "' + input_checkpoint + '" does not exist!')

  if not output_node_names:
    raise ValueError(
        'You must supply the name of a node to --output_node_names.')

  # Remove all the explicit device specifications for this node. This helps to
  # make the graph more portable.
  if clear_devices:
    for node in input_graph_def.node:
      node.device = ''

  _ = importer.import_graph_def(input_graph_def, name='')

  with session.Session() as sess:
    if input_saver_def:
      saver = saver_lib.Saver(saver_def=input_saver_def)
      saver.restore(sess, input_checkpoint)
    else:
      var_list = {}
      reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint)
      var_to_shape_map = reader.get_variable_to_shape_map()
      for key in var_to_shape_map:
        try:
          tensor = sess.graph.get_tensor_by_name(key + ':0')
        except KeyError:
          # This tensor doesn't exist in the graph (for example it's
          # 'global_step' or a similar housekeeping element) so skip it.
          continue
        var_list[key] = tensor
      saver = saver_lib.Saver(var_list=var_list)
      saver.restore(sess, input_checkpoint)
      if initializer_nodes:
        sess.run(initializer_nodes)

    variable_names_blacklist = (variable_names_blacklist.split(',') if
                                variable_names_blacklist else None)
    output_graph_def = graph_util.convert_variables_to_constants(
        sess,
        input_graph_def,
        output_node_names.split(','),
        variable_names_blacklist=variable_names_blacklist)

  return output_graph_def