code: def _add_tf_shape(attr_dict, ints):
      """
      Converts a list of ints to a TensorShapeProto representing the dimensions of
      a blob/object.
  
      Args:
          attr_dict: Dictionary to update (usually attributes of a Node)
          ints: List of integers representing dimensions of some object.
  
      Returns:
          None. Modifies attr_dict in-place.
      """
      shape_proto = TensorShapeProto()
      for i in ints:
          dim = TensorShapeProto.Dim()
          dim.size = i
          shape_proto.dim.extend([dim])
      attr_dict["_output_shapes"].list.shape.extend([shape_proto])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import copy
  import logging
  import os
  import re
  from builtins import bytes
  from caffe2.proto import caffe2_pb2
  from caffe2.python import core, workspace
  from .proto.graph_pb2 import GraphDef
  from .proto.node_def_pb2 import NodeDef
  from .proto.tensor_shape_pb2 import TensorShapeProto
label_desc: Use image node_id 11 for calling a global function with example usage: _add_tf_shape(attr_dict, ints) without return types