code: def create_module_graph(module_spec):
      """Creates a graph and loads Hub Module into it.
  
      Args:
        module_spec: the hub.ModuleSpec for the image module being used.
  
      Returns:
        graph: the tf.Graph that was created.
        bottleneck_tensor: the bottleneck values output by the module.
        resized_input_tensor: the input images, resized as expected by the module.
        wants_quantization: a boolean, whether the module has been instrumented
          with fake quantization ops.
      """
      height, width = hub.get_expected_image_size(module_spec)
      with tf.Graph().as_default() as graph:
          resized_input_tensor = tf.placeholder(
              tf.float32, [None, height, width, 3]
          )
          m = hub.Module(module_spec)
          bottleneck_tensor = m(resized_input_tensor)
          wants_quantization = any(
              node.op in FAKE_QUANT_OPS
              for node in graph.as_graph_def().node
          )
      return (
          graph,
          bottleneck_tensor,
          resized_input_tensor,
          wants_quantization,
      )
  
imports: from absl import logging
  import argparse
  import collections
  from datetime import datetime
  import hashlib
  import os.path
  import random
  import re
  import sys
  import numpy
  import tensorflow
  import tensorflow_hub
  from tensorflow.contrib import quantize
label_desc: Use image node_id 4 for calling a global function with example usage: create_module_graph(module_spec) and returns: graph,  bottleneck_tensor,  resized_input_tensor,  wants_quantization