code: def create_module_graph(module_spec):
    
    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
docs: 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.