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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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