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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 get_meta_graph_def(saved_model_dir, tag_set):
 """Utility function to read a meta_graph_def from disk. From `saved_model_cli.py <https://github.com/tensorflow/tensorflow/blob/8e0e8d41a3a8f2d4a6100c2ea1dc9d6c6c4ad382/tensorflow/python/tools/saved_model_cli.py#L186>`_ Args: :saved_model_dir: path to saved_model. :tag_set: list of string tags identifying the TensorFlow graph within the saved_model. Returns: A TensorFlow meta_graph_def, or raises an Exception otherwise. """

  saved_model = reader.read_saved_model(saved_model_dir)
  set_of_tags = set(tag_set.split(','))
  for meta_graph_def in saved_model.meta_graphs:
    if set(meta_graph_def.meta_info_def.tags) == set_of_tags:
      return meta_graph_def
  raise RuntimeError("MetaGraphDef associated with tag-set {0} could not be found in SavedModel".format(tag_set))