code: def transfer_elmo_model(
      vocab_file,
      options_file,
      weight_file,
      token_embedding_file,
      output_elmo_model,
  ):
      dump_token_embeddings(
          vocab_file, options_file, weight_file, token_embedding_file
      )
      logging.info("finish dump_token_embeddings")
      tf.reset_default_graph()
  
      with tf.Session(graph=tf.Graph()) as sess:
          bilm = BidirectionalLanguageModel(
              options_file,
              weight_file,
              use_character_inputs=False,
              embedding_weight_file=token_embedding_file,
          )
          input_x = tf.placeholder(
              tf.int32, shape=[None, None], name="input_x"
          )
          train_embeddings_op = bilm(input_x)
          input_x_elmo_op = weight_layers(
              "output", train_embeddings_op, l2_coef=0.0
          )["weighted_op"]
          input_x_elmo = tf.identity(
              input_x_elmo_op, name="input_x_elmo"
          )
          logging.info("input_x_elmo shape: {}".format(input_x_elmo))
          sess.run(tf.global_variables_initializer())
          saver = tf.train.Saver()
          saver.save(sess, output_elmo_model)
          logging.info("finish saving!")
  
          all_variables = tf.get_collection_ref(
              tf.GraphKeys.GLOBAL_VARIABLES
          )
          for v in all_variables:
              logging.info("variable name: {}".format(v.name))
  
imports: import sys
  import tensorflow
  from absl import logging
  from bilm.bilm import TokenBatcher, BidirectionalLanguageModel, weight_layers, dump_token_embeddings
label_desc: Use image node_id 1 for calling a global function with example usage: transfer_elmo_model(vocab_file, options_file, weight_file, token_embedding_file, output_elmo_model) without return types