code: def create_graph(
      model_location,
      ntags,
      embeddings_dim,
      nchars,
      lstm_size=128,
      model_filename=None,
      gpu_device=0,
      is_medical=False,
  ):
      tf.disable_v2_behavior()
      tf.reset_default_graph()
  
      if model_filename is None:
          model_filename = (
              "blstm"
              + "_{}_{}_{}_{}".format(
                  ntags, embeddings_dim, lstm_size, nchars
              )
              + ".pb"
          )
  
      with tf.Session() as session:
          ner = NerModel(session=None, use_gpu_device=gpu_device)
          ner.add_cnn_char_repr(nchars, 25, 30)
          ner.add_bilstm_char_repr(nchars, 25, 30)
          ner.add_pretrained_word_embeddings(embeddings_dim)
          ner.add_context_repr(ntags, lstm_size, 3)
          ner.add_inference_layer(
              True, "predictions" if is_medical else None
          )
          ner.add_training_op(5, "train" if is_medical else None)
          ner.init_variables()
          saver = tf.train.Saver()
          tf.io.write_graph(
              ner.session.graph, model_location, model_filename, False
          )
          ner.close()
          session.close()
  
label_desc: Use image node_id 1 for calling a global function with example usage: create_graph(model_location, ntags, embeddings_dim, nchars, lstm_size, model_filename, gpu_device, is_medical) without return types