code: def create_graph(
      output_path,
      number_of_tags,
      embeddings_dimension,
      number_of_chars,
      lstm_size=128,
  ):
      if sys.version_info[0] != 3 or sys.version_info[1] >= 7:
          raise Exception(
              "Python 3.7 or above not supported by TensorFlow"
          )
      if tf.__version__ != "1.15.0":
          error_message = (
              "Spark NLP is compiled with TensorFlow 1.15.0. Please use such version and not "
              + tf.__version__
          )
          raise Exception(error_message)
      tf.reset_default_graph()
      name_prefix = "blstm"
      model_name = name_prefix + "_{}_{}_{}_{}".format(
          number_of_tags,
          embeddings_dimension,
          lstm_size,
          number_of_chars,
      )
      with tf.Session() as session:
          ner = ner_model.NerModel(session=None)
          ner.add_cnn_char_repr(number_of_chars, 25, 30)
          ner.add_bilstm_char_repr(number_of_chars, 25, 30)
          ner.add_pretrained_word_embeddings(embeddings_dimension)
          ner.add_context_repr(number_of_tags, lstm_size, 3)
          ner.add_inference_layer(True)
          ner.add_training_op(5)
          ner.init_variables()
          tf.train.Saver()
          file_name = model_name + ".pb"
          tf.train.write_graph(
              ner.session.graph, output_path, file_name, False
          )
          ner.close()
          session.close()
          print("Graph created successfully")
  
imports: import os
  import sys
  import tensorflow
  import ner_model
  import argparse
label_desc: Use image node_id 1 for calling a global function with example usage: create_graph(output_path, number_of_tags, embeddings_dimension, number_of_chars, lstm_size) without return types