code: def create_tf_lstm_model():
      tf.compat.v1.disable_eager_execution()
  
      # Create a test lstm network based on Pytorch doc
      model = keras.Sequential()
      # Add an Embedding layer expecting input vocab of size 1000, and
      # output embedding dimension of size 64.
      model.add(layers.Embedding(input_dim=1000, output_dim=64))
      model.add(layers.LSTM(128))
      model.add(layers.Dense(10))
  
      # save checkpoint
      model.save_weights("test_models/lstm_ckpt/ckpt")
      ckpt_meta = "test_models/lstm_ckpt/ckpt.meta"
      tf.compat.v1.train.export_meta_graph(filename=ckpt_meta)
  
      # save keras model
      model.save("test_models/lstm.h5")
  
      # save TF savedmodel
      model.save("test_models/lstm_savedmodel", save_format="tf")
  
      # print model input/output names
      print("lstm model input name:", model.input)
      print("lstm model output name:", model.output)
  
imports: import torchvision.models
  import torch
  import torch.nn
  import tensorflow.keras.applications
  import tensorflow
  from tensorflow import keras
  from tensorflow.keras import layers
  from tensorflow.python.util import deprecation
  import string
label_desc: Use image node_id 3 for calling a global function with example usage: create_tf_lstm_model() without return types