code: def create_tf_gru_model():
      tf.compat.v1.disable_eager_execution()
  
      # Create a test rnn network based on Pytorch doc
      model = keras.Sequential()
      model.add(layers.Embedding(input_dim=1000, output_dim=64))
      # The output of GRU will be a 3D tensor of shape (batch_size, timesteps, 256)
      model.add(layers.GRU(256, return_sequences=True))
      # The output of SimpleRNN will be a 2D tensor of shape (batch_size, 128)
      model.add(layers.SimpleRNN(128))
      model.add(layers.Dense(10))
  
      # save keras model
      model.save("test_models/gru_TF1.h5")
      # save TF savedmodel
      model.save("test_models/gru_savedmodel", save_format="tf")
      # print model input/output names
      print("gru model input name:", model.input)
      print("gru 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 4 for calling a global function with example usage: create_tf_gru_model() without return types