text: import numpy as np
  import warnings
  warnings.filterwarnings("ignore")
  import tensorflow as tf
  from tensorflow import keras
  tf.compat.v1.enable_eager_execution()
  import matplotlib.pyplot as plt
  from model_utils import save_model
  
  # construct TensorFlow model
  model = keras.Sequential()
  model.add(keras.layers.InputLayer(input_shape=(None, 1)))
  model.add(keras.layers.GRU (8, activation="tanh", return_sequences=True, recurrent_activation="sigmoid", use_bias=True, kernel_initializer="glorot_uniform", recurrent_initializer="orthogonal", bias_initializer="random_normal",))
  model.add(keras.layers.Dense(8, activation='sigmoid', kernel_initializer=tf.keras.initializers.Orthogonal(), bias_initializer='random_normal'))
  model.add(keras.layers.Dense(1, kernel_initializer=tf.keras.initializers.Orthogonal(), bias_initializer='random_normal'))
  
  # construct signals
  N = 100
  x = 10 * np.sin(np.arange(N) * np.pi * 0.1)
  y = model.predict((x.reshape((1, -1, 1))))
  y = y.flatten()
  
  # plot signals
  plt.figure()
  plt.plot(x)
  plt.plot(y, '--')
  plt.ylim(-0.5, 0.5)
  plt.savefig('python/gru_1d.png')
  
  # save signals
  np.savetxt('test_data/gru_1d_x_python.csv', x, delimiter=',')
  np.savetxt('test_data/gru_1d_y_python.csv', y, delimiter=',')
  
  save_model(model, 'models/gru_1d.json')
  