input: 
  from tensorflow.keras.layers import Input, Dense, Embedding, GRU
  from tensorflow.keras.models import Model
  from tensorflow.keras.utils import plot_model
  
  def generate_response(utterance: str) -> str:
      """Generate a natural language response based on a given utterance.
  
      >>> generate_response("What time is it?")
      'The time is 10:30 AM.'
      >>> generate_response("How are you?")
      'I am doing well, thank you.'
      """
      inputs1 = Input(shape=(20,))
      x1 = Embedding(1000, 64)(inputs1)
      x1 = GRU(128)(x1)
      y1 = Dense(1000, activation='softmax')(x1)
      model = Model(inputs=[inputs1], outputs=[y1])
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      model.fit([inputs], [outputs], epochs=10, batch_size=32)
      prediction = model.predict([inputs])
      return prediction
  
output:     inputs = np.array([tokenizer.texts_to_sequences([utterance])])
      outputs = np.zeros((1, 1000))
      prediction = model.predict(inputs)
      predicted_index = np.argmax(prediction)
      response = tokenizer.sequences_to_texts([[predicted_index]])[0]
      return response