input: def generate_natural_language_response(utterance: str) -> str:
      """Create a system in Python for generating natural language responses.
      The system is built using TensorFlow and Keras layers. It trains a model
      to generate responses to input utterances.
      >>> generate_natural_language_response("What time is it?")
      'The predicted response based on the trained model.'
      """
      from tensorflow.keras.layers import Input, Dense, Embedding, GRU
      from tensorflow.keras.models import Model
      from tensorflow.keras.utils import plot_model
  
      # Create inputs
      inputs1 = Input(shape=(20,))
      x1 = Embedding(1000, 64)(inputs1)
      x1 = GRU(128)(x1)
  
      # Create outputs
      y1 = Dense(1000, activation='softmax')(x1)
  
      # Create model
      model = Model(inputs=[inputs1], outputs=[y1])
  
      # Compile the model
      model.compile(optimizer='adam',
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])
  
      # Train the model
      model.fit([inputs], [outputs],  epochs=10, batch_size=32)
  
      # Generate the response to the sample utterance
      query = utterance
      prediction = model.predict(query)
  
      return prediction
output:     # Preprocess the input utterance
      query = np.array([[ord(char) for char in utterance]])
  
      # Generate the response
      response = model.predict(query)
  
      # Postprocess the response
      response_text = ''.join(chr(int(round(x))) for x in response[0])
  
      return response_text