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_natural_language_response(utterance: str) -> str:
      """Generate a natural language response based on the given utterance.
      >>> generate_natural_language_response("What time is it?")
      'The time is 3:00 PM.'
      """
      # 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
      prediction = model.predict(utterance)
  
      return prediction
  
output:     # Preprocess the utterance
      utterance_vector = np.array([[ord(char) for char in utterance]])
  
      # Generate the response
      response_vector = model.predict(utterance_vector)
      response_indices = np.argmax(response_vector, axis=2)
      response_chars = [chr(idx) for idx in response_indices[0]]
      response = ''.join(response_chars)
  
      return response