input: 
  def generate_natural_language_response(input_utterance: str) -> str:
      """Given an input utterance, this function generates a natural language 
      response using a pre-trained model.
      >>> generate_natural_language_response("What time is it?")
      'The time is 3:45 PM.'
      >>> generate_natural_language_response("How are you?")
      'I am doing well, thank you for asking.'
      """
  
      from tensorflow.keras.layers import Input, Dense, Embedding, GRU
      from tensorflow.keras.models import 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 input utterance
      prediction = model.predict(input_utterance)
  
      return prediction
  
output:     # Load pre-trained model weights
      model.load_weights('pretrained_model.h5')
  
      # Tokenize the input utterance
      tokenized_input = tokenize(input_utterance)
  
      # Convert tokenized input to model input format
      model_input = np.array([tokenized_input])
  
      # Generate the response
      response = model.predict(model_input)
  
      # Decode the response to natural language
      decoded_response = decode_response(response)
  
      return decoded_response