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:
      """Given a natural language utterance, use a pre-trained model to generate a response.
      >>> generate_response("What time is it?")
      'The time is 10:00 AM.'
      """
      # 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 = unicode(utterance)
      prediction = model.predict(query)
  
      return prediction
  
output:     
      # Load pre-trained weights (assuming they are saved as 'model_weights.h5')
      model.load_weights('model_weights.h5')
  
      # Preprocess the utterance (assuming it needs to be tokenized and padded)
      tokenized_utterance = tokenize(utterance)
      padded_utterance = pad_sequences([tokenized_utterance], maxlen=20)
  
      # Generate the response
      response = model.predict(padded_utterance)
      response_text = decode_response(response)
  
      return response_text
  