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, generate a response using a trained model.
      >>> generate_response("What time is it?")
      'The time is currently [time of day].'
      >>> generate_response("How are you?")
      'I am doing well, thank you for asking!'
      """
      # 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:     # Load pre-trained weights (assuming they are available)
      model.load_weights('model_weights.h5')
  
      # Tokenize the input utterance
      tokenized_utterance = tokenize(utterance)
  
      # Pad or truncate the tokenized utterance to a fixed length
      padded_utterance = pad_sequences([tokenized_utterance], maxlen=20)
  
      # Predict the response
      predicted_response = model.predict(padded_utterance)
  
      # Decode the predicted response
      response = decode_response(predicted_response)
  
      return response