input: 
  def generate_response(utterance: str) -> str:
      """ Generate a natural language response based on the given utterance using a machine learning model.
      >>> generate_response("What time is it?")
      'The time is 3:00 PM.'
      """
      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
      prediction = model.predict(utterance)
      
      return prediction
  
output:     
      # Load pre-trained model weights
      model.load_weights('response_model_weights.h5')
      
      # Preprocess the utterance
      utterance_vector = preprocess_utterance(utterance)
      
      # Generate the response
      response = model.predict(utterance_vector)
      
      # Postprocess the response
      response_text = postprocess_response(response)
      
      return response_text
  