input: def generate_natural_response(utterance: str) -> str:
      """
      Generate a natural language response using a trained model. The model is trained to 
      understand and respond to specific utterances.
      
      >>> generate_natural_response("What time is it?")
      'Current time is 12:00 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
      query = utterance
      prediction = model.predict(query)
  
      return prediction
output:     # Load pre-trained model weights
      model.load_weights('model_weights.h5')
  
      # Preprocess the input utterance
      query = preprocess_input(utterance)
  
      # Generate the response
      prediction = model.predict(query)
      response = decode_response(prediction)
  
      return response