input: def generate_natural_language_response(sample_utterance: str) -> str:
      """Generate a natural language response for the given sample utterance using a neural network model.
      
      >>> generate_natural_language_response("What time is it?")
      'The model prediction for the given sample utterance is printed.'
      """
      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 = sample_utterance
      prediction = model.predict(query)
  
      return "The model prediction for the given sample utterance is printed."
output:     # Assuming the model is already trained and ready to use
      # For demonstration purposes, we'll just return a placeholder response
      return "The model prediction for the given sample utterance is printed."