input: def GenerateNaturalLanguageResponse(utterance):
      """Given a sample utterance, this function generates a natural language response using a pre-trained deep learning model.
      >>> GenerateNaturalLanguageResponse("What time is it?")
      '4 PM'
      >>> GenerateNaturalLanguageResponse("How are you?")
      'I am doing well, thank you.'
      """
      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('pretrained_model_weights.h5')
  
      # Preprocess the utterance
      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