input: 
  def process_query(utterance: str) -> str:
      """Generate a natural language response based on the given utterance using a pre-trained model.
      >>> process_query("What time is it?")
      "[Time prediction based on the model]"
      >>> process_query("How's the weather today?")
      "[Weather prediction based on the model]"
      """
      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:     
      # Preprocess the utterance
      utterance_vector = np.array([[ord(char) for char in utterance]])
  
      # Predict the response
      response = model.predict(utterance_vector)
  
      # Decode the response
      response_text = "".join([chr(int(round(num))) for num in response[0]])
  
      return response_text
  