input: Write a function to create a natural language response generation system in Python. The function should define and train a TensorFlow model using the Keras API, compile it, and generate a response to a sample utterance.
  
  from tensorflow.keras.layers import Input, Dense, Embedding, GRU
  from tensorflow.keras.models import Model
  
  def generate_response_system():
      # 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 = "What time is it?"
      prediction = model.predict(query)
  
      print(prediction)
  
  assert generate_response_system() is not None
  assert "What time is it?" in generate_response_system().__code__.co_consts
output: def generate_response_system():
      inputs1 = Input(shape=(20,))
      x1 = Embedding(1000, 64)(inputs1)
      x1 = GRU(128)(x1)
      y1 = Dense(1000, activation='softmax')(x1)
      model = Model(inputs=[inputs1], outputs=[y1])
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      model.fit([inputs], [outputs], epochs=10, batch_size=32)
      query = "What time is it?"
      prediction = model.predict(query)
      return prediction