input: Write a function to create a natural language response generation system in Python using TensorFlow. The function should take a sample utterance as input and output the generated response. The function should include creating, compiling, and training a model, and then use the trained model to generate a response.
  
  from tensorflow.keras.layers import Input, Dense, Embedding, GRU
  from tensorflow.keras.models import Model
  from tensorflow.keras.utils import plot_model
  
  def generate_response_system(utterance):
      # 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
  
  # Test cases
  assert generate_response_system("What time is it?") == model.predict("What time is it?")
  assert generate_response_system("How are you?") == model.predict("How are you?")
  assert generate_response_system("Hello") == model.predict("Hello")
  assert generate_response_system("") == model.predict("")
  assert generate_response_system("Goodbye") == model.predict("Goodbye")
output: def generate_response_system(utterance):
      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)
      prediction = model.predict(utterance)
      return prediction