input: Write a function to generate natural language responses using a predefined model. The function should take a sample utterance as input and return the generated response as output.
  
  from tensorflow.keras.layers import Input, Dense, Embedding, GRU
  from tensorflow.keras.models import Model
  
  # Define the model architecture
  def create_model():
      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'])
      return model
  
  # Train the model with sample inputs and outputs
  def train_model(model, inputs, outputs):
      model.fit(inputs, outputs, epochs=10, batch_size=32)
      return model
  
  # Function to generate natural language response
  def generate_response(utterance, model):
      return model.predict(utterance)
  
  # Sample usage
  model = create_model()
  trained_model = train_model(model, [inputs], [outputs])
  response = generate_response("What time is it?", trained_model)
  print(response)
  
  assert generate_response("What time is it?", trained_model) is not None
  assert generate_response("Who are you?", trained_model) is not None
  assert generate_response("How are you?", trained_model) is not None
  assert generate_response("What is the weather today?", trained_model) is not None
  assert generate_response("Good morning!", trained_model) is not None
output: def generate_response(utterance, model):
      return model.predict(utterance)