input: Write a function to create a system in Python for generating natural language responses. The function should build a neural network model using Keras to predict responses based on a given sample utterance. The model should include layers for embedding and GRU, and should be trained on provided inputs and outputs.
  from tensorflow.keras.layers import Input, Dense, Embedding, GRU
  from tensorflow.keras.models import Model
  from tensorflow.keras.utils import plot_model
  
  def create_response_generator_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'])
  
      return model
  
  model = create_response_generator_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 create_response_generator_model() is not None
  assert isinstance(create_response_generator_model(), Model)
  assert model.predict(np.array([[1]*20])) is not None
output: def create_response_generator_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