input: Write a function to create a natural language response generation system in Python. The function should define a model using TensorFlow's Keras API, compile it, train it with sample inputs, and generate a natural language response for a given query. The function should return the generated response in the form of a prediction.
  def generate_response(query):
      from tensorflow.keras.layers import Input, Dense, Embedding, GRU
      from tensorflow.keras.models import 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(query)
      
      return prediction
  
  assert generate_response("What time is it?") == "Expected prediction based on trained model"
output: def generate_response(query):
      from tensorflow.keras.layers import Input, Dense, Embedding, GRU
      from tensorflow.keras.models import 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
      inputs = np.random.randint(0, 1000, size=(100, 20))
      outputs = np.eye(1000)[np.random.choice(1000, 100)]
      model.fit(inputs, outputs, epochs=10, batch_size=32)
      
      # Generate the response to the sample utterance
      prediction = model.predict(np.array([query]))
      
      return prediction