input: Write a function to create and train a natural language processing model using TensorFlow and Keras that generates responses to given utterances. The function should compile and train the model using provided input and output data, and then predict the response to a sample utterance.
  def generate_response(inputs, outputs, 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 query
      prediction = model.predict([query])
      return prediction[0]
  
  assert generate_response([[0] * 20], [[1] * 1000], [0] * 20) != None
  assert generate_response([[0] * 20], [[1] * 1000], ["What time is it?"]) != None
output: def generate_response(inputs, outputs, 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 query
      prediction = model.predict([query])
      return prediction[0]