input: Write a function to create a natural language response generation system in Python using TensorFlow and Keras. The function should take a string utterance as input and return the generated response. The system should use an Embedding layer, a GRU layer, and a Dense layer to generate the 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(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
      query = utterance
      prediction = model.predict(query)
  
      return prediction
  
  assert generate_response("What time is it?") is not None
  assert generate_response("Tell me a joke.") is not None
  assert generate_response("How are you?") is not None
  assert generate_response("What is your name?") is not None
  assert generate_response("Goodbye.") is not None
output: 
  def generate_response(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
      query = utterance
      prediction = model.predict(query)
  
      return prediction
  