input: Write a function to create a system in Python for generating natural language responses using a deep learning model. The function should take a sample utterance as input and return the predicted response. The function should use TensorFlow/Keras to define the model architecture, compile the model, train it, and generate the response.
  
  def generate_response(sample_utterance):
      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 (assume inputs and outputs are defined)
      inputs = [...]  # Placeholder for actual inputs
      outputs = [...]  # Placeholder for actual outputs
      model.fit([inputs], [outputs], epochs=10, batch_size=32)
  
      # Generate the response to the sample utterance
      prediction = model.predict(sample_utterance)
  
      return prediction
  
  assert generate_response("What time is it?") == ["The predicted response"]
  assert generate_response("Hello, how are you?") == ["Another predicted response"]
  assert generate_response("Tell me a joke.") == ["Yet another predicted response"]
output: def generate_response(sample_utterance):
      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 (assume inputs and outputs are defined)
      inputs = [...]  # Placeholder for actual inputs
      outputs = [...]  # Placeholder for actual outputs
      model.fit([inputs], [outputs], epochs=10, batch_size=32)
  
      # Generate the response to the sample utterance
      prediction = model.predict(sample_utterance)
  
      return prediction