input: Write a function to create a natural language response generation system using TensorFlow/Keras. The function should take a sample utterance as input, generate a response using the trained model, and return the predicted response. The model should be trained to minimize categorical cross-entropy loss and maximize accuracy. If the input query is invalid, return an empty string.
  
  def generate_response(utterance):
      from tensorflow.keras.layers import Input, Dense, Embedding, GRU
      from tensorflow.keras.models import Model
      from tensorflow.keras.utils import plot_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
      if isinstance(utterance, str) and utterance.strip() != "":
          prediction = model.predict(utterance)
          return prediction
      else:
          return ""
  
  assert generate_response("What time is it?") # expected output: a tensor with predicted probabilities
  assert generate_response("") == "" # expected output: an empty string
  assert generate_response(None) == "" # expected output: an empty string
output: 
  def generate_response(utterance):
      from tensorflow.keras.models import load_model
      model = load_model('response_model.h5')
      if isinstance(utterance, str) and utterance.strip() != "":
          prediction = model.predict([utterance])
          return prediction
      else:
          return ""
  