input: Write a function to generate natural language responses using a deep learning model. The function should take a sample utterance as input and return the predicted response from the model. If the input is empty, the function should return an empty string.
  
  
  def generate_response(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 (assuming the model is already trained and weights are loaded)
  
      # Predict the response
      if not utterance:
          return ""
      prediction = model.predict(utterance)
      
      # Return the predicted response
      return prediction
  
  # Test cases
  assert generate_response("What time is it?") != ""  # Should return a non-empty response
  assert generate_response("") == ""  # Should return an empty string
  
output: def generate_response(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'])
  
      # Predict the response
      if not utterance:
          return ""
      prediction = model.predict(utterance)
      
      # Return the predicted response
      return prediction