code: def predict_fn(data, model_and_tokenizer):
      # unpack model and tokenizer
      model, tokenizer = model_and_tokenizer
  
      # process input
      inputs = data.pop("inputs", data)
      parameters = data.pop("parameters", None)
  
      # preprocess
      input_ids = tokenizer(inputs, return_tensors="pt").input_ids.to(
          model.device
      )
  
      # pass inputs with all kwargs in data
      if parameters is not None:
          outputs = model.generate(input_ids, **parameters)
      else:
          outputs = model.generate(input_ids)
  
      # postprocess the prediction
      prediction = tokenizer.decode(
          outputs[0], skip_special_tokens=True
      )
  
      return [{"generated_text": prediction}]
  
label_desc: Use image node_id 2 for calling a global function with example usage: predict_fn(data, model_and_tokenizer) and returns: list