code: def predict_fn(data, model_and_processor):
      # unpack model and tokenizer
      model, processor = model_and_processor
  
      image = data.get("inputs")
      pixel_values = processor.feature_extractor(
          image, return_tensors="pt"
      ).pixel_values
      task_prompt = "<s>"  # start of sequence token for decoder since we are not having a user prompt
      decoder_input_ids = processor.tokenizer(
          task_prompt, add_special_tokens=False, return_tensors="pt"
      ).input_ids
  
      # run inference
      outputs = model.generate(
          pixel_values.to(device),
          decoder_input_ids=decoder_input_ids.to(device),
          max_length=model.decoder.config.max_position_embeddings,
          early_stopping=True,
          pad_token_id=processor.tokenizer.pad_token_id,
          eos_token_id=processor.tokenizer.eos_token_id,
          use_cache=True,
          num_beams=1,
          bad_words_ids=[[processor.tokenizer.unk_token_id]],
          return_dict_in_generate=True,
      )
  
      # process output
      prediction = processor.batch_decode(outputs.sequences)[0]
      prediction = processor.token2json(prediction)
  
      return prediction
  
label_desc: Use image node_id 2 for calling a global function with example usage: predict_fn(data, model_and_processor) and returns: prediction