code: def postprocess(prediction, texts):
      result = []
      pred = prediction.as_ndarray()
      for index in range(len(texts)):
          result_i = {}
          result_i["text"] = texts[index]["origin"]
          label, key = get_predict_label(pred[index])
          result_i["emotion_label"] = label
          result_i["emotion_key"] = key
          result_i["positive_probs"] = float("%.4f" % pred[index, 2])
          result_i["negative_probs"] = float("%.4f" % (pred[index, 0]))
          result_i["neutral_probs"] = float("%.4f" % (pred[index, 1]))
          result.append(result_i)
      return result
  
imports: import io
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: postprocess(prediction, texts) and returns: result