code: def predict(score: DataFrame):
      def softmax(s):
          s = np.array(s["score"]).astype(np.float64)
          ret = (np.exp(s) / np.exp(s).sum()).tolist()
          return [ret]
  
      pred_rs = score.create_frame()
      pred_rs["score"] = score.apply_row(lambda s: softmax(s))
      return pred_rs
  
imports: import numpy
  import pandas
  import torch
  from fate.arch.dataframe import DataFrame
  from scipy.special import expit
  from fate.ml.utils.predict_tools import BINARY, MULTI, REGRESSION
label_desc: Use image node_id 3 for calling the CELoss obj's underlying member method code with example usage: obj.predict(score) and returns: pred_rs, list