code: def compute_hess(gh: DataFrame, y, score):
      gh["h"] = score.apply_row(
          lambda s: [[2 * i * (1 - i) for i in s["score"]]]
      )
  
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 6 for calling the CELoss obj's underlying member method code with example usage: obj.compute_hess(gh, y, score) without return types