code: def hess(self, y, y_pred):
      p = self.log_func(y_pred)
      return p * (1 - p)
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/xgboost.py
imports: from __future__ import division, print_function
  import numpy
  import progressbar
  from mlfromscratch.utils import train_test_split, standardize, to_categorical, normalize
  from mlfromscratch.utils import mean_squared_error, accuracy_score
  from mlfromscratch.supervised_learning import XGBoostRegressionTree
  from mlfromscratch.deep_learning.activation_functions import Sigmoid
  from mlfromscratch.utils.misc import bar_widgets
  from mlfromscratch.utils import Plot
label_desc: Use image node_id 4 for calling the LogisticLoss obj's underlying member method code with example usage: obj.hess(y, y_pred) and returns: unknown