code: def loss(self, y, y_pred):
      y_pred = np.clip(y_pred, 1e-15, 1 - 1e-15)
      p = self.log_func(y_pred)
      return y * np.log(p) + (1 - y) * np.log(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 2 for calling the LogisticLoss obj's underlying member method code with example usage: obj.loss(y, y_pred) and returns: unknown