code: def log_loss_eps(y_true, y_pred):
      ll = log_loss(y_true, y_pred)
      return ll
  
file_path: mljar-supervised/supervised/utils/importance.py
imports: import json
  import logging
  import os
  import warnings
  import matplotlib.pyplot
  import numpy
  import pandas
  from sklearn.inspection import permutation_importance
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.utils.subsample import subsample
  from supervised.utils.config import LOG_LEVEL
  import sys
  from sklearn.metrics import log_loss, make_scorer
label_desc: Use image node_id 1 for calling a global function with example usage: log_loss_eps(y_true, y_pred) and returns: ll