code: def binary_error(preds, train_data):
      labels = train_data.get_label()
      preds = 1.0 / (1.0 + np.exp(-preds))
      return "error", np.mean(labels != (preds > 0.5)), False
  
file_path: LightGBM/examples/python-guide/advanced_example.py
imports: import copy
  import json
  import pickle
  from pathlib import Path
  import numpy
  import pandas
  from sklearn.metrics import roc_auc_score
  import lightgbm
label_desc: Use image node_id 2 for calling a global function with example usage: binary_error(preds, train_data) and returns: str,  np,  False