code: def gini_normalized_c(target, prediction):
      # Below changed because targets/predictions passed as 2D arrays to metric functions
      if prediction.shape[1] == 2:
          prediction = prediction[
              :, 1
          ]  # If proba array contains both 0 and 1 classes, pick class 1
      if prediction.ndim == 2:  # Required for sklearn wrapper
          prediction = prediction[:, 0]
      if target.ndim == 2:  # Required for sklearn wrapper
          target = target[:, 0]
      return gini_c(target, prediction) / gini_c(target, target)
  
file_path: hyperparameter_hunter/examples/feature_engineering_examples/olivier_porto_seguro_kaggle.py
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  import numpy
  import pandas
  from sklearn.model_selection import StratifiedKFold
  from sklearn.preprocessing import LabelEncoder
  from xgboost import XGBClassifier
label_desc: Use image node_id 2 for calling a global function with example usage: gini_normalized_c(target, prediction) and returns: unknown