code: def rae(y_true, y_pred):
      return (
          "RAE",
          np.sum(np.abs(y_pred - y_true))
          / np.sum(np.abs(np.mean(y_true) - y_true)),
          False,
      )
  
file_path: LightGBM/examples/python-guide/sklearn_example.py
imports: from pathlib import Path
  import numpy
  import pandas
  from sklearn.metrics import mean_squared_error
  from sklearn.model_selection import GridSearchCV
  import lightgbm
label_desc: Use image node_id 2 for calling a global function with example usage: rae(y_true, y_pred) and returns: str,  unknown,  False