code: def _mae_normalizer(df, y, model_score, **kwargs):
      """In case of MAE, calculates the baseline score for y and derives the PPS."""
      df["naive"] = df[y].median()
      baseline_score = mean_absolute_error(
          df[y], df["naive"]
      )  # true, pred
  
      ppscore = _normalized_mae_score(abs(model_score), baseline_score)
      return ppscore, baseline_score
  
imports: import warnings
  import numpy
  import pandas
  from pandas.api.types import is_bool_dtype, is_categorical_dtype, is_datetime64_any_dtype, is_numeric_dtype, is_object_dtype, is_string_dtype, is_timedelta64_dtype
  from sklearn import preprocessing, tree
  from sklearn.metrics import f1_score, mean_absolute_error
  from sklearn.model_selection import cross_val_score
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 3 for calling a global function with example usage: _mae_normalizer(df, y, model_score) and returns: ppscore,  baseline_score