code: def _normalized_mae_score(model_mae, naive_mae):
      """Normalize the model MAE score, given the baseline score."""
      # # Value range of MAE is [0, infinity), 0 is best
      # 10, 5 ==> 0 because worse than naive
      # 10, 20 ==> 0.5
      # 5, 20 ==> 0.75 = 1 - (mae/base_mae)
      if model_mae > naive_mae:
          return 0
      else:
          return 1 - (model_mae / naive_mae)
  
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 2 for calling a global function with example usage: _normalized_mae_score(model_mae, naive_mae) and returns: int, unknown