code: def auc_to_drift_score(auc: float) -> float:
      """Calculate the drift score, which is 2*auc - 1, with auc being the auc of the Domain Classifier.
  
      Parameters
      ----------
      auc : float
          auc of the Domain Classifier
      """
      return max(2 * auc - 1, 0)
  
imports: import warnings
  from typing import Container, List, Tuple
  import pandas
  import plotly.graph_objects
  from sklearn.ensemble import HistGradientBoostingClassifier
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import OrdinalEncoder
  from deepchecks.tabular import Dataset
  from deepchecks.tabular.utils.feature_importance import N_TOP_MESSAGE, calculate_feature_importance_or_none
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.dataframes import floatify_dataframe
  from deepchecks.utils.distribution.drift import get_drift_plot_sidenote
  from deepchecks.utils.distribution.plot import drift_score_bar_traces, feature_distribution_traces
  from deepchecks.utils.distribution.rare_category_encoder import RareCategoryEncoder
  from deepchecks.utils.plot import DEFAULT_DATASET_NAMES
  from deepchecks.utils.strings import format_percent
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 2 for calling a global function with example usage: auc_to_drift_score(auc) and returns: max