code: def ot_distance(
      y_true: pd.Series,
      y_pred: Union[pd.Series, pd.DataFrame],
      prot_attr: pd.Series = None,
      pos_label: Union[str, float] = None,
      scoring: str = "Wasserstein1",
      num_iters: int = 1e5,
      penalty: float = 1e-17,
      mode: str = "binary",
      cost_matrix: np.ndarray = None,
      **kwargs,
  ):
      """Normalize and calculate Wasserstein distance between groups defined by `prot_attr` in `y_true` and `y_pred`.
  
      Args:
          y_true (pd.Series): ground truth (correct) target values.
          y_pred (pd.Series, pd.DataFrame): estimated target values.
              If `mode` is nominal, must be a `pd.DataFrame` with columns containing predictions for each nominal class,
                  or list of corresponding column names in `data`.
              If `None`, model is assumed to be a dummy model that predicts the mean of the targets
                  or 1/(number of categories) for nominal mode.
          prot_attr (pd.Series): sensitive attribute values.
              If `None`, assume all samples belong to the same protected group.
          pos_label(str, float, optional): Either "high", "low" or a float value if the mode in [binary, ordinal, or continuous].
                  If float, value has to be the minimum or the maximum in the ground_truth column.
                  Defaults to high if None for these modes.
                  Support for float left in to keep the intuition clear in binary classification tasks.
                  If `mode` is nominal, favorable values should be one of the unique categories in the ground_truth.
                  Defaults to a one-vs-all scan if None for nominal mode.
          scoring (str or class): only 'Wasserstein1'
          num_iters (int, optional): number of iterations (random restarts) for EMD. Should be positive.
          penalty (float, optional): penalty term. Should be positive. The penalty term as with any regularization parameter
              may need to be tuned for a particular use case. The higher the penalty, the higher the influence of entropy regualizer.
          mode: one of ['binary', 'continuous', 'nominal', 'ordinal']. Defaults to binary.
                  In nominal mode, up to 10 categories are supported by default.
                  To increase this, pass in keyword argument max_nominal = integer value.
          cost_matrix (np.ndarray): cost matrix for the Wasserstein distance. Defaults to absolute difference between samples.
  
      Returns:
          ot.emd2 (float, dict): Earth mover's distance or dictionary of optimal transports for each of option of classifier
  
      Raises:
          ValueError: if `mode` is 'binary' but `ground_truth` contains less than 1 or more than 2 unique values.
      """
      return ot_metric.ot_distance(
          ground_truth=y_true,
          classifier=y_pred,
          prot_attr=prot_attr,
          favorable_value=pos_label,
          scoring=scoring,
          num_iters=num_iters,
          penalty=penalty,
          mode=mode,
          cost_matrix=cost_matrix,
          **kwargs,
      )
  
imports: from itertools import permutations
  from typing import Union
  import warnings
  import numpy
  import pandas
  from scipy.special import rel_entr
  from sklearn.metrics import make_scorer, recall_score, precision_score
  from sklearn.metrics import multilabel_confusion_matrix
  from sklearn.metrics._classification import _prf_divide, _check_zero_division
  from sklearn.neighbors import NearestNeighbors
  from sklearn.utils import check_X_y
  from aif360.metrics import ot_metric
  from aif360.sklearn.utils import check_inputs, check_groups
  from aif360.detectors.mdss.ScoringFunctions import BerkJones, Bernoulli
  from aif360.detectors.mdss.MDSS import MDSS
label_desc: Use image node_id 16 for calling a global function with example usage: ot_distance(y_true, y_pred, prot_attr, pos_label, scoring, num_iters, penalty, mode, cost_matrix) and returns: ot_metric