class_docstr: Abstract class for a wrapper of a postprocessing technique.
  
  Methods
  -------
  fit(X, y_hat, y, s)
      Fit the post-processing technique to the validation data.
  
      Parameters
      ----------
      X : pandas.DataFrame
          The input data.
      y_hat : pandas.Series
          The pre-transformed predictions.
      y : pandas.Series
          The target values.
      s : pandas.Series, optional
          The protected attribute.
  
  transform(X, y_hat, s)
      Transform the predictions according to the technique.
  
      Parameters
      ----------
      X : numpy.ndarray
          The input data.
      y_hat : pandas.Series
          The pre-transformed predictions.
      s : pandas.Series, optional
          The protected attribute.
  
      Returns
      -------
      pandas.Series
          The transformed predicted values.
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docstr: Fit the post-processing technique to the validation data.
  
  Parameters
  ----------
  X : pandas.DataFrame
      The input data.
  y_hat : pandas.Series
      The pre-transformed predictions.
  y : pandas.Series
      The target values.
  s : pandas.Series, optional
      The protected attribute.
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code: def fit(
      self,
      X: pd.DataFrame,
      y_hat: pd.Series,
      y: pd.Series,
      s: Optional[pd.Series] = None,
  ) -> None:
      """
      Fit the post-processing technique to the validation data.
  
      Parameters
      ----------
      X : pandas.DataFrame
          The input data.
      y_hat : pandas.Series
          The pre-transformed predictions.
      y : pandas.Series
          The target values.
      s : pandas.Series, optional
          The protected attribute.
      """
      pass
  
imports: from abc import ABC, abstractmethod
  from typing import Optional
  import pandas
label_desc: Use image node_id 1 for calling the PostProcessing obj's underlying member method code with example usage: obj.fit(X, y_hat, y, s) without return types