class_docstr: Abstract class for a wrapper of an in-processing FairML model.
  
  Methods
  -------
  fit(X, y, s)
      Train the machine learning model on the provided data.
  
      Parameters
      ----------
      X : pandas.DataFrame
          The input data.
      y : pandas.Series
          The target values.
      s : pandas.Series, optional
          The protected attribute.
  
  predict_proba(X, s)
      Use the machine learning model to make predictions on new data.
  
      Parameters
      ----------
      X : numpy.ndarray
          The input data.
      s : pandas.Series, optional
          The protected attribute.
  
      Returns
      -------
      pandas.Series
          The predicted values.
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docstr: Use the machine learning model to make predictions on new data.
  
  Parameters
  ----------
  X : pandas.DataFrame
      The input data.
  s : pandas.Series, optional
      The protected attribute.
  Returns
  -------
  pandas.Series
      The predicted values.
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code: def predict_proba(
      self, X: pd.DataFrame, s: Optional[pd.Series] = None
  ) -> pd.Series:
      """
      Use the machine learning model to make predictions on new data.
  
      Parameters
      ----------
      X : pandas.DataFrame
          The input data.
      s : pandas.Series, optional
          The protected attribute.
      Returns
      -------
      pandas.Series
          The predicted values.
      """
      pass
  
imports: from abc import ABC, abstractmethod
  from typing import Optional
  import pandas
label_desc: Use image node_id 2 for calling the InProcessing obj's underlying member method code with example usage: obj.predict_proba(X, s) without return types