class_docstr: Abstract class for a wrapper of a machine learning 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.
docstr: Inplace update of X with s. Only performed if unawareness is False.
code: def _update_X(self, X, s):
      """Inplace update of X with s. Only performed if unawareness is False."""
      if not self._unawareness:
          if s is None:
              raise ValueError("s must be passed.")
          X[s.name] = s
  
file_path: aequitas/src/aequitas/fairflow/methods/base_estimator/base_estimator.py
imports: from abc import ABC, abstractmethod
  from typing import Optional
  import pandas
label_desc: Use image node_id 4 for calling the BaseEstimator obj's underlying member method code with example usage: obj._update_X(X, s) without return types