docstr: Fits the fairlearn classifier to the data.
  
  Parameters
  ----------
  X : pd.DataFrame
      The input data.
  y : pd.Series
      The target values.
  s : pd.Series
      The protected attribute.
docstr_tok: ["Fits","the","fairlearn","classifier","to","the","data",".","Parameters","--","--","--","--","--","X",":","pd.DataFrame","The","input","data",".","y",":","pd.Series","The","target","values",".","s",":","pd.Series","The","protected","attribute","."]
code: def fit(self, X: pd.DataFrame, y: pd.Series, s: pd.Series):
      """Fits the fairlearn classifier to the data.
  
      Parameters
      ----------
      X : pd.DataFrame
          The input data.
      y : pd.Series
          The target values.
      s : pd.Series
          The protected attribute.
      """
      if isinstance(self.reduction, GridSearch):
          self._generate_grid(X, y, s)
      return self.reduction.fit(X, y, sensitive_features=s)
  
file_path: aequitas/src/aequitas/fairflow/methods/inprocessing/fairlearn_classifier.py
imports: from copy import deepcopy
  from typing import Any, Callable, Optional, Union
  import numpy
  import pandas
  from fairlearn.reductions import ExponentiatedGradient, GridSearch
  from fairlearn.reductions._grid_search._grid_generator import _GridGenerator
  from ...utils import create_logger, import_object
  from .inprocessing import InProcessing
label_desc: Use image node_id 2 for calling the FairlearnClassifier obj's underlying member method code with example usage: obj.fit(X, y, s) and returns: self