code: def test_eps(self):
      X, Y, A = _quick_data()
      eps = 0.01
      self.estimator = LinearRegression().fit(X, Y)
  
      def predictor(x):
          return self.estimator.predict(x)
  
      self.disparity_criterion = BoundedGroupLoss(
          SquareLoss(-np.inf, np.inf), upper_bound=eps
      )
      self.disparity_criterion.load_data(X, Y, sensitive_features=A)
      bnd = self.disparity_criterion.bound()
      loss_eps = self.disparity_criterion.gamma(predictor) - bnd
      loss = self.disparity_criterion.gamma(predictor)
      assert np.all(np.isclose(loss - eps, loss_eps))
  
file_path: fairlearn/test/unit/reductions/moments/test_moments_bounded_group_loss.py
imports: from test.unit.reductions.grid_search.utilities import _quick_data
  import numpy
  import pandas
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import train_test_split
  from fairlearn.reductions import BoundedGroupLoss, SquareLoss
label_desc: Use image node_id 1 for calling the TestBoundedGroupLoss obj's underlying member method code with example usage: obj.test_eps() and returns: self