code: def test_simple_fit_predict_binary_classification(self, Constraints):
      X, y, sensitive_features = _get_data()
      estimator = LeastSquaresBinaryClassifierLearner()
      expgrad = ExponentiatedGradient(estimator, Constraints())
      expgrad.fit(X, y, sensitive_features=sensitive_features)
      expgrad.predict(X)
  
imports: from copy import deepcopy
  import numpy
  import pandas
  import pytest
  from sklearn.dummy import DummyClassifier
  from sklearn.linear_model import LogisticRegression
  from fairlearn.reductions import AbsoluteLoss, BoundedGroupLoss, DemographicParity, EqualizedOdds, ErrorRate, ErrorRateParity, ExponentiatedGradient, FalsePositiveRateParity, SquareLoss, TruePositiveRateParity, ZeroOneLoss
  from fairlearn.reductions._exponentiated_gradient._constants import _MIN_ITER
  from fairlearn.reductions._moments.bounded_group_loss import MeanLoss
  from .simple_learners import LeastSquaresBinaryClassifierLearner, LeastSquaresRegressor
  from .test_utilities import _get_data
label_desc: Use image node_id 5 for calling the TestExponentiatedGradientSmoke obj's underlying member method code with example usage: obj.test_simple_fit_predict_binary_classification(Constraints) without return types