code: def test_single_y_value(self):
      # Setup with data designed to result in "all single class"
      # at some point in the grid
      X_dict = {"c": [10, 50, 10]}
      X = pd.DataFrame(X_dict)
  
      y = [1, 1, 1]
      A = ["a", "b", "b"]
  
      estimator = LogisticRegression(
          solver="liblinear", fit_intercept=True, random_state=97
      )
      expgrad = ExponentiatedGradient(estimator, DemographicParity())
  
      # Following line should not throw an exception
      expgrad.fit(X, y, sensitive_features=A)
  
      # Check the predictors for a ConstantPredictor
      test_X_dict = {"c": [1, 2, 3, 4, 5, 6]}
      test_X = pd.DataFrame(test_X_dict)
      assert expgrad.n_oracle_calls_dummy_returned_ > 0
      assert (
          len(expgrad.oracle_execution_times_)
          == expgrad.n_oracle_calls_
      )
      for p in expgrad.predictors_:
          assert isinstance(p, DummyClassifier)
          assert np.array_equal(p.predict(test_X), [1, 1, 1, 1, 1, 1])
  
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 7 for calling the TestExponentiatedGradientSmoke obj's underlying member method code with example usage: obj.test_single_y_value() without return types