code: def test_single_y_class():
      # Setup with data designed to result in "all single class"
      # at some point in the grid
      X_dict = {
          "c": [0, 1, 4, 1, 5, 1, 6, 0, 2, 4],
          "d": [1, 5, 1, 6, 2, 3, 5, 1, 5, 2],
      }
      X = pd.DataFrame(X_dict)
  
      # Set y to a constant
      y_val = 1
      y = np.full(10, y_val)
      A = ["a", "a", "a", "a", "a", "a", "a", "a", "a", "b"]
  
      estimator = LogisticRegression(
          solver="liblinear", fit_intercept=True, random_state=97
      )
  
      grid_search = GridSearch(
          copy.deepcopy(estimator),
          constraints=DemographicParity(),
          grid_size=3,
          grid_limit=0.1,
          grid_offset=5,
      )
  
      # We want to avoid an exception on the following line
      grid_search.fit(X, y, sensitive_features=A)
  
      # Check all predictors are DummyClassifiers
      test_X_dict = {"c": [134534, 27381], "d": [1923, 14123]}
      test_X = pd.DataFrame(test_X_dict)
      for p in grid_search.predictors_:
          assert isinstance(p, DummyClassifier)
          assert np.array_equal(p.predict(test_X), [y_val, y_val])
  
file_path: fairlearn/test/unit/reductions/grid_search/test_grid_search_demographicparity.py
imports: import copy
  from test.unit.reductions.grid_search.utilities import assert_n_grid_search_results
  import numpy
  import pandas
  import pytest
  from sklearn.dummy import DummyClassifier
  from sklearn.linear_model import LogisticRegression
  from fairlearn.reductions import DemographicParity, GridSearch
label_desc: Use image node_id 6 for calling a global function with example usage: test_single_y_class() without return types