code: def test_demographicparity_fair_uneven_populations(A_two_dim):
      # Variant of test_demographicparity_already_fair, which has unequal
      # populations in the two classes
      # Also allow the threshold to be adjustable
  
      score_threshold = 0.625
  
      number_a0 = 4
      number_a1 = 4
  
      a0_label = 17
      a1_label = 37
  
      grid_size = 11
  
      X, Y, A = _simple_threshold_data(
          number_a0,
          number_a1,
          score_threshold,
          score_threshold,
          a0_label,
          a1_label,
          A_two_dim,
      )
  
      grid_search = GridSearch(
          LogisticRegression(solver="liblinear", fit_intercept=True),
          constraints=DemographicParity(),
          grid_size=grid_size,
      )
  
      grid_search.fit(X, Y, sensitive_features=A)
      assert_n_grid_search_results(grid_size, grid_search)
  
      test_X = pd.DataFrame(
          {
              "actual_feature": [0.2, 0.7],
              "sensitive_features": [a0_label, a1_label],
              "constant_ones_feature": [1, 1],
          }
      )
  
      sample_results = grid_search.predict(test_X)
      assert np.array_equal(sample_results, [0, 1])
  
      sample_proba = grid_search.predict_proba(test_X)
      assert np.allclose(
          sample_proba,
          [[0.53748641, 0.46251359], [0.46688736, 0.53311264]],
      )
  
      sample_results = grid_search.predictors_[0].predict(test_X)
      assert np.array_equal(sample_results, [1, 0])
  
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 2 for calling a global function with example usage: test_demographicparity_fair_uneven_populations(A_two_dim) without return types