code: def test_bgl_unfair(A_two_dim):
      a0_count = 5
      a1_count = 7
  
      a0_label = 2
      a1_label = 3
  
      a0_factor = 1
      a1_factor = 16
  
      grid_size = 7
  
      X, Y, A = _simple_regression_data(
          a0_count,
          a1_count,
          a0_factor,
          a1_factor,
          a0_label,
          a1_label,
          A_two_dim,
      )
  
      bgl_square_loss = BoundedGroupLoss(SquareLoss(-np.inf, np.inf))
      grid_search = GridSearch(
          LinearRegression(),
          constraints=bgl_square_loss,
          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],
          }
      )
  
      best_predict = grid_search.predict(test_X)
      assert np.allclose([-1.91764706, 9.61176471], best_predict)
  
      all_predict = [
          predictor.predict(test_X)
          for predictor in grid_search.predictors_
      ]
  
      # TODO: investigate where the different outcomes for the first grid point are from, likely
      # due to some ignored data points at the edge resulting in another solution with the same
      # least squares loss (i.e. both solutions acceptable).
      # Reflects https://github.com/fairlearn/fairlearn/issues/265
      assert logging_all_close(
          [[3.2, 11.2]], [all_predict[0]]
      ) or logging_all_close([[3.03010885, 11.2]], [all_predict[0]])
  
      assert logging_all_close(
          [
              [-3.47346939, 10.64897959],
              [-2.68, 10.12],
              [-1.91764706, 9.61176471],
              [-1.18461538, 9.12307692],
              [-0.47924528, 8.65283019],
              [0.2, 0.7],
          ],
          all_predict[1:],
      )
  
file_path: fairlearn/test/unit/reductions/grid_search/test_grid_search_regression.py
imports: import copy
  from test.unit.reductions.grid_search.utilities import assert_n_grid_search_results
  from test.unit.utility_functions import logging_all_close
  import numpy
  import pandas
  import pytest
  from sklearn.linear_model import LinearRegression
  from fairlearn.reductions import BoundedGroupLoss, GridSearch, SquareLoss, ZeroOneLoss
label_desc: Use image node_id 2 for calling a global function with example usage: test_bgl_unfair(A_two_dim) without return types