code: def test_pickle_bgl(loss):
      gs = GridSearch(
          LinearRegression(),
          constraints=BoundedGroupLoss(
              loss=copy.deepcopy(loss), upper_bound=0.2
          ),
      )
      X, y, A = _quick_data()
  
      gs.fit(X, y, sensitive_features=A)
  
      # The following should not throw an exception
      pickle.dumps(gs)
  
file_path: fairlearn/test/unit/reductions/grid_search/test_pickle.py
imports: import copy
  import pickle
  import pytest
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from fairlearn.reductions import DemographicParity, TruePositiveRateParity, FalsePositiveRateParity, ErrorRateParity
  from fairlearn.reductions import BoundedGroupLoss
  from fairlearn.reductions import AbsoluteLoss, SquareLoss, ZeroOneLoss
  from fairlearn.reductions import GridSearch
  from .utilities import _quick_data
label_desc: Use image node_id 2 for calling a global function with example usage: test_pickle_bgl(loss) without return types