433d5122e9c72868571d7c72f10cbf58dcec3f3d,optuna/integration/botorch.py,,qei_candidates_func,#Any#Any#Any#Any#,49

Before Change

        train_y = torch.cat([train_obj, train_con], dim=-1)

        is_feas = (train_con <= 0).all(dim=-1)
        best_f = (train_obj.transpose(1, 0) * is_feas).max()

        constraints = []
        n_contraints = train_con.size(1)
        for i in range(n_contraints):

After Change

            )
            best_f = torch.zeros(())
        else:
            best_f = train_obj_feas.max()

        constraints = []
        n_contraints = train_con.size(1)
        for i in range(n_contraints):

Instances