automl21/scs_neural/experimentation/launcher.py [247:264]:
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            if multi_instance.num_instances == batch_size:
                soln_neural, scs_neural_metrics = self.scs_neural.solve(
                    multi_instance, max_iters=n_iter, track_metrics=True, train=False)
            else:
                all_soln_neural, all_neural_metrics = [], []
                for i in range(0, multi_instance.num_instances, batch_size):
                    max_instance_id = min((i + batch_size), multi_instance.num_instances)
                    curr_test = self.scs_neural.select_instances(
                        multi_instance, 
                        [x for x in range(i, max_instance_id)])
                    soln_neural, scs_neural_metrics = self.scs_neural.solve(
                        curr_test, max_iters=n_iter, track_metrics=True, train=False)
                    all_soln_neural = all_soln_neural + soln_neural
                    all_neural_metrics = all_neural_metrics + scs_neural_metrics
                soln_neural, scs_neural_metrics = all_soln_neural, all_neural_metrics
        
        losses = [soln_neural[i]['loss'] for i in range(len(soln_neural))]
        loss, index_nans = self._compute_loss(losses)
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automl21/scs_neural/experimentation/launcher.py [389:406]:
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        if multi_instance.num_instances == batch_size:
            soln_neural, scs_neural_metrics = self.scs_neural.solve(
                multi_instance, max_iters=n_iter, track_metrics=True, train=False)
        else:
            all_soln_neural, all_neural_metrics = [], []
            for i in range(0, multi_instance.num_instances, batch_size):
                max_instance_id = min((i + batch_size), multi_instance.num_instances)
                curr_test = self.scs_neural.select_instances(
                    multi_instance, 
                    [x for x in range(i, max_instance_id)])
                soln_neural, scs_neural_metrics = self.scs_neural.solve(
                    curr_test, max_iters=n_iter, track_metrics=True, train=False)
                all_soln_neural = all_soln_neural + soln_neural
                all_neural_metrics = all_neural_metrics + scs_neural_metrics
            soln_neural, scs_neural_metrics = all_soln_neural, all_neural_metrics

        losses = [soln_neural[i]['loss'] for i in range(len(soln_neural))]
        loss, index_nans = self._compute_loss(losses)
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