415be78cc0d1275a29d0ceda550d0d7a7a5224ea,python/ray/util/sgd/torch/training_operator.py,TrainingOperator,train_epoch,#TrainingOperator#Any#Any#,120

Before Change

                    postfix.update(loss=metrics["train_loss"])
                _progress_bar.set_postfix(postfix)

            if self.scheduler and batch_info.get(
                    SCHEDULER_STEP) == SCHEDULER_STEP_BATCH:
                self.scheduler.step()

            metric_meters.update(metrics, n=metrics.pop(NUM_SAMPLES, 1))

After Change

        Returns:
            A dict of metrics from training.
        
        if not hasattr(self, "model"):
            raise RuntimeError("Either set self.model in setup function or "
                               "override this method to implement a custom "
                               "training loop.")
        model = self.model
        scheduler = None
        if hasattr(self, "scheduler"):
            scheduler = self.scheduler

Instances