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

Before Change

            self.global_step += 1

        if self.scheduler and info.get(SCHEDULER_STEP) == SCHEDULER_STEP_EPOCH:
            self.scheduler.step()

        return metric_meters.summary()

    def train_batch(self, batch, batch_info):

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

        if self.use_tqdm and self.world_rank == 0:

Instances