diff --git a/train.py b/train.py
index 9f5c7ff89..9c8883772 100755
--- a/train.py
+++ b/train.py
@@ -641,10 +641,11 @@ def flush(self):
                 model = torch.compile(model, mode=args.compiler_mode,
                                       backend=args.compiler_backend)
 
-                #TODO: Optimize DDP is currently not supported with QAT.
-                #      Once pytorch supports DDP with higher order ops, we can enable optimize DDP with QAT.
-                #      https://github.com/pytorch/pytorch/issues/104674.
-                torch._dynamo.config.optimize_ddp=False
+                # TODO: Optimize DDP is currently not supported with QAT.
+                # Once pytorch supports DDP with higher order ops,
+                # we can enable optimize DDP with QAT.
+                # https://github.com/pytorch/pytorch/issues/104674.
+                torch._dynamo.config.optimize_ddp = False # pylint: disable=protected-access
                 msglogger.info(
                     'torch.compile() successful, mode=%s, cache limit=%d',
                     args.compiler_mode,
@@ -1089,17 +1090,16 @@ def test(test_loader, model, criterion, loggers, args, mode='ckpt', ckpt_name=No
             model = apputils.load_lean_checkpoint(model, best_ckpt_path)
 
         if ddp:
-                model = DistributedDataParallel(
-                    model,
-                    device_ids=[local_rank] if args.device == 'cuda' else None,
-                    output_device=local_rank if args.device == 'cuda' else None,
-                )
+            model = DistributedDataParallel(
+                model,
+                device_ids=[local_rank] if args.device == 'cuda' else None,
+                output_device=local_rank if args.device == 'cuda' else None,
+            )
 
         if dynamo:
             torch._dynamo.reset()  # pylint: disable=protected-access
             model = torch.compile(model, mode=args.compiler_mode,
-                                    backend=args.compiler_backend,
-                                    )
+                                  backend=args.compiler_backend)
             msglogger.info(
                 'torch.compile() successful, mode=%s, cache limit=%d',
                 args.compiler_mode,