Chore: revert some unwanted formatting changes

Author: S1ro1

src/transformers/trainer.py

@@ -2427,7 +2427,7 @@ def _inner_training_loop(
         self.state = TrainerState(
             stateful_callbacks=[
                 cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState)
-            ],
+            ]
         )
         self.state.is_hyper_param_search = trial is not None
         self.state.train_batch_size = self._train_batch_size
@@ -3198,10 +3198,7 @@ def _maybe_log_save_evaluate(
             # reset tr_loss to zero
             tr_loss -= tr_loss
 
-            logs["loss"] = round(
-                tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged),
-                4,
-            )
+            logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
             if grad_norm is not None:
                 logs["grad_norm"] = grad_norm.item() if isinstance(grad_norm, torch.Tensor) else grad_norm
             if learning_rate is not None:
@@ -5495,7 +5492,6 @@ def create_accelerator_and_postprocess(self):
         self.is_deepspeed_enabled = getattr(self.accelerator.state, "deepspeed_plugin", None) is not None
         self.is_fsdp_enabled = getattr(self.accelerator.state, "fsdp_plugin", None) is not None
         self.is_tp_enabled = getattr(self.accelerator.state, "torch_tp_plugin", None) is not None
-
         # post accelerator creation setup
         if self.is_fsdp_enabled:
             fsdp_plugin = self.accelerator.state.fsdp_plugin