Updated Fabric trainer example to not call self.trainer.model durin…

…g validation (#19993)

examples/fabric/build_your_own_trainer/trainer.py

@@ -264,7 +264,7 @@ def val_loop(
         val_loader: Optional[torch.utils.data.DataLoader],
         limit_batches: Union[int, float] = float("inf"),
     ):
-        """The validation loop ruunning a single validation epoch.
+        """The validation loop running a single validation epoch.
 
         Args:
             model: the LightningModule to evaluate
@@ -285,7 +285,10 @@ def val_loop(
             )
             return
 
-        self.fabric.call("on_validation_model_eval")  # calls `model.eval()`
+        if not is_overridden("on_validation_model_eval", _unwrap_objects(model)):
+            model.eval()
+        else:
+            self.fabric.call("on_validation_model_eval")  # calls `model.eval()`
 
         torch.set_grad_enabled(False)
 
@@ -311,7 +314,10 @@ def val_loop(
 
         self.fabric.call("on_validation_epoch_end")
 
-        self.fabric.call("on_validation_model_train")
+        if not is_overridden("on_validation_model_train", _unwrap_objects(model)):
+            model.train()
+        else:
+            self.fabric.call("on_validation_model_train")
         torch.set_grad_enabled(True)
 
     def training_step(self, model: L.LightningModule, batch: Any, batch_idx: int) -> torch.Tensor: