diff --git a/pytorch_lightning/core/decorators.py b/pytorch_lightning/core/decorators.py
index cc74cfc71d83f8..15843492293cba 100644
--- a/pytorch_lightning/core/decorators.py
+++ b/pytorch_lightning/core/decorators.py
@@ -1,8 +1,10 @@
 from functools import wraps
 from typing import Callable
 
+import torch
+
 from pytorch_lightning.core.lightning import LightningModule
-from pytorch_lightning.utilities import rank_zero_warn, transfer_batch_to_device
+from pytorch_lightning.utilities import rank_zero_warn
 
 
 def data_loader(fn):
@@ -32,21 +34,24 @@ def auto_move_data(fn: Callable) -> Callable:
 
     Example:
 
-        .. code-block:: python
+        >>> class LitModel(LightningModule):
+        ...     @auto_move_data
+        ...     def forward(self, x):
+        ...         return x
+        >>> LitModel.forward = auto_move_data(LitModel.forward)
+        >>> model = LitModel()
+        >>> model = model.to('cuda')
+        >>> model(torch.zeros(1, 3))
+        tensor([[0., 0., 0.]], device='cuda:0')
 
-            model = model.cuda(0)
-            model.prepare_data()
-            loader = model.train_dataloader()
-            for x, y in loader:
-                output = model(x)
     """
     @wraps(fn)
     def auto_transfer_args(self, *args, **kwargs):
         if not isinstance(self, LightningModule):
             return fn(self, *args, **kwargs)
 
-        args = transfer_batch_to_device(args, self.device)
-        kwargs = transfer_batch_to_device(kwargs, self.device)
+        args = self.transfer_batch_to_device(args, self.device)
+        kwargs = self.transfer_batch_to_device(kwargs, self.device)
         return fn(self, *args, **kwargs)
 
     return auto_transfer_args