diff --git a/docs/source/datamodules.rst b/docs/source/datamodules.rst
index 998ccf49c5..e1fbeabd96 100644
--- a/docs/source/datamodules.rst
+++ b/docs/source/datamodules.rst
@@ -165,6 +165,7 @@ Here's a more realistic, complex DataModule that shows how much more reusable th
 
 .. note:: ``setup`` expects a string arg ``stage``. It is used to separate setup logic for ``trainer.fit`` and ``trainer.test``.
 
+
 ---------------
 
 LightningDataModule API
@@ -203,6 +204,7 @@ There are also data operations you might want to perform on every GPU. Use setup
 - count number of classes
 - build vocabulary
 - perform train/val/test splits
+- apply transforms (defined explicitly in your datamodule or assigned in init)
 - etc...
 
 .. code-block:: python
@@ -216,13 +218,23 @@ There are also data operations you might want to perform on every GPU. Use setup
 
             # Assign Train/val split(s) for use in Dataloaders
             if stage == 'fit' or stage is None:
-                mnist_full = MNIST(self.data_dir, train=True, download=True)
+                mnist_full = MNIST(
+                    self.data_dir,
+                    train=True,
+                    download=True,
+                    transform=self.transform
+                )
                 self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])
                 self.dims = self.mnist_train[0][0].shape
 
             # Assign Test split(s) for use in Dataloaders
             if stage == 'test' or stage is None:
-                self.mnist_test = MNIST(self.data_dir, train=False, download=True)
+                self.mnist_test = MNIST(
+                    self.data_dir,
+                    train=False,
+                    download=True,
+                    transform=self.transform
+                )
                 self.dims = getattr(self, 'dims', self.mnist_test[0][0].shape)
 
 
@@ -231,7 +243,7 @@ There are also data operations you might want to perform on every GPU. Use setup
 
 train_dataloader
 ^^^^^^^^^^^^^^^^
-Use this method to generate the train dataloader. This is also a good place to place default transformations.
+Use this method to generate the train dataloader.  Usually you just wrap the dataset you defined in ``setup``.
 
 .. code-block:: python
 
@@ -240,25 +252,12 @@ Use this method to generate the train dataloader. This is also a good place to p
 
     class MNISTDataModule(pl.LightningDataModule):
         def train_dataloader(self):
-            transforms = transform_lib.Compose([
-                transform_lib.ToTensor(),
-                transform_lib.Normalize(mean=(0.5,), std=(0.5,)),
-            ])
-            return DataLoader(self.train_dataset, transform=transforms, batch_size=64)
+            return DataLoader(self.mnist_train, batch_size=64)
 
-However, to decouple your data from transforms you can parametrize them via `__init__`.
-
-.. code-block:: python
-
-    class MNISTDataModule(pl.LightningDataModule):
-        def __init__(self, train_transforms, val_transforms, test_transforms):
-            self.train_transforms = train_transforms
-            self.val_transforms = val_transforms
-            self.test_transforms = test_transforms
 
 val_dataloader
 ^^^^^^^^^^^^^^
-Use this method to generate the val dataloader. This is also a good place to place default transformations.
+Use this method to generate the val dataloader.  Usually you just wrap the dataset you defined in ``setup``.
 
 .. code-block:: python
 
@@ -267,15 +266,12 @@ Use this method to generate the val dataloader. This is also a good place to pla
 
     class MNISTDataModule(pl.LightningDataModule):
         def val_dataloader(self):
-            transforms = transform_lib.Compose([
-                transform_lib.ToTensor(),
-                transform_lib.Normalize(mean=(0.5,), std=(0.5,)),
-            ])
-            return DataLoader(self.val_dataset, transform=transforms, batch_size=64)
+            return DataLoader(self.mnist_val, batch_size=64)
+
 
 test_dataloader
 ^^^^^^^^^^^^^^^
-Use this method to generate the test dataloader. This is also a good place to place default transformations.
+Use this method to generate the test dataloader. Usually you just wrap the dataset you defined in ``setup``.
 
 .. code-block:: python
 
@@ -284,11 +280,7 @@ Use this method to generate the test dataloader. This is also a good place to pl
 
     class MNISTDataModule(pl.LightningDataModule):
         def test_dataloader(self):
-            transforms = transform_lib.Compose([
-                transform_lib.ToTensor(),
-                transform_lib.Normalize(mean=(0.5,), std=(0.5,)),
-            ])
-            return DataLoader(self.test_dataset, transform=transforms, batch_size=64)
+            return DataLoader(self.mnist_test, batch_size=64)
 
 transfer_batch_to_device
 ^^^^^^^^^^^^^^^^^^^^^^^^
@@ -306,6 +298,19 @@ Override to define how you want to move an arbitrary batch to a device
             batch['x'].to(device)
             return batch
 
+
+.. note:: To decouple your data from transforms you can parametrize them via `__init__`.
+
+.. code-block:: python
+
+    class MNISTDataModule(pl.LightningDataModule):
+        def __init__(self, train_transforms, val_transforms, test_transforms):
+            super().__init__()
+            self.train_transforms = train_transforms
+            self.val_transforms = val_transforms
+            self.test_transforms = test_transforms
+
+
 ------------------
 
 Using a DataModule