Fix doctests

docs/source/starter/core_guide.rst

@@ -1,9 +1,3 @@
-.. testsetup:: *
-
-    from pytorch_lightning.core.lightning import LightningModule
-    from pytorch_lightning.core.datamodule import LightningDataModule
-    from pytorch_lightning.trainer.trainer import Trainer
-
 .. _introduction_guide:
 
 #########################
@@ -72,7 +66,7 @@ Let's first start with the model. In this case, we'll design a 3-layer neural ne
     import torch
     from torch.nn import functional as F
     from torch import nn
-    from pytorch_lightning.core.lightning import LightningModule
+    from pytorch_lightning import LightningModule
 
 
     class LitMNIST(LightningModule):
@@ -187,14 +181,13 @@ Data
 
 Lightning operates on pure dataloaders. Here's the PyTorch code for loading MNIST.
 
-.. testcode::
-    :skipif: not _TORCHVISION_AVAILABLE
+.. code-block:: python
 
-    from torch.utils.data import DataLoader, random_split
-    from torchvision.datasets import MNIST
     import os
-    from torchvision import datasets, transforms
-    from pytorch_lightning import Trainer
+
+    from torch.utils.data import DataLoader
+    from torchvision import transforms
+    from torchvision.datasets import MNIST
 
     # transforms
     # prepare transforms standard to MNIST
@@ -204,19 +197,6 @@ Lightning operates on pure dataloaders. Here's the PyTorch code for loading MNIS
     mnist_train = MNIST(os.getcwd(), train=True, download=True, transform=transform)
     mnist_train = DataLoader(mnist_train, batch_size=64)
 
-.. testoutput::
-    :hide:
-    :skipif: os.path.isdir(os.path.join(os.getcwd(), 'MNIST')) or not _TORCHVISION_AVAILABLE
-
-    Downloading ...
-    Extracting ...
-    Downloading ...
-    Extracting ...
-    Downloading ...
-    Extracting ...
-    Processing...
-    Done!
-
 You can use DataLoaders in three ways:
 
 1. Pass DataLoaders to .fit()