diff --git a/beginner_source/introyt/introyt1_tutorial.py b/beginner_source/introyt/introyt1_tutorial.py
index 74675070708..c01befb40cc 100644
--- a/beginner_source/introyt/introyt1_tutorial.py
+++ b/beginner_source/introyt/introyt1_tutorial.py
@@ -303,22 +303,21 @@ def num_flat_features(self, x):
 #    The values passed to the transform are the means (first tuple) and the
 #    standard deviations (second tuple) of the rgb values of the images in
 #    the dataset. You can calculate these values yourself by running these
-#    few lines of code:
-#          ```
-#           from torch.utils.data import ConcatDataset
-#           transform = transforms.Compose([transforms.ToTensor()])
-#           trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
+#    few lines of code::
+#
+#        from torch.utils.data import ConcatDataset
+#        transform = transforms.Compose([transforms.ToTensor()])
+#        trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
 #                                        download=True, transform=transform)
 #
-#           #stack all train images together into a tensor of shape 
-#           #(50000, 3, 32, 32)
-#           x = torch.stack([sample[0] for sample in ConcatDataset([trainset])])
+#        # stack all train images together into a tensor of shape 
+#        # (50000, 3, 32, 32)
+#        x = torch.stack([sample[0] for sample in ConcatDataset([trainset])])
 #           
-#           #get the mean of each channel            
-#           mean = torch.mean(x, dim=(0,2,3)) #tensor([0.4914, 0.4822, 0.4465])
-#           std = torch.std(x, dim=(0,2,3)) #tensor([0.2470, 0.2435, 0.2616])  
-# 
-#          ```   
+#        # get the mean of each channel            
+#        mean = torch.mean(x, dim=(0,2,3)) # tensor([0.4914, 0.4822, 0.4465])
+#        std = torch.std(x, dim=(0,2,3)) # tensor([0.2470, 0.2435, 0.2616])  
+#    
 # 
 # There are many more transforms available, including cropping, centering,
 # rotation, and reflection.