Update autograd_tutorial.py (#1787)

Fixed link and note

Author: holly1238

beginner_source/blitz/autograd_tutorial.py

@@ -32,16 +32,16 @@
 
 
 Usage in PyTorch
-~~~~~~~~~~~
+~~~~~~~~~~~~~~~~
 Let's take a look at a single training step.
 For this example, we load a pretrained resnet18 model from ``torchvision``.
 We create a random data tensor to represent a single image with 3 channels, and height & width of 64,
 and its corresponding ``label`` initialized to some random values. Label in pretrained models has
 shape (1,1000).
 
-Important
-~~~~~~~~~~~
-This tutorial work only on CPU and will not work on GPU (even if tensor are moved to CUDA).
+.. note::
+    This tutorial work only on CPU and will not work on GPU (even if tensor are moved to CUDA).
+
 """
 import torch, torchvision
 model = torchvision.models.resnet18(pretrained=True)
@@ -66,7 +66,7 @@
 loss.backward() # backward pass
 
 ############################################################
-# Next, we load an optimizer, in this case SGD with a learning rate of 0.01 and [momentum](https://towardsdatascience.com/stochastic-gradient-descent-with-momentum-a84097641a5d) of 0.9.
+# Next, we load an optimizer, in this case SGD with a learning rate of 0.01 and `momentum <https://towardsdatascience.com/stochastic-gradient-descent-with-momentum-a84097641a5d>`__ of 0.9.
 # We register all the parameters of the model in the optimizer.
 #