diff --git a/beginner_source/basics/autogradqs_tutorial.py b/beginner_source/basics/autogradqs_tutorial.py
index 8eff127ddee..2753103eaa8 100644
--- a/beginner_source/basics/autogradqs_tutorial.py
+++ b/beginner_source/basics/autogradqs_tutorial.py
@@ -133,7 +133,8 @@
 #   - To mark some parameters in your neural network as **frozen parameters**.
 #   - To **speed up computations** when you are only doing forward pass, because computations on tensors that do
 #     not track gradients would be more efficient.
-
+# See this `note <https://docs.pytorch.org/docs/stable/notes/autograd.html#locally-disabling-gradient-computation>`_ 
+# for additional reference. 
 
 ######################################################################
 
@@ -160,6 +161,16 @@
 # - accumulates them in the respective tensor’s ``.grad`` attribute
 # - using the chain rule, propagates all the way to the leaf tensors.
 #
+# To get a sense of what this computational graph looks like we can use the following tools:  
+#
+#1. torchviz is a package to visualize computational graphs. 
+#   See the repository here: `https://github.com/szagoruyko/pytorchviz <https://github.com/szagoruyko/pytorchviz>`_
+#
+#2. Setting ``TORCH_LOGS="+autograd"`` enables logging for the backward pass. See details in this 
+#   discussion: `https://dev-discuss.pytorch.org/t/highlighting-a-few-recent-autograd-features-h2-2023/1787 <https://dev-discuss.pytorch.org/t/highlighting-a-few-recent-autograd-features-h2-2023/1787>`_
+#
+#
+#
 # .. note::
 #   **DAGs are dynamic in PyTorch**
 #   An important thing to note is that the graph is recreated from scratch; after each