From 12d2470931902eca6d5acdd04efec174c27bef19 Mon Sep 17 00:00:00 2001
From: tgilbert09 <24855209+tgilbert09@users.noreply.github.com>
Date: Sun, 25 Aug 2024 13:05:25 +0100
Subject: [PATCH] Change order of optimizer.step() in documentation

Commit be6e86342233bdc932deace52d7388073fbb66b4 changed the order of optimization steps in the "# Backpropagation" section of the code (see lines 161 to 163). This commit updates the order of optimization steps in the description/comments to match the code.
---
 beginner_source/basics/optimization_tutorial.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/beginner_source/basics/optimization_tutorial.py b/beginner_source/basics/optimization_tutorial.py
index c6c327f8511..b5918dc09a1 100644
--- a/beginner_source/basics/optimization_tutorial.py
+++ b/beginner_source/basics/optimization_tutorial.py
@@ -134,9 +134,9 @@ def forward(self, x):
 
 #####################################
 # Inside the training loop, optimization happens in three steps:
-#  * Call ``optimizer.zero_grad()`` to reset the gradients of model parameters. Gradients by default add up; to prevent double-counting, we explicitly zero them at each iteration.
 #  * Backpropagate the prediction loss with a call to ``loss.backward()``. PyTorch deposits the gradients of the loss w.r.t. each parameter.
 #  * Once we have our gradients, we call ``optimizer.step()`` to adjust the parameters by the gradients collected in the backward pass.
+#  * Call ``optimizer.zero_grad()`` to reset the gradients of model parameters. Gradients by default add up; to prevent double-counting, we explicitly zero them at each iteration.
 
 
 ########################################