💡 [REQUEST] - What is purpose of `out.backward(torch.randn(1, 10))` in neural_networks_tutorial

🚀 Describe the improvement or the new tutorial

In neural networks tutorial for beginners, we have the following:

Zero the gradient buffers of all parameters and backprops with random gradients:

net.zero_grad()
out.backward(torch.randn(1, 10))

What is the purpose of this? It is not part of standard ML workflows and can be confusing to beginners. (As evidence,I am helping some people learn basics of ML and I got questions about this line. This is how I found out about it!)

If there is no good reason for it, then I suggest:

• dropping these few lines
• changing wording of other parts of the page if needed. E.g. 'at this point we covered... calling backward'

Existing tutorials on this topic

No response

Additional context

No response

cc @subramen @albanD

Open the linked neural networks tutorial and read the section containing net.zero_grad() and out.backward(torch.randn(1, 10)), including the surrounding explanation. Decide whether the example needs those lines for its teaching goal, then update the snippet and any wording that depends on it. Done means the beginner-facing explanation is consistent with the revised example.