Update Custom autograd Function example — use torch.accelerator, modernize Function pattern

[sekyondaMeta]

Description

The Defining New autograd Functions tutorial contains outdated patterns that should be modernized.

Changes needed

Suboptimal / Outdated Patterns

Issue	Current Code	Modern Alternative	Notes
Hardcoded CPU device with commented-out CUDA	device = torch.device("cpu") / # device = torch.device("cuda:0")	device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"	PyTorch 2.4 introduced accelerator-agnostic device selection.
autograd.Function using forward(ctx, ...) for context	def forward(ctx, input):	Consider using @staticmethod def forward(input): with separate def setup_context(ctx, inputs, output):	PyTorch 2.0+ introduced setup_context as the preferred pattern for new code; old style still works but is no longer recommended.

Files

• beginner_source/examples_autograd/polynomial_custom_function.py

[jvz37]

/assigntome