fix FakeTensor creation on noncontiguous subclasses

[bdhirsh]

Fixes https://github.com/pytorch/pytorch/issues/125287

Fixes https://github.com/pytorch/pytorch/issues/124090, context on the issue

Stack from ghstack (oldest at bottom):

• #124400
• -> #124399
• #124398

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[soulitzer]

Public API sounds okay, but preferably with a clear story around when we'd use this over existing APIs that do similar "forbid_in_autograd" things like .detach() or setting .requires_grad=False. (We also have the private torch._C._functions.DelayedError, which is an out-of-place version of this.) If it is difficult to have such examples, I don't mind keeping it private either for now, but no strong preference.

[bdhirsh]

I updated the PR with @soulitzer's idea to not add a new API: it seems like torch._C._functions.DelayedError should do the job.

Adding that ErrorNode also caused some test failures, which made me realize that autograd.backward is broken in dynamo: https://github.com/pytorch/pytorch/issues/125287. I just fixed it directly in this PR.

[bdhirsh]

Hmm... the errors I'm getting are because y = torch._C._functions.DelayedError(1)(x) don't "propagate" the fact that x is a fake tensor (it returns a plain tensor)

[bdhirsh]

oh it's probably because the SparseTensorImpl C++ subclasses don't override shallow_copy_and_detach properly...

We probably are not able to handle "tensor subclass holding a fake sparse tensor" today for other reasons, so I'm going to leave the sparse fakify logic alone and have it continue using clone for now.