trunk/e534ad51600e9ef595a181084ba9487c87c3c1c8: Fix detach_ autograd metadata tracking in Dynamo (#177875)

The PyTorch team has merged a crucial fix for a bug in its Dynamo graph compiler, identified as issue #176854. The root cause was that when Dynamo traced the in-place `Tensor.detach_()` operation into an FX graph, it failed to update the internal metadata of the corresponding `TensorVariable`. This meant subsequent queries for attributes like `requires_grad` or `grad_fn` would return pre-detach, stale values, even though the operation to detach the tensor had been correctly recorded. This mismatch between tracked metadata and the actual graph state could lead to incorrect compilation and runtime errors.

The proposed fix, authored by bobrenjc93, introduces a dedicated handler for `detach_()` within Dynamo. This handler ensures that after the operation is traced, it performs fake tensor propagation and immediately synchronizes the tensor variable's metadata with the new, detached state of the fake tensor. This approach corrects the bookkeeping error at the source within the compiler, rather than applying a patch downstream. A regression test was also added to validate the behavior of `requires_grad` and `grad_fn` after `detach_()` when using `torch.compile(..., backend='eager')`, safeguarding against future regressions.