- Torch 2.0 was released in march 2023, introducing a number of
  new features to the torch ecosystem, while being non-breaking
  wrt past versions on most points.
- One of the salient changes is the introduction of `torch.func`,
  that integrates features previously introduced as part of the
  `functorch` package, which we have been relying upon in order
  to efficiently compute and clip sample-wise gradients.
- This commit therefore introduces a new backend code branch so
  as to make use of `torch.func` when `torch~=2.0` is used, and
  retain the existing `functorch`-based branch when older torch
  versions are used.
- As part of this effort, some of the `TorchModel` backend code
  was refactored, notably deferring the functional transform of
  the input `torch.nn.Module` to the first call for sample-wise
  gradients computation. This has the nice side effect of fixing
  cases when users want to train a non-functorch-compatible model
  without DP-SGD.