* ENH: Support PyTorch builtin loss functions for hedging loss (568) (569)
* You can now use PyTorch built-in loss function modules as `criterion` of `Hedger`.
* For instance, with `MSELoss`, criterion measures mean-squared error between the payoff of a contingent claim and its replicating portfolio.
* Migration guide: If you have defined your own `HedgeLoss`, please modify the signatures of its methods as `forward(self, input) -> forward(self, input, target=0.0)` and `cash(self, input) -> cash(self, input, target=0.0)`.
* ENH: Suppprt multiple hedges in `nn.functional.pl` (571)
* DOC: Add examples to Black-Scholes functionals (566)
* MAINT: Use `cast_state` (567)
* Bumping version from 0.19.2 to 0.20.0 (573)
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