# dtw_loss_functions.soft_dtw_implementations.soft_dtw_cuda_ron.module module

class dtw_loss_functions.soft_dtw_implementations.soft_dtw_cuda_ron.module.SoftDTW(*, gamma: float = 1.0, bandwidth: float | None = None, normalize: bool = False, dist: str = 'sqeuclidean', fused: bool | None = None)

  Bases: `Module`

  User-facing module.

  - dist: currently supports “sqeuclidean”

  - normalize: SoftDTW(x,y) - 0.5*(SoftDTW(x,x)+SoftDTW(y,y))

  - fused:

      None -> auto (use fused only when possible) True -> require fused (error if not possible) False -> never fused (always materialize D and use D-based autograd)

  Methods

  `forward`(x, y) | Define the computation performed at every call.

  forward(x: Tensor, y: Tensor) → Tensor

    Define the computation performed at every call.

    Should be overridden by all subclasses.

    Note

    Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.