code: def wgan(cls, data:DataBunch, generator:nn.Module, critic:nn.Module, switcher:Callback=None, clip:float=0.01, **learn_kwargs):
          "Create a WGAN from `data`, `generator` and `critic`."
          return cls(data, generator, critic, NoopLoss(), WassersteinLoss(), switcher=switcher, clip=clip, **learn_kwargsf wgan(cls, data:DataBunch, generator:nn.Module, critic:nn.Module, switcher:Callback=None, clip:float=0.01, **learn_kwargs):
          "Create a WGAN from `data`, `generator` and `critic`."
          return cls(data, generator, critic, NoopLoss(), WassersteinLoss(), switcher=switcher, clip=clip, **learn_kwargs)
docs: Create a WGAN from `data`, `generator` and `critic`.