class_docstr: wraps a discretizer, and prunes the sigmas
  params:
      strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
code: def __call__(self, *args, **kwargs):
      # sigmas start large first, and decrease then
      sigmas = self.discretization(*args, **kwargs)
      print(
          f"sigmas after discretization, before pruning img2img: ",
          sigmas,
      )
      sigmas = torch.flip(sigmas, (0,))
      sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
      print("prune index:", max(int(self.strength * len(sigmas)), 1))
      sigmas = torch.flip(sigmas, (0,))
      print(f"sigmas after pruning: ", sigmas)
      return sigmas
  
imports: import torch
  from sgm.modules.diffusionmodules.discretizer import Discretization
label_desc: Use image node_id 2 for calling the Img2ImgDiscretizationWrapper obj's underlying member method code with example usage: obj.__call__() and returns: sigmas