class_docstr: A diffusion process which can skip steps in a base diffusion process.
  
  :param use_timesteps: a collection (sequence or set) of timesteps from the
                        original diffusion process to retain.
  :param kwargs: the kwargs to create the base diffusion process.
code: def condition_mean(self, cond_fn, *args, **kwargs):
      return super().condition_mean(
          self._wrap_model(cond_fn), *args, **kwargs
      )
  
file_path: stablediffusion/ldm/modules/karlo/kakao/modules/diffusion/respace.py
imports: import torch
  from .gaussian_diffusion import GaussianDiffusion
label_desc: Use image node_id 3 for calling the SpacedDiffusion obj's underlying member method code with example usage: obj.condition_mean(cond_fn) and returns: super