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 _wrap_model(self, model):
      def wrapped(x, ts, **kwargs):
          ts_cpu = ts.detach().to("cpu")
          return model(
              x,
              self.timestep_map[ts_cpu].to(
                  device=ts.device, dtype=ts.dtype
              ),
              **kwargs,
          )
  
      return wrapped
  
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 5 for calling the SpacedDiffusion obj's underlying member method code with example usage: obj._wrap_model(model) and returns: wrapped, model