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 p_mean_variance(
      self, model, *args, **kwargs
  ):  # pylint: disable=signature-differs
      return super().p_mean_variance(
          self._wrap_model(model), *args, **kwargs
      )
  
file_path: improved-diffusion/improved_diffusion/respace.py
imports: import numpy
  import torch
  from .gaussian_diffusion import GaussianDiffusion
label_desc: Use image node_id 2 for calling the SpacedDiffusion obj's underlying member method code with example usage: obj.p_mean_variance(model) and returns: super