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.
class_docstr_tok: ["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
      )
  
imports: import enum
  import math
  import numpy
  import torch
  import torch
  from tqdm import tqdm
  from TTS.tts.layers.tortoise.dpm_solver import DPM_Solver, NoiseScheduleVP, model_wrapper
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