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 _wrap_model(self, model, autoregressive=False):
      if isinstance(model, _WrappedModel) or isinstance(
          model, _WrappedAutoregressiveModel
      ):
          return model
      mod = (
          _WrappedAutoregressiveModel
          if autoregressive
          else _WrappedModel
      )
      return mod(
          model,
          self.timestep_map,
          self.rescale_timesteps,
          self.original_num_steps,
      )
  
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 7 for calling the SpacedDiffusion obj's underlying member method code with example usage: obj._wrap_model(model, autoregressive) and returns: mod, model