code: def __call__(self, x, ts, **kwargs):
      map_tensor = th.tensor(
          self.timestep_map, device=ts.device, dtype=ts.dtype
      )
      new_ts = map_tensor[ts]
      if self.rescale_timesteps:
          new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
      model_output = self.model(x, new_ts, **kwargs)
      return model_output
  
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 _WrappedModel obj's underlying member method code with example usage: obj.__call__(x, ts) and returns: model_output