code: def sample(
      self,
      x,
      steps=20,
      t_start=None,
      t_end=None,
      order=3,
      skip_type="time_uniform",
      method="singlestep",
      lower_order_final=True,
      denoise_to_zero=False,
      solver_type="dpm_solver",
      atol=0.0078,
      rtol=0.05,
      corrector=False,
  ):
      t_0 = (
          1.0 / self.noise_schedule.total_N if t_end is None else t_end
      )
      t_T = self.noise_schedule.T if t_start is None else t_start
      device = x.device
      if method == "multistep":
          assert steps >= order, "UniPC order must be < sampling steps"
          timesteps = self.get_time_steps(
              skip_type=skip_type,
              t_T=t_T,
              t_0=t_0,
              N=steps,
              device=device,
          )
          # print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
          assert timesteps.shape[0] - 1 == steps
          with torch.no_grad():
              vec_t = timesteps[0].expand((x.shape[0]))
              model_prev_list = [self.model_fn(x, vec_t)]
              t_prev_list = [vec_t]
              with tqdm.tqdm(total=steps) as pbar:
                  # Init the first `order` values by lower order multistep DPM-Solver.
                  for init_order in range(1, order):
                      vec_t = timesteps[init_order].expand(x.shape[0])
                      x, model_x = self.multistep_uni_pc_update(
                          x,
                          model_prev_list,
                          t_prev_list,
                          vec_t,
                          init_order,
                          use_corrector=True,
                      )
                      if model_x is None:
                          model_x = self.model_fn(x, vec_t)
                      if self.after_update is not None:
                          self.after_update(x, model_x)
                      model_prev_list.append(model_x)
                      t_prev_list.append(vec_t)
                      pbar.update()
  
                  for step in range(order, steps + 1):
                      vec_t = timesteps[step].expand(x.shape[0])
                      if lower_order_final:
                          step_order = min(order, steps + 1 - step)
                      else:
                          step_order = order
                      # print('this step order:', step_order)
                      if step == steps:
                          # print('do not run corrector at the last step')
                          use_corrector = False
                      else:
                          use_corrector = True
                      x, model_x = self.multistep_uni_pc_update(
                          x,
                          model_prev_list,
                          t_prev_list,
                          vec_t,
                          step_order,
                          use_corrector=use_corrector,
                      )
                      if self.after_update is not None:
                          self.after_update(x, model_x)
                      for i in range(order - 1):
                          t_prev_list[i] = t_prev_list[i + 1]
                          model_prev_list[i] = model_prev_list[i + 1]
                      t_prev_list[-1] = vec_t
                      # We do not need to evaluate the final model value.
                      if step < steps:
                          if model_x is None:
                              model_x = self.model_fn(x, vec_t)
                          model_prev_list[-1] = model_x
                      pbar.update()
      else:
          raise NotImplementedError()
      if denoise_to_zero:
          x = self.denoise_to_zero_fn(
              x, torch.ones((x.shape[0],)).to(device) * t_0
          )
      return x
  
imports: import torch
  import math
  import tqdm
label_desc: Use image node_id 13 for calling the UniPC obj's underlying member method code with example usage: obj.sample(x, steps, t_start, t_end, order, skip_type, method, lower_order_final, denoise_to_zero, solver_type, atol, rtol, corrector) and returns: x