code: def sampler_step(
      self, sigma, next_sigma, denoiser, x, cond, uc=None, **kwargs
  ):
      sigma_down, sigma_up = get_ancestral_step(
          sigma, next_sigma, eta=self.eta
      )
      denoised = self.denoise(x, denoiser, sigma, cond, uc)
      x_euler = self.ancestral_euler_step(
          x, denoised, sigma, sigma_down
      )
  
      if torch.sum(sigma_down) < 1e-14:
          # Save a network evaluation if all noise levels are 0
          x = x_euler
      else:
          h, s, t, t_next = self.get_variables(sigma, sigma_down)
          mult = [
              append_dims(mult, x.ndim)
              for mult in self.get_mult(h, s, t, t_next)
          ]
  
          x2 = mult[0] * x - mult[1] * denoised
          denoised2 = self.denoise(x2, denoiser, to_sigma(s), cond, uc)
          x_dpmpp2s = mult[2] * x - mult[3] * denoised2
  
          # apply correction if noise level is not 0
          x = torch.where(
              append_dims(sigma_down, x.ndim) > 0.0, x_dpmpp2s, x_euler
          )
  
      x = self.ancestral_step(x, sigma, next_sigma, sigma_up)
      return x
  
imports: from typing import Dict, Union
  import torch
  from omegaconf import ListConfig, OmegaConf
  from tqdm import tqdm
  from ...modules.diffusionmodules.sampling_utils import get_ancestral_step, linear_multistep_coeff, to_d, to_neg_log_sigma, to_sigma
  from ...util import append_dims, default, instantiate_from_config
label_desc: Use image node_id 3 for calling the DPMPP2SAncestralSampler obj's underlying member method code with example usage: obj.sampler_step(sigma, next_sigma, denoiser, x, cond, uc) and returns: x