code: def sampler_step(
      self,
      old_denoised,
      previous_sigma,
      sigma,
      next_sigma,
      denoiser,
      x,
      cond,
      uc=None,
  ):
      denoised = self.denoise(x, denoiser, sigma, cond, uc)
  
      h, r, t, t_next = self.get_variables(
          sigma, next_sigma, previous_sigma
      )
      mult = [
          append_dims(mult, x.ndim)
          for mult in self.get_mult(h, r, t, t_next, previous_sigma)
      ]
  
      x_standard = mult[0] * x - mult[1] * denoised
      if old_denoised is None or torch.sum(next_sigma) < 1e-14:
          # Save a network evaluation if all noise levels are 0 or on the first step
          return x_standard, denoised
      else:
          denoised_d = mult[2] * denoised - mult[3] * old_denoised
          x_advanced = mult[0] * x - mult[1] * denoised_d
  
          # apply correction if noise level is not 0 and not first step
          x = torch.where(
              append_dims(next_sigma, x.ndim) > 0.0,
              x_advanced,
              x_standard,
          )
  
      return x, denoised
  
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 DPMPP2MSampler obj's underlying member method code with example usage: obj.sampler_step(old_denoised, previous_sigma, sigma, next_sigma, denoiser, x, cond, uc) and returns: x,  denoised, x_standard,  denoised