code: def possible_correction_step(
      self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc
  ):
      if torch.sum(next_sigma) < 1e-14:
          # Save a network evaluation if all noise levels are 0
          return euler_step
      else:
          denoised = self.denoise(
              euler_step, denoiser, next_sigma, cond, uc
          )
          d_new = to_d(euler_step, next_sigma, denoised)
          d_prime = (d + d_new) / 2.0
  
          # apply correction if noise level is not 0
          x = torch.where(
              append_dims(next_sigma, x.ndim) > 0.0,
              x + d_prime * dt,
              euler_step,
          )
          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 1 for calling the HeunEDMSampler obj's underlying member method code with example usage: obj.possible_correction_step(euler_step, x, d, dt, next_sigma, denoiser, cond, uc) and returns: euler_step, x