code: def ancestral_step(self, x, sigma, next_sigma, sigma_up):
      x = torch.where(
          append_dims(next_sigma, x.ndim) > 0.0,
          x
          + self.noise_sampler(x)
          * self.s_noise
          * append_dims(sigma_up, x.ndim),
          x,
      )
      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 AncestralSampler obj's underlying member method code with example usage: obj.ancestral_step(x, sigma, next_sigma, sigma_up) and returns: x