code: def __init__(self, eta=1.0, s_noise=1.0, *args, **kwargs):
      super().__init__(*args, **kwargs)
  
      self.eta = eta
      self.s_noise = s_noise
      self.noise_sampler = lambda x: torch.randn_like(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 to create a new AncestralSampler object from inherited base classes: SingleStepDiffusionSampler with example: obj = AncestralSampler(eta, s_noise)