code: def prepare_sampling_loop(self, x, cond, uc=None, num_steps=None):
      sigmas = self.discretization(
          self.num_steps if num_steps is None else num_steps,
          device=self.device,
      )
      uc = default(uc, cond)
  
      x *= torch.sqrt(1.0 + sigmas[0] ** 2.0)
      num_sigmas = len(sigmas)
  
      s_in = x.new_ones([x.shape[0]])
  
      return x, s_in, sigmas, num_sigmas, cond, uc
  
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 2 for calling the BaseDiffusionSampler obj's underlying member method code with example usage: obj.prepare_sampling_loop(x, cond, uc, num_steps) and returns: x,  s_in,  sigmas,  num_sigmas,  cond,  uc