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,
      )
      sigmas = sigmas[
          self.steps_subset[: self.n_sample_steps]
          + self.steps_subset[-1:]
      ]
      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
  
file_path: generative-models/scripts/demo/turbo.py
imports: from streamlit_helpers import *
  from st_keyup import st_keyup
  from sgm.modules.diffusionmodules.sampling import EulerAncestralSampler
label_desc: Use image node_id 2 for calling the SubstepSampler 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