code: def get_ancestral_step(sigma_from, sigma_to):
      """Calculates the noise level (sigma_down) to step down to and the amount
      of noise to add (sigma_up) when doing an ancestral sampling step.
      """
      sigma_up = (
          sigma_to**2
          * (sigma_from**2 - sigma_to**2)
          / sigma_from**2
      ) ** 0.5
      sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
      return sigma_down, sigma_up
  
imports: import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from piq import LPIPS
  from torchvision.transforms import RandomCrop
  from .None import dist_util
  from .nn import mean_flat, append_dims, append_zero
  from .random_util import get_generator
label_desc: Use image node_id 5 for calling a global function with example usage: get_ancestral_step(sigma_from, sigma_to) and returns: sigma_down,  sigma_up