code: def get_sigmas_karras(n, sigma_min, sigma_max, rho=7.0, device="cpu"):
      """Constructs the noise schedule of Karras et al. (2022)."""
      ramp = th.linspace(0, 1, n)
      min_inv_rho = sigma_min ** (1 / rho)
      max_inv_rho = sigma_max ** (1 / rho)
      sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return append_zero(sigmas).to(device)
  
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 3 for calling a global function with example usage: get_sigmas_karras(n, sigma_min, sigma_max, rho, device) and returns: append_zero