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 = torch.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 math
  import torch
  import torchsde
  from scipy import integrate
  from torch import nn
  from torchdiffeq import odeint
  from tqdm.auto import tqdm, trange
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