code: def get_sigmas_exponential(n, sigma_min, sigma_max, device="cpu"):
      """Constructs an exponential noise schedule."""
      sigmas = torch.linspace(
          math.log(sigma_max), math.log(sigma_min), n, device=device
      ).exp()
      return append_zero(sigmas)
  
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 4 for calling a global function with example usage: get_sigmas_exponential(n, sigma_min, sigma_max, device) and returns: append_zero