code: def get_sigmas_polyexponential(
      n, sigma_min, sigma_max, rho=1.0, device="cpu"
  ):
      """Constructs an polynomial in log sigma noise schedule."""
      ramp = torch.linspace(1, 0, n, device=device) ** rho
      sigmas = torch.exp(
          ramp * (math.log(sigma_max) - math.log(sigma_min))
          + math.log(sigma_min)
      )
      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 5 for calling a global function with example usage: get_sigmas_polyexponential(n, sigma_min, sigma_max, rho, device) and returns: append_zero