code: def get_sigmas_vp(
      n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device="cpu"
  ):
      """Constructs a continuous VP noise schedule."""
      t = torch.linspace(1, eps_s, n, device=device)
      sigmas = torch.sqrt(
          torch.exp(beta_d * t**2 / 2 + beta_min * t) - 1
      )
      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 6 for calling a global function with example usage: get_sigmas_vp(n, beta_d, beta_min, eps_s, device) and returns: append_zero