class_docstr: A mapping between continuous noise levels (sigmas) and a list of discrete noise
  levels.
class_docstr_tok: ["A","mapping","between","continuous","noise","levels","(","sigmas",")","and","a","list","of","discrete","noise","levels","."]
code: def get_sigmas(self, n=None):
      if n is None:
          return append_zero(self.sigmas.flip(0))
      t_max = len(self.sigmas) - 1
      t = torch.linspace(t_max, 0, n, device=self.sigmas.device)
      return append_zero(self.t_to_sigma(t))
  
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 the DiscreteSchedule obj's underlying member method code with example usage: obj.get_sigmas(n) and returns: append_zero, append_zero