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 t_to_sigma(self, t):
      t = t.float()
      low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
      log_sigma = (1 - w) * self.log_sigmas[
          low_idx
      ] + w * self.log_sigmas[high_idx]
      return log_sigma.exp()
  
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 the DiscreteSchedule obj's underlying member method code with example usage: obj.t_to_sigma(t) and returns: log_sigma