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 __init__(self, sigmas, quantize):
      super().__init__()
      self.register_buffer("sigmas", sigmas)
      self.register_buffer("log_sigmas", sigmas.log())
      self.quantize = quantize
  
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 1 to create a new DiscreteSchedule object from inherited base classes: nn with example: obj = DiscreteSchedule(sigmas, quantize)