class_docstr: A wrapper for discrete schedule DDPM models that output eps (the predicted
  noise).
class_docstr_tok: ["A","wrapper","for","discrete","schedule","DDPM","models","that","output","eps","(","the","predicted","noise",")","."]
code: def get_scalings(self, sigma):
      c_out = -sigma
      c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5
      return c_out, c_in
  
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 2 for calling the DiscreteEpsDDPMDenoiser obj's underlying member method code with example usage: obj.get_scalings(sigma) and returns: c_out,  c_in