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 forward(self, input, sigma, **kwargs):
      c_out, c_in = [
          append_dims(x, input.ndim) for x in self.get_scalings(sigma)
      ]
      eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
      return input + eps * c_out
  
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 5 for calling the DiscreteEpsDDPMDenoiser obj's underlying member method code with example usage: obj.forward(input, sigma) and returns: unknown