class_docstr: Wrapper of the score network for odeint/sdeint.
  
  We split this module out, so that `forward` of the score network is solely
  used for computing the score, and the `forward` here is used for odeint.
  Helps with data parallel.
code: def sde_sample_final(
      self, batch_size=64, tau=1.0, t=None, y=None, dt=1e-2
  ):
      return self.sde_sample(batch_size, tau, t, y, dt)[-1]
  
imports: import abc
  import logging
  import math
  import os
  import fire
  import torch
  import torchdiffeq
  import torchvision
  import tqdm
  from torch import nn, optim
  from torch.utils import data
  import torchsde
  from .None import unet
label_desc: Use image node_id 9 for calling the ReverseDiffeqWrapper obj's underlying member method code with example usage: obj.sde_sample_final(batch_size, tau, t, y, dt) and returns: self