class_docstr: Wraps score network with analytical sampling and cond. score computation.
  
  The variance preserving formulation in
      Score-Based Generative Modeling through Stochastic Differential Equations
      https://arxiv.org/abs/2011.13456
code: def __init__(
      self,
      denoiser,
      input_size=(1, 28, 28),
      t0=0.0,
      t1=1.0,
      beta_min=0.1,
      beta_max=20.0,
  ):
      super(ScoreMatchingSDE, self).__init__()
      if t0 > t1:
          raise ValueError(
              f"Expected t0 <= t1, but found t0={t0:.4f}, t1={t1:.4f}"
          )
  
      self.input_size = input_size
      self.denoiser = denoiser
  
      self.t0 = t0
      self.t1 = t1
  
      self.beta_min = beta_min
      self.beta_max = beta_max
  
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 1 to create a new ScoreMatchingSDE object from inherited base classes: Module with example: obj = ScoreMatchingSDE(denoiser, input_size, t0, t1, beta_min, beta_max)