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 analytical_score(self, x_t, t, x_t0):
      mean = self.analytical_mean(t, x_t0)
      var = self.analytical_var(t, x_t0)
      return -(x_t - mean) / fill_tail_dims(var, mean).clamp_min(1e-5)
  
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 8 for calling the ScoreMatchingSDE obj's underlying member method code with example usage: obj.analytical_score(x_t, t, x_t0) and returns: unknown