class_docstr: Stochastic Lorenz attractor.
  
  Used for simulating ground truth and obtaining noisy data.
  Details described in Section 7.2 https://arxiv.org/pdf/2001.01328.pdf
  Default a, b from https://openreview.net/pdf?id=HkzRQhR9YX
class_docstr_tok: ["Stochastic","Lorenz","attractor",".","Used","for","simulating","ground","truth","and","obtaining","noisy","data",".","Details","described","in","Section","7.2","https",":","\/\/arxiv.org\/pdf\/2001.01328.pdf","Default","a",",","b","from","https",":","\/\/openreview.net\/pdf","?","id=HkzRQhR9YX"]
code: def g(self, t, y):
      x1, x2, x3 = torch.split(
          y, split_size_or_sections=(1, 1, 1), dim=1
      )
      b1, b2, b3 = self.b
  
      g1 = x1 * b1
      g2 = x2 * b2
      g3 = x3 * b3
      return torch.cat([g1, g2, g3], dim=1)
  
imports: import logging
  import os
  from typing import Sequence
  import fire
  import matplotlib.gridspec
  import matplotlib.pyplot
  import numpy
  import torch
  import tqdm
  from torch import nn
  from torch import optim
  from torch.distributions import Normal
  import torchsde
label_desc: Use image node_id 3 for calling the StochasticLorenz obj's underlying member method code with example usage: obj.g(t, y) and returns: torch