code: def sde(self, x, t, y):
      drift = self.theta * (y - x)
      # the sqrt(2*logsig) factor is required here so that logsig does not in the end
      # affect the perturbation kernel standard deviation. this can be understood
      # from solving the integral of [exp(2s) * g(s)^2] from s=0 to t with
      # g(t) = sigma(t) as defined here, and seeing that `logsig` remains in the
      # integral solution unless this sqrt(2*logsig) factor is included.
      sigma = self.sigma_min * (self.sigma_max / self.sigma_min) ** t
      diffusion = sigma * np.sqrt(2 * self.logsig)
      return drift, diffusion
  
imports: import abc
  import warnings
  import numpy
  import torch
label_desc: Use image node_id 4 for calling the OUVESDE obj's underlying member method code with example usage: obj.sde(x, t, y) and returns: drift,  diffusion