code: def _std(self, t):
      # This is a full solution to the ODE for P(t) in our derivations,
      # after choosing g(s) as in self.sde()
      sigma_min, theta, logsig = self.sigma_min, self.theta, self.logsig
      # could maybe replace the two torch.exp(... * t) terms here by cached values **t
      return torch.sqrt(
          (
              sigma_min**2
              * torch.exp(-2 * theta * t)
              * (torch.exp(2 * (theta + logsig) * t) - 1)
              * logsig
          )
          / (theta + logsig)
      )
  
imports: import abc
  import warnings
  import numpy
  import torch
label_desc: Use image node_id 6 for calling the OUVESDE obj's underlying member method code with example usage: obj._std(t) and returns: torch