code: def step(self, t0, t1, y0, extra0):
      f0, g0, z0 = extra0
      # f is a drift-like quantity
      # g is a diffusion-like quantity
      # z is a state-like quantity (like y)
      dt = t1 - t0
      dW = self.bm(t0, t1)
  
      z1 = 2 * y0 - z0 + f0 * dt + self.sde.prod(g0, dW)
      f1, g1 = self.sde.f_and_g(t1, z1)
      y1 = (
          y0 + (f0 + f1) * (0.5 * dt) + self.sde.prod(g0 + g1, 0.5 * dW)
      )
  
      return y1, (f1, g1, z1)
  
file_path: torchsde/torchsde/_core/methods/reversible_heun.py
imports: import torch
  from ..None import adjoint_sde
  from ..None import base_solver
  from ..None import misc
  from ...settings import SDE_TYPES, NOISE_TYPES, LEVY_AREA_APPROXIMATIONS, METHODS
label_desc: Use image node_id 3 for calling the ReversibleHeun obj's underlying member method code with example usage: obj.step(t0, t1, y0, extra0) and returns: y1,  f1,  g1,  z1