code: def update_fn(self, x, t, *args):
      f, g = self.rsde.discretize(x, t, *args)
      z = torch.randn_like(x)
      x_mean = x - f
      x = x_mean + g[:, None, None, None] * z
      return x, x_mean
  
file_path: espnet/espnet2/enh/diffusion/sampling/predictors.py
imports: import abc
  import numpy
  import torch
label_desc: Use image node_id 2 for calling the ReverseDiffusionPredictor obj's underlying member method code with example usage: obj.update_fn(x, t) and returns: x,  x_mean