class_docstr: The original annealed Langevin dynamics predictor in NCSN/NCSNv2.
class_docstr_tok: ["The","original","annealed","Langevin","dynamics","predictor","in","NCSN\/NCSNv2","."]
code: def update_fn(self, x, t, *args):
      n_steps = self.n_steps
      target_snr = self.snr
      std = self.sde.marginal_prob(x, t, *args)[1]
  
      for _ in range(n_steps):
          grad = self.score_fn(x, t, *args)
          noise = torch.randn_like(x)
          step_size = (target_snr * std) ** 2 * 2
          x_mean = x + step_size[:, None, None, None] * grad
          x = (
              x_mean
              + noise * torch.sqrt(step_size * 2)[:, None, None, None]
          )
  
      return x, x_mean
  
file_path: espnet/espnet2/enh/diffusion/sampling/correctors.py
imports: import abc
  import torch
  import espnet2.enh.diffusion.sdes
label_desc: Use image node_id 2 for calling the AnnealedLangevinDynamics obj's underlying member method code with example usage: obj.update_fn(x, t) and returns: x,  x_mean