code: def update_fn(self, x, t, *args):
      target_snr = self.snr
      for _ in range(self.n_steps):
          grad = self.score_fn(x, t, *args)
          noise = torch.randn_like(x)
          grad_norm = torch.norm(
              grad.reshape(grad.shape[0], -1), dim=-1
          ).mean()
          noise_norm = torch.norm(
              noise.reshape(noise.shape[0], -1), dim=-1
          ).mean()
          step_size = (
              (target_snr * noise_norm / grad_norm) ** 2 * 2
          ).unsqueeze(0)
          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 LangevinCorrector obj's underlying member method code with example usage: obj.update_fn(x, t) and returns: x,  x_mean