code: def forward(self, x, mode: str):
      if mode == "norm":
          self._get_statistics(x)
          x = self._normalize(x)
      elif mode == "denorm":
          x = self._denormalize(x)
      else:
          raise NotImplementedError
      return x
  
imports: import math
  import numpy
  from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from ..common._base_windows import BaseWindows
  from ..losses.pytorch import MAE
label_desc: Use image node_id 2 for calling the RevIN obj's underlying member method code with example usage: obj.forward(x, mode) and returns: x