docstr: :param num_features: the number of features or channels
  :param eps: a value added for numerical stability
  :param affine: if True, RevIN has learnable affine parameters
docstr_tok: [":","param","num_features",":","the","number","of","features","or","channels",":","param","eps",":","a","value","added","for","numerical","stability",":","param","affine",":","if","True",",","RevIN","has","learnable","affine","parameters"]
code: def __init__(
      self,
      num_features: int,
      eps=1e-5,
      affine=True,
      subtract_last=False,
  ):
      """
      :param num_features: the number of features or channels
      :param eps: a value added for numerical stability
      :param affine: if True, RevIN has learnable affine parameters
      """
      super(RevIN, self).__init__()
      self.num_features = num_features
      self.eps = eps
      self.affine = affine
      self.subtract_last = subtract_last
      if self.affine:
          self._init_params()
  
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 1 to create a new RevIN object from inherited base classes: nn with example: obj = RevIN(num_features, eps, affine, subtract_last)