class_docstr: Special designed layernorm for the seasonal part
class_docstr_tok: ["Special","designed","layernorm","for","the","seasonal","part"]
code: def forward(self, x):
      x_hat = self.layernorm(x)
      bias = (
          torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1)
      )
      return x_hat - bias
  
imports: import numpy
  from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from ..common._modules import DataEmbedding
  from ..common._base_windows import BaseWindows
  from ..losses.pytorch import MAE
label_desc: Use image node_id 2 for calling the LayerNorm obj's underlying member method code with example usage: obj.forward(x) and returns: unknown