code: def forward(self, x):
      B, T, N = x.size()
      period_list, period_weight = FFT_for_Period(x, self.k)
  
      res = []
      for i in range(self.k):
          period = period_list[i]
          # padding
          if (self.input_size + self.h) % period != 0:
              length = (
                  ((self.input_size + self.h) // period) + 1
              ) * period
              padding = torch.zeros(
                  [
                      x.shape[0],
                      (length - (self.input_size + self.h)),
                      x.shape[2],
                  ]
              ).to(x.device)
              out = torch.cat([x, padding], dim=1)
          else:
              length = self.input_size + self.h
              out = x
          # reshape
          out = (
              out.reshape(B, length // period, period, N)
              .permute(0, 3, 1, 2)
              .contiguous()
          )
          # 2D conv: from 1d Variation to 2d Variation
          out = self.conv(out)
          # reshape back
          out = out.permute(0, 2, 3, 1).reshape(B, -1, N)
          res.append(out[:, : (self.input_size + self.h), :])
      res = torch.stack(res, dim=-1)
      # adaptive aggregation
      period_weight = F.softmax(period_weight, dim=1)
      period_weight = (
          period_weight.unsqueeze(1).unsqueeze(1).repeat(1, T, N, 1)
      )
      res = torch.sum(res * period_weight, -1)
      # residual connection
      res = res + x
      return res
  
imports: from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.fft
  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 TimesBlock obj's underlying member method code with example usage: obj.forward(x) and returns: res