code: def __init__(
      self, input_size, h, k, hidden_size, conv_hidden_size, num_kernels
  ):
      super(TimesBlock, self).__init__()
      self.input_size = input_size
      self.h = h
      self.k = k
      # parameter-efficient design
      self.conv = nn.Sequential(
          Inception_Block_V1(
              hidden_size, conv_hidden_size, num_kernels=num_kernels
          ),
          nn.GELU(),
          Inception_Block_V1(
              conv_hidden_size, hidden_size, num_kernels=num_kernels
          ),
      )
  
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 1 to create a new TimesBlock object from inherited base classes: nn with example: obj = TimesBlock(input_size, h, k, hidden_size, conv_hidden_size, num_kernels)