code: def __init__(
      self, in_channels, out_channels, num_kernels=6, init_weight=True
  ):
      super(Inception_Block_V1, self).__init__()
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.num_kernels = num_kernels
      kernels = []
      for i in range(self.num_kernels):
          kernels.append(
              nn.Conv2d(
                  in_channels,
                  out_channels,
                  kernel_size=2 * i + 1,
                  padding=i,
              )
          )
      self.kernels = nn.ModuleList(kernels)
      if init_weight:
          self._initialize_weights()
  
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 Inception_Block_V1 object from inherited base classes: nn with example: obj = Inception_Block_V1(in_channels, out_channels, num_kernels, init_weight)