class_docstr: Construct Inception module. Concatenation after four branches (1x1x1 conv; 1x1x1 + 3x3x3 convs; 1x1x1 + 3x3x3
  convs; 3x3x3 max-pool + 1x1x1 conv). In `forward`, we check if SELayers are used, which are
  channel-wise (SELayerC), temporal-wise (SELayerT), channel-temporal-wise (SELayerTC & SELayerCT).
class_docstr_tok: ["Construct","Inception","module",".","Concatenation","after","four","branches","(","1x1x1","conv",";","1x1x1","+","3x3x3","convs",";","1x1x1","+","3x3x3","convs",";","3x3x3","max-pool","+","1x1x1","conv",")",".","In","`","forward","`",",","we","check","if","SELayers","are","used",",","which","are","channel-wise","(","SELayerC",")",",","temporal-wise","(","SELayerT",")",",","channel-temporal-wise","(","SELayerTC","&","SELayerCT",")","."]
code: def __init__(self, in_channels, out_channels, name):
      super(InceptionModule, self).__init__()
  
      self.b0 = Unit3D(
          in_channels=in_channels,
          output_channels=out_channels[0],
          kernel_shape=[1, 1, 1],
          padding=0,
          name=name + "/Branch_0/Conv3d_0a_1x1",
      )
      self.b1a = Unit3D(
          in_channels=in_channels,
          output_channels=out_channels[1],
          kernel_shape=[1, 1, 1],
          padding=0,
          name=name + "/Branch_1/Conv3d_0a_1x1",
      )
      self.b1b = Unit3D(
          in_channels=out_channels[1],
          output_channels=out_channels[2],
          kernel_shape=[3, 3, 3],
          name=name + "/Branch_1/Conv3d_0b_3x3",
      )
      self.b2a = Unit3D(
          in_channels=in_channels,
          output_channels=out_channels[3],
          kernel_shape=[1, 1, 1],
          padding=0,
          name=name + "/Branch_2/Conv3d_0a_1x1",
      )
      self.b2b = Unit3D(
          in_channels=out_channels[3],
          output_channels=out_channels[4],
          kernel_shape=[3, 3, 3],
          name=name + "/Branch_2/Conv3d_0b_3x3",
      )
      self.b3a = MaxPool3dSamePadding(
          kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=0
      )
      self.b3b = Unit3D(
          in_channels=in_channels,
          output_channels=out_channels[5],
          kernel_shape=[1, 1, 1],
          padding=0,
          name=name + "/Branch_3/Conv3d_0b_1x1",
      )
      self.name = name
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.hub import load_state_dict_from_url
label_desc: Use image node_id 1 to create a new InceptionModule object from inherited base classes: nn with example: obj = InceptionModule(in_channels, out_channels, name)