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 forward(self, x):
      outputs = self._forward(x)
      out = torch.cat(outputs, dim=1)
  
      # Check if SELayer is used.
      if "SELayerC" in dir(self):  # check channel-wise
          out = self.SELayerC(out)
      if "SELayerCoC" in dir(self):
          out = self.SELayerCoC(out)
      if "SELayerMC" in dir(self):
          out = self.SELayerMC(out)
      if "SELayerMAC" in dir(self):
          out = self.SELayerMAC(out)
  
      if "SELayerT" in dir(self):  # check temporal-wise
          out = self.SELayerT(out)
  
      if "SELayerCTc" in dir(self):  # check channel-temporal-wise
          out = self.SELayerCTc(out)
      if "SELayerCTt" in dir(self):
          out = self.SELayerCTt(out)
  
      if "SELayerTCt" in dir(self):  # check temporal-channel-wise
          out = self.SELayerTCt(out)
      if "SELayerTCc" in dir(self):
          out = self.SELayerTCc(out)
  
      return out
  
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 3 for calling the InceptionModule obj's underlying member method code with example usage: obj.forward(x) and returns: out