In torchvision’s googlenet implementation, where is 1x1 convolution layer in the stem region.
  

      github.com
  

  
    pytorch/vision/blob/master/torchvision/models/googlenet.py#L92



      
      
          assert len(blocks) == 3
          conv_block = blocks[0]
          inception_block = blocks[1]
          inception_aux_block = blocks[2]
          
          
self.aux_logits = aux_logits
          self.transform_input = transform_input
          
          
self.conv1 = conv_block(3, 64, kernel_size=7, stride=2, padding=3)
          self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
          self.conv2 = conv_block(64, 64, kernel_size=1)
          self.conv3 = conv_block(64, 192, kernel_size=3, padding=1)
          self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
          
          
self.inception3a = inception_block(192, 64, 96, 128, 16, 32, 32)
          self.inception3b = inception_block(256, 128, 128, 192, 32, 96, 64)
          self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
          
          
self.inception4a = inception_block(480, 192, 96, 208, 16, 48, 64)
          self.inception4b = inception_block(512, 160, 112, 224, 24, 64, 64)
          self.inception4c = inception_block(512, 128, 128, 256, 24, 64, 64)
      
    
  


  

  
    
    
  

  


However, from this paper(https://static.googleusercontent.com/media/research.google.com/ko//pubs/archive/43022.pdf),
I cannot find any clue where this layer came from.
Is this an error?