I guess it’s like a resnet module:
class ConcatMod(nn.Module):
    def __init__(self, args):
        # Use args properly to set conv params here depending on your application
        self.conv1 = nn.Conv3d(32, 64, kernel_size=3, stride=1)
        self.conv1_norm = nn.BatchNorm3d(64)
        self.conv1_relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv3d(64, 128, kernel_size=3, stride=1)

    def forward(self, input):
        out1 = self.conv1(input)
        out1_renormed = self.conv1_relu(self.conv1_norm(out1))
        out2 = self.conv2(out1_renormed)
        output = torch.cat([out1, out2], 1)
        return output

# In your Sequential, you can do
self.add_module("concat1", ConcatMod(args))