code: def ConvBlock(kernel_size, filters, strides):
    
    ks = kernel_size
    filters1, filters2, filters3 = filters
    main = layers.Serial(
        layers.Conv(filters1, (1, 1), strides),
        layers.BatchNorm(),
        layers.Relu(),
        layers.Conv(filters2, (ks, ks), padding='SAME'),
        layers.BatchNorm(),
        layers.Relu(),
        layers.Conv(filters3, (1, 1)),
        layers.BatchNorm()
    )
    shortcut = layers.Serial(
        layers.Conv(filters3, (1, 1), strides),
        layers.BatchNorm()
    )
    return layers.Serial(
        layers.Branch(),
        layers.Parallel(main, shortcut),
        layers.SumBranches(),
        layers.Relu()
    )
docs: ResNet convolutional striding block.