def METHOD_NAME(self, input_size):
    self.additional_blocks = []
    for i, (input_size, output_size, channels) in enumerate(zip(input_size[:-1], input_size[1:], [256, 256, 128, 128, 128])):
        if i < 3:
            layer = nn.Sequential(
                nn.Conv2d(input_size, channels, kernel_size=1, bias=False),
                nn.BatchNorm2d(channels),
                nn.ReLU(inplace=True),
                nn.Conv2d(channels, output_size, kernel_size=3, padding=1, stride=2, bias=False),
                nn.BatchNorm2d(output_size),
                nn.ReLU(inplace=True),
            )
        else:
            layer = nn.Sequential(
                nn.Conv2d(input_size, channels, kernel_size=1, bias=False),
                nn.BatchNorm2d(channels),
                nn.ReLU(inplace=True),
                nn.Conv2d(channels, output_size, kernel_size=3, bias=False),
                nn.BatchNorm2d(output_size),
                nn.ReLU(inplace=True),
            )
        self.additional_blocks.append(layer)
    self.additional_blocks = nn.ModuleList(self.additional_blocks)