def dense_block(self, input_channels):
    return torch.nn.Sequential(torch.nn.Conv2d(input_channels, 128, kernel_size=1, bias=False), torch.nn.BatchNorm2d(128), torch.nn.ReLU(inplace=True), torch.nn.Conv2d(128, 32, kernel_size=3, padding=1, bias=False), torch.nn.BatchNorm2d(32), torch.nn.ReLU(inplace=True))
def dense_block(self, input_channels):
    return torch.nn.Sequential(torch.nn.Conv2d(input_channels, 128, kernel_size=1, bias=False), torch.nn.BatchNorm2d(128), torch.nn.ReLU(inplace=True), torch.nn.Conv2d(128, 32, kernel_size=3, padding=1, bias=False), torch.nn.BatchNorm2d(32), torch.nn.ReLU(inplace=True))