I am trying to block the gradient of the ResNet shortcut (i.e., residual in the following code, when self.downsample is None, which means residual = x ). If the self.downsample is not None, I can register a backward hook function block_grad(self, grad_input, grad_output) to self.downsample layer  and change the gradient of grad_input[0] = 0. However, when self.downsample is None, I cannot register backward hook function to the layer anymore. So, my question is how to block the gradient in this case?
   class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out