Thank you for your reply.
I’m calling xavier_init as follows:
def xavier_init(ms):
    for m in ms :
        if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
            nn.init.xavier_uniform(m.weight,gain=nn.init.calculate_gain('relu'))
            m.bias.data.zero_()


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 64
        
        
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)

      
    
    
    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)


    def forward(self, x, lin=0, lout=5):
        out = x
        if lin < 1 and lout > -1:
            out = self.conv1(out)
            out = self.bn1(out)
            out = F.relu(out)
        if lin < 2 and lout > 0:
            out = self.layer1(out)
        if lin < 3 and lout > 1:
            out = self.layer2(out)
        if lin < 4 and lout > 2:
            out = self.layer3(out)
        if lin < 5 and lout > 3:
            out = self.layer4(out)
        if lout > 4:
            out = F.avg_pool2d(out, 4)
            out = out.view(out.size(0), -1)
            out = self.linear(out)  
        return out   
    
   
    def weight_init(self):
        for m in self._modules:
            xavier_init(self._modules[m])

and in the main code, I’m calling resnet and weight_init as follows:
net = models.__dict__[args.model](num_classes)
net.weight_init() 

What should I add as an argument for (net.weight_init())?