weight_norm layer prevents "Kernel Shape" output

[helion-du-mas-des-bourboux-thales]

weight_norm layer prevents "Kernel Shape" output
Taking the example from the README

from torchsummaryX import summary 
import torch 
import torch.nn as nn 
import torch.nn.functional as F 
 
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

        self.conv2 = torch.nn.utils.weight_norm(self.conv2) 

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)
summary(Net(), torch.zeros((1, 1, 28, 28)))

returns the following

================================================================
               Kernel Shape     Output Shape  Params Mult-Adds
Layer                                                         
0_conv1       [1, 10, 5, 5]  [1, 10, 24, 24]   260.0    144.0k
1_conv2                   -    [1, 20, 8, 8]   5.04k     5.02k
2_conv2_drop              -    [1, 20, 8, 8]       -         -
3_fc1             [320, 50]          [1, 50]  16.05k     16.0k
4_fc2              [50, 10]          [1, 10]   510.0     500.0
----------------------------------------------------------------
                       Totals
Total params           21.86k
Trainable params       21.86k
Non-trainable params      0.0
Mult-Adds             165.52k
================================================================

But it should return the following instead:

================================================================
               Kernel Shape     Output Shape  Params Mult-Adds
Layer                                                         
0_conv1       [1, 10, 5, 5]  [1, 10, 24, 24]   260.0    144.0k
1_conv2       [10, 20, 5, 5]   [1, 20, 8, 8]   5.04k     5.02k
2_conv2_drop              -    [1, 20, 8, 8]       -         -
3_fc1             [320, 50]          [1, 50]  16.05k     16.0k
4_fc2              [50, 10]          [1, 10]   510.0     500.0
----------------------------------------------------------------
                       Totals
Total params           21.86k
Trainable params       21.86k
Non-trainable params      0.0
Mult-Adds             165.52k
================================================================