torch.add() does not show up in the output of summary

[Rander2002]

torch.add() doesn't show up in the output of summary.
Is there any way to show all the correct sequential layers in the forward method of the Net?
My Net as follows.

`import torch
import torch.nn as nn
from math import sqrt
from torchsummary import summary

from torchkeras import summary

class Conv_ReLU_Block(nn.Module):
def init(self):
super(Conv_ReLU_Block, self).init()
self.conv = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)

def forward(self, x):
    return self.relu(self.conv(x))

class Net(nn.Module):
def init(self):
super(Net, self).init()
self.residual_layer = self.make_layer(Conv_ReLU_Block, 18)
self.input = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)
self.output = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)

    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            m.weight.data.normal_(0, sqrt(2. / n))
            
def make_layer(self, block, num_of_layer):
    layers = []
    for _ in range(num_of_layer):
        layers.append(block())
    return nn.Sequential(*layers)

def forward(self, x):
    residual = x
    out1 = self.relu(self.input(x))
    out2 = self.residual_layer(out1)
    out3 = self.output(out2)
    out = torch.add(out3, residual)
    return out

if name == 'main':
model = Net()

summary(model, input_size=(1, 33, 33))

`