the code is shown below:

``````class Net(nn.Module):

def __init__(self):
super(Net, self).__init__()
# 1 input image channel, 6 output channels, 5x5 square convolution
# kernel
self.conv1 = nn.Conv2d(1, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
# an affine operation: y = Wx + b
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)

def forward(self, x):
# Max pooling over a (2, 2) window
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
# If the size is a square you can only specify a single number
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x

def num_flat_features(self, x):
size = x.size()[1:]  # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
return num_features

net = Net()
print(net)
out = net(input)
``````

x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)), self.conv1 is an instance of nn.Conv2d defined in the __init__function, but how to understand the operation self.conv1(x) since conv1 has been initialized. As I know, self.conv1() is a operation initializing an instance.
The second problem is the same as above. “net” has been initialized in the code “net = Net()”, how to understand “net(input)”?

Calling a `nn.Module` like `self.conv1(x)` or `net(input)` calls the internal `__call__` method.
Have a look at the source. This method performs some operations regarding forward and backward hooks and calls `forward`.