I tried implementing the above model word for word like this:
class TargetA(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(64, 5, 5)
        self.conv2 = nn.Conv2d(64, 5, 5)
        self.drop1 = nn.Dropout(0.25)
        self.fc1 = nn.Linear(128)
        self.drop2 = nn.Dropout(0.5)
        self.fc2 = nn.Linear()

    def forward(self, x):
        x = self.conv1(x)
        x = nn.ReLU(x)
        x = self.conv2(x)
        x = nn.ReLU(x)
        x = nn.Dropout(0.25)(x)
        x = self.fc1(x)
        x = nn.ReLU(x)
        x = nn.Dropout(0.5)(x)
        x = self.fc2(x)
        x = nn.Softmax(x)
        return x


line 51, in init
self.fc1 = nn.Linear()
TypeError: init() missing 2 required positional arguments: ‘in_features’ and ‘out_features’
It doesn’t like how I’m doing the Linear layers.  Any help on how to implement the above photo?