that other functions can use apart nn.Linear and log_softmax in  the class ConvolutionalNetwork
How to configure in this function:
class ConvolutionalNetwork(nn.Module):
        def __init__(self):
            super().__init__()
            self.conv1 = nn.Conv2d(tipo_image, 6, kernel_size=(kernel,kernel), stride=stridee,padding=paddingg,bias=biass)
            self.conv2 = nn.Conv2d(6, 16, kernel_size=(kernel,kernel), stride=stridee,padding=paddingg,bias=biass)
            self.conv3 = nn.Conv2d(16, 32, kernel_size=(kernel,kernel), stride=stridee,padding=paddingg,bias=biass)

        
            self.fc1 = nn.Linear(f*f*32, 120)
            self.fc2 = nn.Linear(120, 84)
            self.fc3 = nn.Linear(84, 18)

        def forward(self, X):
            X = F.relu(self.conv1(X))
            X = F.max_pool2d(X, 2, 2)
            X = F.relu(self.conv2(X))
            X = F.max_pool2d(X, 2, 2)
            X = F.relu(self.conv3(X))
            X = F.max_pool2d(X, 2, 2)
            X = X.view(-1, f*f*32)
            X = F.relu(self.fc1(X))
            X = F.relu(self.fc2(X))
            X = self.fc3(X)
            return F.log_softmax(X, dim=1)
    torch.manual_seed(101)
    CNNmodel = ConvolutionalNetwork()
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(CNNmodel.parameters(), lr=0.001)
    print(CNNmodel)