that other functions apart from linear and log_softmax exist in pytorch,

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)
```