Sure! Here is a small example concatenating the outputs of two linear layers:
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.features1 = nn.Sequential(
            nn.Conv2d(3, 6, 3, 1, 1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(6, 12, 3, 1, 1),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        
        self.features2 = nn.Sequential(
            nn.Conv2d(1, 6, 3, 1, 1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(6, 12, 3, 1, 1),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        
        self.fc1 = nn.Linear(12*6*6, 64)
        self.fc2 = nn.Linear(12*6*6, 64)
        
        self.fc_out = nn.Linear(128, 10)
        
    def forward(self, x1, x2):
        x1 = self.features1(x1)
        x1 = x1.view(x1.size(0), -1)
        x1 = F.relu(self.fc1(x1))
        
        x2 = self.features2(x2)
        x2 = x2.view(x2.size(0), -1)
        x2 = F.relu(self.fc2(x2))

        # Concatenate in dim1 (feature dimension)
        x = torch.cat((x1, x2), 1)
        x = self.fc_out(x)
        return x

model = MyModel()
x1 = torch.randn(2, 3, 24, 24)
x2 = torch.randn(2, 1, 24, 24)
output = model(x1, x2)

Note that you could also concatenate/sum/average the outputs of the features modules.
I’m not sure what would work best in your use case, so you might want to play around a bit.