Sorry for not being clear enough.
You could pass both tensors to the forward method and concatenate the activations as seen in this dummy code:
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv = nn.Conv2d(1, 1, 3, 1, 1)
        self.lin = nn.Linear(106, 2)
        
    def forward(self, x1, x2):
        x1 = self.conv(x1)
        x1 = x1.view(x1.size(0), -1)
        
        x = torch.cat((x1, x2), dim=1)
        x = self.lin(x)
        return x

model = MyModel()
x1 = torch.randn(24, 1, 10, 10)
x2 = torch.randn(24, 6)

out = model(x1, x2)