In that case you could write a custom model and use these two inputs directly in the forward method.
Here is a small example:
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(10, 10)
        self.fc2 = nn.Linear(10, 10)
        
    def forward(self, x1, x2):
        x1 = self.fc1(x1)
        x2 = self.fc2(x2)
        x = x1 + x2
        return x

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