Assuming you have created both models, which accept the features with different dimensions, you could concatenate their outputs if their shapes allow to do so.
Here is a simple example:
class ModelA(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv1d(1, 1, 1)
        self.lin = nn.Linear(10, 10)
        
    def forward(self, x):
        x = self.conv(x)
        x = x.view(x.size(0), -1)
        x = self.lin(x)
        return x
    
class ModelB(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(1, 1, 1)
        self.lin = nn.Linear(10, 10)
        
    def forward(self, x):
        x = self.conv(x)
        x = x.view(x.size(0), -1)
        x = self.lin(x)
        return x

modelA = ModelA()
modelB = ModelB()
xA = torch.randn(1, 1, 10)
xB = torch.randn(1, 1, 5, 2)

outA = modelA(xA)
outB = modelB(xB)
out = torch.cat((outA, outB), dim=1)
print(out.shape)
> torch.Size([1, 20])