This you can easily do. Just get the output from two tasks. Calculate loss for them separately. While backpropagating, sum them and do. If the tasks are related it is good to do joint training. It helps in performance improvement. Even if the tasks are no related, you can see if there is any learning happening.  In some cases, example one result being a subset of other. Both the tasks may look redundant to us. But having multi task learning with parallel related tasks does help in performance improvement.