Well, it’s something like this:
out_tensor1, out_tensor2, out_tensor3 = tf.cond(some_condition,
                         lambda: some_tensor1, some_tensor2, some_tensor3,
                         lambda: some_tensor4, some_tensor5, some_tensor6)

Currently, I am using a workaround I found on another thread:
def where(cond, f1, f2):
    return c * f1() + (1-c) * f2()

and finding all 3 out_tensors seperately. Though having an inbuilt operation in pytorch would be useful.