This is the ugly hack i created that works for this problem. 16 outputs with the first output being weighted 8/16ths and the remaining outputs weighted 0.5/15.
I’m sure there’s a better way to do this but if you’re in a hurry this works.
def weighted_mse_loss(input,target):
    #alpha of 0.5 means half weight goes to first, remaining half split by remaining 15
    weights = Variable(torch.Tensor([0.5,0.5/15,0.5/15,0.5/15,0.5/15,0.5/15,0.5/15,0.5/15,0.5/15,0.5/15,0.5/15,0.5/15,0.5/15,0.5/15,0.5/15,0.5/15])).cuda()  
    pct_var = (input-target)**2
    out = pct_var * weights.expand_as(target)
    loss = out.mean() 
    return loss