Multiple GPUs with user defined functions

I am trying to use multiple GPUs for a deep neural network. When I restrict to a neural network with forward and backward functions, torch nn dataparallel works fine. However, I would like to include one more function in the neural network like this

class MyNN(nn.module):
def __init__(self):
    self.layer = nn.Linear(10, 20)
def forward(self, x):
    x = self.block(x)
    return x
def evaluate(self,x):
   return some_scalar_value(x,self)

The function evaluate returns some scalar value. I am wondering how to average over the values obtained by multiple GPUs? If I had to simply do forward and backward pass, I could simply use

model = MyNN()
model = torch.nn.Dataparallel(model,device_ids)

i have the same problema, how solve it?

eval_model = MyNN()
train_model = torch.nn.Dataparallel(eval_model,device_ids)

Use eval_model for evaluation and train_model for training. The parameters are shared.

Please check my solution above for reference.