(self, hidden)
return torch.stack(distributions).mean(0)
Compute a distribution over the target dictionary
        by averaging distributions from models in the ensemble.
        All models in the ensemble must share a target vocabulary.
Compute a distribution over the target dictionary
        by averaging distributions from models in the ensemble.
        All models in the ensemble must share a target vocabulary.
Compute a distribution over the target dictionary by averaging distributions from models in the ensemble . All models in the ensemble must share a target vocabulary .
def forward(self, hidden):
        """
        Compute a distribution over the target dictionary
        by averaging distributions from models in the ensemble.
        All models in the ensemble must share a target vocabulary.
        """
        distributions = [model_generator.forward(hidden[i])
                         for (i, model_generator)
                         in enumerate(self.model_generators)]
        return torch.stack(distributions).mean(0)
def forward ( self , hidden ) : distributions = [ model_generator . forward ( hidden [ i ] ) for ( i , model_generator ) in enumerate ( self . model_generators ) ] return torch . stack ( distributions ) . mean ( 0 )