Args:
            output (Tensor): NxK tensor that for each of the N examples
                indicates the probability of the example belonging to each of
                the K classes, according to the model. The probabilities should
                sum to one over all classes
            target (Tensor): binary NxK tensort that encodes which of the K
                classes are associated with the N-th input
                    (eg: a row [0, 1, 0, 1] indicates that the example is
                         associated with classes 2 and 4)
            weight (optional, Tensor): Nx1 tensor representing the weight for
                each example (each weight > 0)

def add(self, output, target):
        """
        Args:
            output (Tensor): NxK tensor that for each of the N examples
                indicates the probability of the example belonging to each of
                the K classes, according to the model. The probabilities should
                sum to one over all classes
            target (Tensor): binary NxK tensort that encodes which of the K
                classes are associated with the N-th input
                    (eg: a row [0, 1, 0, 1] indicates that the example is
                         associated with classes 2 and 4)
            weight (optional, Tensor): Nx1 tensor representing the weight for
                each example (each weight > 0)
        """
        if not torch.is_tensor(output):
            output = torch.from_numpy(output)
        if not torch.is_tensor(target):
            target = torch.from_numpy(target)

        if output.dim() == 1:
            output = output.view(-1, 1)
        else:
            assert output.dim() == 2, \
                'wrong output size (should be 1D or 2D with one column \
                per class)'
        if target.dim() == 1:
            target = target.view(-1, 1)
        else:
            assert target.dim() == 2, \
                'wrong target size (should be 1D or 2D with one column \
                per class)'
        if self.scores.numel() > 0:
            assert target.size(1) == self.targets.size(1), \
                'dimensions for output should match previously added examples.'

        # make sure storage is of sufficient size
        if self.scores.storage().size() < self.scores.numel() + output.numel():
            new_size = math.ceil(self.scores.storage().size() * 1.5)
            self.scores.storage().resize_(int(new_size + output.numel()))
            self.targets.storage().resize_(int(new_size + output.numel()))

        # store scores and targets
        offset = self.scores.size(0) if self.scores.dim() > 0 else 0
        self.scores.resize_(offset + output.size(0), output.size(1))
        self.targets.resize_(offset + target.size(0), target.size(1))
        self.scores.narrow(0, offset, output.size(0)).copy_(output)
        self.targets.narrow(0, offset, target.size(0)).copy_(target)