def forward(self, outputs, targets):
        """ This performs the loss computation.
        Parameters:
             outputs: dict of tensors, see the output specification of the model for the format
             targets: list of dicts, such that len(targets) == batch_size.
                      The expected keys in each dict depends on the losses applied, see each loss' doc
        """
        outputs_without_aux = {
            k: v
            for k, v in outputs.items() if k != 'aux_outputs'
        }

        # Retrieve the matching between the outputs of the last layer and the targets
        indices = self.matcher(outputs_without_aux, targets)

        # Compute the average number of target boxes across all nodes, for normalization purposes
        num_boxes = sum(len(t["labels"]) for t in targets)
        num_boxes = paddle.to_tensor(
            [num_boxes],
            dtype="float32",
            place=next(iter(outputs.values())).place)

        # Compute all the requested losses
        losses = {}
        for loss in self.losses:
            losses.update(
                self.get_loss(loss, outputs, targets, indices, num_boxes))

        # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
        if 'aux_outputs' in outputs:
            for i, aux_outputs in enumerate(outputs['aux_outputs']):
                indices = self.matcher(aux_outputs, targets)
                for loss in self.losses:
                    kwargs = {}
                    if loss == 'labels':
                        # Logging is enabled only for the last layer
                        kwargs = {'log': False}
                    l_dict = self.get_loss(loss, aux_outputs, targets, indices,
                                           num_boxes, **kwargs)

                    w_dict = {}
                    for k in l_dict.keys():
                        if k in self.weight_dict:
                            w_dict[k + f'_{i}'] = l_dict[k] * self.weight_dict[
                                k]
                        else:
                            w_dict[k + f'_{i}'] = l_dict[k]
                    losses.update(w_dict)

        return losses
 This performs the loss computation.
        Parameters:
             outputs: dict of tensors, see the output specification of the model for the format
             targets: list of dicts, such that len(targets) == batch_size.
                      The expected keys in each dict depends on the losses applied, see each loss' doc
        
ppdet/modeling/losses/sparsercnn_loss.py