def METHOD_NAME(self, outputs: Dict[str, Tensor], targets: List[Dict[str, Tensor]]):
    """Performs the matching
    Params:
        outputs: This is a dict that contains at least these entries:
             "pred_logits": Tensor of dim [batch_size, num_queries, num_classes]
                with the classification logits
             "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the
                predicted box coordinates
        targets: This is a list of targets (len(targets) = batch_size), where each
                target is a dict containing:
             "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is
                the number of ground-truth objects in the target) containing the
                class labels
             "boxes": Tensor of dim [num_target_boxes, 4] containing the target box
                coordinates
    Returns:
        A list of size batch_size, containing tuples of (index_i, index_j) where:
            - index_i is the indices of the selected predictions (in order)
            - index_j is the indices of the corresponding selected targets (in
              order)
        For each batch element, it holds:
            len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
    """
    bs, num_queries = outputs["pred_logits"].shape[:2]
    # We flatten to compute the cost matrices in a batch
    out_prob = self.norm(
        outputs["pred_logits"].flatten(0, 1)
    )  # [batch_size * num_queries, num_classes]
    out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]
    # Also concat the target labels and boxes
    tgt_ids = torch.cat([v["labels"] for v in targets])
    tgt_bbox = torch.cat([v["boxes"] for v in targets])
    # Compute the classification cost. Contrary to the loss, we don't use the NLL,
    # but approximate it in 1 - proba[target class].
    # The 1 is a constant that doesn't change the matching, it can be omitted.
    cost_class = -out_prob[:, tgt_ids]
    # Compute the L1 cost between boxes
    cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)
    # Compute the giou cost betwen boxes
    cost_giou = -generalized_box_iou(
        box_cxcywh_to_xyxy(out_bbox).float(), box_cxcywh_to_xyxy(tgt_bbox)
    )
    # Final cost matrix
    C = (
        self.cost_bbox * cost_bbox
        + self.cost_class * cost_class
        + self.cost_giou * cost_giou
    )
    C = C.view(bs, num_queries, -1).cpu()
    sizes = [len(v["boxes"]) for v in targets]
    indices = [
        linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))
    ]
    return [
        (
            torch.as_tensor(i, dtype=torch.int64),
            torch.as_tensor(j, dtype=torch.int64),
        )
        for i, j in indices
    ]