code: def forward(self, outputs, batch_gt_instances):
      with torch.no_grad():
          B, N, H, W = outputs["pred_masks"].shape
          pred_masks = outputs["pred_masks"]
          pred_logits = outputs["pred_logits"].sigmoid()
          device = pred_masks.device
  
          tgt_ids = torch.cat([gt.labels for gt in batch_gt_instances])
  
          if tgt_ids.shape[0] == 0:
              return [
                  (
                      torch.as_tensor([]).to(pred_logits),
                      torch.as_tensor([]).to(pred_logits),
                  )
              ] * B
          tgt_masks = torch.cat(
              [
                  gt.masks.to_tensor(
                      dtype=pred_masks.dtype, device=device
                  )
                  for gt in batch_gt_instances
              ]
          )
  
          tgt_masks = F.interpolate(
              tgt_masks[:, None],
              size=pred_masks.shape[-2:],
              mode="bilinear",
              align_corners=False,
          ).squeeze(1)
  
          pred_masks = pred_masks.view(B * N, -1)
          tgt_masks = tgt_masks.flatten(1)
          with autocast(enabled=False):
              pred_masks = pred_masks.float()
              tgt_masks = tgt_masks.float()
              pred_logits = pred_logits.float()
              mask_score = self.mask_score(pred_masks, tgt_masks)
              # Nx(Number of gts)
              matching_prob = pred_logits.view(B * N, -1)[:, tgt_ids]
              C = (mask_score**self.alpha) * (
                  matching_prob**self.beta
              )
  
          C = C.view(B, N, -1).cpu()
          # hungarian matching
          sizes = [len(gt.masks) for gt in batch_gt_instances]
          indices = [
              linear_sum_assignment(c[i], maximize=True)
              for i, c in enumerate(C.split(sizes, -1))
          ]
          indices = [
              (
                  torch.as_tensor(i, dtype=torch.int64),
                  torch.as_tensor(j, dtype=torch.int64),
              )
              for i, j in indices
          ]
          return indices
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from scipy.optimize import linear_sum_assignment
  from torch.cuda.amp import autocast
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.utils import reduce_mean
label_desc: Use image node_id 2 for calling the SparseInstMatcher obj's underlying member method code with example usage: obj.forward(outputs, batch_gt_instances) and returns: indices, unknown