code: def __call__(self, match_quality_matrix: Tensor) -> Tensor:
      matches = super().__call__(match_quality_matrix)
  
      # For each gt, find the prediction with which it has the highest quality
      _, highest_quality_pred_foreach_gt = match_quality_matrix.max(
          dim=1
      )
      matches[highest_quality_pred_foreach_gt] = torch.arange(
          highest_quality_pred_foreach_gt.size(0),
          dtype=torch.int64,
          device=highest_quality_pred_foreach_gt.device,
      )
  
      return matches
  
imports: import math
  from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from torchvision.ops import complete_box_iou_loss, distance_box_iou_loss, FrozenBatchNorm2d, generalized_box_iou_loss
label_desc: Use image node_id 2 for calling the SSDMatcher obj's underlying member method code with example usage: obj.__call__(match_quality_matrix) and returns: matches