code: def multiclass_nms(
      bboxs, num_classes, match_threshold=0.6, match_metric="iou"
  ):
      final_boxes = []
      for c in range(num_classes):
          idxs = bboxs[:, 0] == c
          if np.count_nonzero(idxs) == 0:
              continue
          r = nms(bboxs[idxs, 1:], match_threshold, match_metric)
          final_boxes.append(
              np.concatenate([np.full((r.shape[0], 1), c), r], 1)
          )
      return final_boxes
  
imports: import time
  import os
  import ast
  import argparse
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: multiclass_nms(bboxs, num_classes, match_threshold, match_metric) and returns: final_boxes