code: def nms(boxes: np.ndarray, thresh: float = 0.5) -> List[int]:
      """Perform non-max suppression, borrowed from <https://github.com/rbgirshick/fast-rcnn>`_.
  
      Args:
      ----
          boxes: np array of straight boxes: (*, 5), (xmin, ymin, xmax, ymax, score)
          thresh: iou threshold to perform box suppression.
  
      Returns:
      -------
          A list of box indexes to keep
      """
      x1 = boxes[:, 0]
      y1 = boxes[:, 1]
      x2 = boxes[:, 2]
      y2 = boxes[:, 3]
      scores = boxes[:, 4]
  
      areas = (x2 - x1) * (y2 - y1)
      order = scores.argsort()[::-1]
  
      keep = []
      while order.size > 0:
          i = order[0]
          keep.append(i)
          xx1 = np.maximum(x1[i], x1[order[1:]])
          yy1 = np.maximum(y1[i], y1[order[1:]])
          xx2 = np.minimum(x2[i], x2[order[1:]])
          yy2 = np.minimum(y2[i], y2[order[1:]])
  
          w = np.maximum(0.0, xx2 - xx1)
          h = np.maximum(0.0, yy2 - yy1)
          inter = w * h
          ovr = inter / (areas[i] + areas[order[1:]] - inter)
  
          inds = np.where(ovr <= thresh)[0]
          order = order[inds + 1]
      return keep
  
imports: from typing import Dict, List, Optional, Tuple
  import cv2
  import numpy
  from scipy.optimize import linear_sum_assignment
  from unidecode import unidecode
label_desc: Use image node_id 8 for calling a global function with example usage: nms(boxes, thresh) and returns: keep