code: def batch_iou_similarity(box1, box2, eps=1e-9):
      """Calculate iou of box1 and box2 in batch
  
      Args:
          box1 (Tensor): box with the shape [N, M1, 4]
          box2 (Tensor): box with the shape [N, M2, 4]
  
      Return:
          iou (Tensor): iou between box1 and box2 with the shape [N, M1, M2]
      """
      box1 = box1.unsqueeze(2)  # [N, M1, 4] -> [N, M1, 1, 4]
      box2 = box2.unsqueeze(1)  # [N, M2, 4] -> [N, 1, M2, 4]
      px1y1, px2y2 = box1[:, :, :, 0:2], box1[:, :, :, 2:4]
      gx1y1, gx2y2 = box2[:, :, :, 0:2], box2[:, :, :, 2:4]
      x1y1 = paddle.maximum(px1y1, gx1y1)
      x2y2 = paddle.minimum(px2y2, gx2y2)
      overlap = (x2y2 - x1y1).clip(0).prod(-1)
      area1 = (px2y2 - px1y1).clip(0).prod(-1)
      area2 = (gx2y2 - gx1y1).clip(0).prod(-1)
      union = area1 + area2 - overlap + eps
      return overlap / union
  
imports: import math
  import paddle
  import numpy
label_desc: Use image node_id 14 for calling a global function with example usage: batch_iou_similarity(box1, box2, eps) and returns: unknown