code: def gather_topk_anchors(
      metrics, topk, largest=True, topk_mask=None, eps=1e-9
  ):
      r"""
      Args:
          metrics (Tensor, float32): shape[B, n, L], n: num_gts, L: num_anchors
          topk (int): The number of top elements to look for along the axis.
          largest (bool) : largest is a flag, if set to true,
              algorithm will sort by descending order, otherwise sort by
              ascending order. Default: True
          topk_mask (Tensor, float32): shape[B, n, 1], ignore bbox mask,
              Default: None
          eps (float): Default: 1e-9
      Returns:
          is_in_topk (Tensor, float32): shape[B, n, L], value=1. means selected
      """
      num_anchors = metrics.shape[-1]
      topk_metrics, topk_idxs = paddle.topk(
          metrics, topk, axis=-1, largest=largest
      )
      if topk_mask is None:
          topk_mask = (
              topk_metrics.max(axis=-1, keepdim=True) > eps
          ).astype(metrics.dtype)
      is_in_topk = (
          F.one_hot(topk_idxs, num_anchors)
          .sum(axis=-2)
          .astype(metrics.dtype)
      )
      return is_in_topk * topk_mask
  
file_path: PaddleDetection/ppdet/modeling/assigners/utils.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn.functional
label_desc: Use image node_id 2 for calling a global function with example usage: gather_topk_anchors(metrics, topk, largest, topk_mask, eps) and returns: unknown