class_docstr: Bridging the Gap Between Anchor-based and Anchor-free Detection
  via Adaptive Training Sample Selection
code: def __init__(
      self,
      topk=9,
      num_classes=80,
      force_gt_matching=False,
      eps=1e-9,
      sm_use=False,
  ):
      super(ATSSAssigner, self).__init__()
      self.topk = topk
      self.num_classes = num_classes
      self.force_gt_matching = force_gt_matching
      self.eps = eps
      self.sm_use = sm_use
  
file_path: PaddleDetection/ppdet/modeling/assigners/atss_assigner.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ..bbox_utils import iou_similarity, batch_iou_similarity
  from ..bbox_utils import bbox_center
  from .utils import check_points_inside_bboxes, compute_max_iou_anchor, compute_max_iou_gt
label_desc: Use image node_id 1 to create a new ATSSAssigner object from inherited base classes: nn with example: obj = ATSSAssigner(topk, num_classes, force_gt_matching, eps, sm_use)