docstr: inputs: ground-truth instances.
  anchor_box (Tensor): [num_anchors, 4], num_anchors are all anchors in all feature maps.
docstr_tok: ["inputs",":","ground-truth","instances",".","anchor_box","(","Tensor",")",":","[","num_anchors",",","4","]",",","num_anchors","are","all","anchors","in","all","feature","maps","."]
code: def __call__(self, inputs, anchors):
      """
      inputs: ground-truth instances.
      anchor_box (Tensor): [num_anchors, 4], num_anchors are all anchors in all feature maps.
      """
      gt_boxes = inputs["gt_bbox"]
      is_crowd = inputs.get("is_crowd", None)
      batch_size = len(gt_boxes)
      tgt_labels, tgt_bboxes, tgt_deltas = rpn_anchor_target(
          anchors,
          gt_boxes,
          self.batch_size_per_im,
          self.positive_overlap,
          self.negative_overlap,
          self.fg_fraction,
          self.use_random,
          batch_size,
          self.ignore_thresh,
          is_crowd,
          assign_on_cpu=self.assign_on_cpu,
      )
      norm = self.batch_size_per_im * batch_size
  
      return tgt_labels, tgt_bboxes, tgt_deltas, norm
  
imports: import sys
  import paddle
  from ppdet.core.workspace import register, serializable
  from .target import rpn_anchor_target, generate_proposal_target, generate_mask_target, libra_generate_proposal_target
  import numpy
label_desc: Use image node_id 2 for calling the RPNTargetAssign obj's underlying member method code with example usage: obj.__call__(inputs, anchors) and returns: tgt_labels,  tgt_bboxes,  tgt_deltas,  norm