code: def __call__(self, samples, context=None):
      assert len(self.anchor_masks) == len(
          self.downsample_ratios
      ), "anchor_masks', and 'downsample_ratios' should have same length."
      h, w = samples[0]["image"].shape[1:3]
  
      num_max = 0
      for sample in samples:
          num_max = max(num_max, len(sample["gt_bbox"]))
  
      for sample in samples:
          gt_bbox = sample["gt_bbox"]
          gt_ide = sample["gt_ide"]
          for i, (anchor_hw, downsample_ratio) in enumerate(
              zip(self.anchors, self.downsample_ratios)
          ):
              anchor_hw = (
                  np.array(anchor_hw, dtype=np.float32)
                  / downsample_ratio
              )
              nA = len(anchor_hw)
              nGh, nGw = int(h / downsample_ratio), int(
                  w / downsample_ratio
              )
              tbox = np.zeros((nA, nGh, nGw, 4), dtype=np.float32)
              tconf = np.zeros((nA, nGh, nGw), dtype=np.float32)
              tid = -np.ones((nA, nGh, nGw, 1), dtype=np.float32)
  
              gxy, gwh = gt_bbox[:, 0:2].copy(), gt_bbox[:, 2:4].copy()
              gxy[:, 0] = gxy[:, 0] * nGw
              gxy[:, 1] = gxy[:, 1] * nGh
              gwh[:, 0] = gwh[:, 0] * nGw
              gwh[:, 1] = gwh[:, 1] * nGh
              gxy[:, 0] = np.clip(gxy[:, 0], 0, nGw - 1)
              gxy[:, 1] = np.clip(gxy[:, 1], 0, nGh - 1)
              tboxes = np.concatenate([gxy, gwh], axis=1)
  
              anchor_mesh = self.generate_anchor(nGh, nGw, anchor_hw)
  
              anchor_list = np.transpose(
                  anchor_mesh, (0, 2, 3, 1)
              ).reshape(-1, 4)
              iou_pdist = bbox_iou_np_expand(
                  anchor_list, tboxes, x1y1x2y2=False
              )
  
              iou_max = np.max(iou_pdist, axis=1)
              max_gt_index = np.argmax(iou_pdist, axis=1)
  
              iou_map = iou_max.reshape(nA, nGh, nGw)
              gt_index_map = max_gt_index.reshape(nA, nGh, nGw)
  
              id_index = iou_map > self.ide_thresh
              fg_index = iou_map > self.fg_thresh
              bg_index = iou_map < self.bg_thresh
              ign_index = (iou_map < self.fg_thresh) * (
                  iou_map > self.bg_thresh
              )
              tconf[fg_index] = 1
              tconf[bg_index] = 0
              tconf[ign_index] = -1
  
              gt_index = gt_index_map[fg_index]
              gt_box_list = tboxes[gt_index]
              gt_id_list = gt_ide[gt_index_map[id_index]]
  
              if np.sum(fg_index) > 0:
                  tid[id_index] = gt_id_list
  
                  fg_anchor_list = anchor_list.reshape(nA, nGh, nGw, 4)[
                      fg_index
                  ]
                  delta_target = self.encode_delta(
                      gt_box_list, fg_anchor_list
                  )
                  tbox[fg_index] = delta_target
  
              sample["tbox{}".format(i)] = tbox
              sample["tconf{}".format(i)] = tconf
              sample["tide{}".format(i)] = tid
          sample.pop("gt_class")
          sample = self.pad_box(sample, num_max)
      return samples
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from numbers import Integral
  import cv2
  import copy
  import numpy
  import random
  import math
  from .operators import BaseOperator, register_op
  from .batch_operators import Gt2TTFTarget
  from ppdet.modeling.bbox_utils import bbox_iou_np_expand
  from ppdet.utils.logger import setup_logger
  from .op_helper import gaussian_radius
label_desc: Use image node_id 5 for calling the Gt2JDETargetThres obj's underlying member method code with example usage: obj.__call__(samples, context) and returns: samples