code: def __call__(self, samples, context=None):
      for b_id, sample in enumerate(samples):
          output_h = sample["image"].shape[1] // self.down_ratio
          output_w = sample["image"].shape[2] // self.down_ratio
  
          heatmap = np.zeros(
              (self.num_classes, output_h, output_w), dtype="float32"
          )
          bbox_size = np.zeros((self.max_objs, 4), dtype=np.float32)
          center_offset = np.zeros((self.max_objs, 2), dtype=np.float32)
          index = np.zeros((self.max_objs,), dtype=np.int64)
          index_mask = np.zeros((self.max_objs,), dtype=np.int32)
          reid = np.zeros((self.max_objs,), dtype=np.int64)
          bbox_xys = np.zeros((self.max_objs, 4), dtype=np.float32)
          if self.num_classes > 1:
              # each category corresponds to a set of track ids
              cls_tr_ids = np.zeros(
                  (self.num_classes, output_h, output_w), dtype=np.int64
              )
              cls_id_map = np.full(
                  (output_h, output_w), -1, dtype=np.int64
              )
  
          gt_bbox = sample["gt_bbox"]
          gt_class = sample["gt_class"]
          gt_ide = sample["gt_ide"]
  
          for k in range(len(gt_bbox)):
              cls_id = gt_class[k][0]
              bbox = gt_bbox[k]
              ide = gt_ide[k][0]
              bbox[[0, 2]] = bbox[[0, 2]] * output_w
              bbox[[1, 3]] = bbox[[1, 3]] * output_h
              bbox_amodal = copy.deepcopy(bbox)
              bbox_amodal[0] = bbox_amodal[0] - bbox_amodal[2] / 2.0
              bbox_amodal[1] = bbox_amodal[1] - bbox_amodal[3] / 2.0
              bbox_amodal[2] = bbox_amodal[0] + bbox_amodal[2]
              bbox_amodal[3] = bbox_amodal[1] + bbox_amodal[3]
              bbox[0] = np.clip(bbox[0], 0, output_w - 1)
              bbox[1] = np.clip(bbox[1], 0, output_h - 1)
              h = bbox[3]
              w = bbox[2]
  
              bbox_xy = copy.deepcopy(bbox)
              bbox_xy[0] = bbox_xy[0] - bbox_xy[2] / 2
              bbox_xy[1] = bbox_xy[1] - bbox_xy[3] / 2
              bbox_xy[2] = bbox_xy[0] + bbox_xy[2]
              bbox_xy[3] = bbox_xy[1] + bbox_xy[3]
  
              if h > 0 and w > 0:
                  radius = gaussian_radius(
                      (math.ceil(h), math.ceil(w)), 0.7
                  )
                  radius = max(0, int(radius))
                  ct = np.array([bbox[0], bbox[1]], dtype=np.float32)
                  ct_int = ct.astype(np.int32)
                  self.draw_truncate_gaussian(
                      heatmap[cls_id], ct_int, radius, radius
                  )
                  bbox_size[k] = (
                      ct[0] - bbox_amodal[0],
                      ct[1] - bbox_amodal[1],
                      bbox_amodal[2] - ct[0],
                      bbox_amodal[3] - ct[1],
                  )
  
                  index[k] = ct_int[1] * output_w + ct_int[0]
                  center_offset[k] = ct - ct_int
                  index_mask[k] = 1
                  reid[k] = ide
                  bbox_xys[k] = bbox_xy
                  if self.num_classes > 1:
                      cls_id_map[ct_int[1], ct_int[0]] = cls_id
                      cls_tr_ids[cls_id][ct_int[1]][ct_int[0]] = ide - 1
                      # track id start from 0
  
          sample["heatmap"] = heatmap
          sample["index"] = index
          sample["offset"] = center_offset
          sample["size"] = bbox_size
          sample["index_mask"] = index_mask
          sample["reid"] = reid
          if self.num_classes > 1:
              sample["cls_id_map"] = cls_id_map
              sample["cls_tr_ids"] = cls_tr_ids
          sample["bbox_xys"] = bbox_xys
          sample.pop("is_crowd", None)
          sample.pop("difficult", None)
          sample.pop("gt_class", None)
          sample.pop("gt_bbox", None)
          sample.pop("gt_score", None)
          sample.pop("gt_ide", None)
      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 2 for calling the Gt2FairMOTTarget obj's underlying member method code with example usage: obj.__call__(samples, context) and returns: samples