class_docstr: GFLHead
  Args:
      conv_feat (object): Instance of 'FCOSFeat'
      num_classes (int): Number of classes
      fpn_stride (list): The stride of each FPN Layer
      prior_prob (float): Used to set the bias init for the class prediction layer
      loss_class (object): Instance of QualityFocalLoss.
      loss_dfl (object): Instance of DistributionFocalLoss.
      loss_bbox (object): Instance of bbox loss.
      reg_max: Max value of integral set :math: `{0, ..., reg_max}`
              n QFL setting. Default: 16.
code: def post_process(self, gfl_head_outs, im_shape, scale_factor):
      cls_scores, bboxes_reg = gfl_head_outs
      bboxes = paddle.concat(bboxes_reg, axis=1)
      # rescale: [h_scale, w_scale] -> [w_scale, h_scale, w_scale, h_scale]
      im_scale = scale_factor.flip([1]).tile([1, 2]).unsqueeze(1)
      bboxes /= im_scale
      mlvl_scores = paddle.concat(cls_scores, axis=1)
      mlvl_scores = mlvl_scores.transpose([0, 2, 1])
      bbox_pred, bbox_num, _ = self.nms(bboxes, mlvl_scores)
      return bbox_pred, bbox_num
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.nn.initializer import Normal, Constant
  from ppdet.core.workspace import register
  from ppdet.modeling.bbox_utils import distance2bbox, bbox2distance, batch_distance2bbox
  from ppdet.data.transform.atss_assigner import bbox_overlaps
label_desc: Use image node_id 7 for calling the GFLHead obj's underlying member method code with example usage: obj.post_process(gfl_head_outs, im_shape, scale_factor) and returns: bbox_pred,  bbox_num