class_docstr: FCOSHead
  Args:
      num_classes (int): Number of classes
      fcos_feat (object): Instance of 'FCOSFeat'
      fpn_stride (list): The stride of each FPN Layer
      prior_prob (float): Used to set the bias init for the class prediction layer
      norm_reg_targets (bool): Normalization the regression target if true
      centerness_on_reg (bool): The prediction of centerness on regression or clssification branch
      num_shift (float): Relative offset between the center of the first shift and the top-left corner of img
      fcos_loss (object): Instance of 'FCOSLoss'
      nms (object): Instance of 'MultiClassNMS'
      trt (bool): Whether to use trt in nms of deploy
code: def _post_process_by_level(
      self, locations, box_cls, box_reg, box_ctn, sqrt_score=False
  ):
      box_scores = F.sigmoid(box_cls).flatten(2).transpose([0, 2, 1])
      box_centerness = (
          F.sigmoid(box_ctn).flatten(2).transpose([0, 2, 1])
      )
      pred_scores = box_scores * box_centerness
      if sqrt_score:
          pred_scores = paddle.sqrt(pred_scores)
  
      box_reg_ch_last = box_reg.flatten(2).transpose([0, 2, 1])
      box_reg_decoding = paddle.stack(
          [
              locations[:, 0] - box_reg_ch_last[:, :, 0],
              locations[:, 1] - box_reg_ch_last[:, :, 1],
              locations[:, 0] + box_reg_ch_last[:, :, 2],
              locations[:, 1] + box_reg_ch_last[:, :, 3],
          ],
          axis=1,
      )
      pred_boxes = box_reg_decoding.transpose([0, 2, 1])
  
      return pred_scores, pred_boxes
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  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.layers import ConvNormLayer, MultiClassNMS
label_desc: Use image node_id 5 for calling the FCOSHead obj's underlying member method code with example usage: obj._post_process_by_level(locations, box_cls, box_reg, box_ctn, sqrt_score) and returns: pred_scores,  pred_boxes