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 get_loss(self, fcos_head_outs, targets):
      cls_logits, bboxes_reg, centerness = fcos_head_outs
  
      # get labels,reg_target,centerness
      tag_labels, tag_bboxes, tag_centerness = [], [], []
      for i in range(len(self.fpn_stride)):
          k_lbl = "labels{}".format(i)
          if k_lbl in targets:
              tag_labels.append(targets[k_lbl])
          k_box = "reg_target{}".format(i)
          if k_box in targets:
              tag_bboxes.append(targets[k_box])
          k_ctn = "centerness{}".format(i)
          if k_ctn in targets:
              tag_centerness.append(targets[k_ctn])
  
      losses_fcos = self.fcos_loss(
          cls_logits,
          bboxes_reg,
          centerness,
          tag_labels,
          tag_bboxes,
          tag_centerness,
      )
      return losses_fcos
  
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 4 for calling the FCOSHead obj's underlying member method code with example usage: obj.get_loss(fcos_head_outs, targets) and returns: losses_fcos