class_docstr: GFLHead for LD distill
  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 __init__(
      self,
      conv_feat="FCOSFeat",
      dgqp_module=None,
      num_classes=80,
      fpn_stride=[8, 16, 32, 64, 128],
      prior_prob=0.01,
      loss_class="QualityFocalLoss",
      loss_dfl="DistributionFocalLoss",
      loss_bbox="GIoULoss",
      loss_ld="KnowledgeDistillationKLDivLoss",
      loss_ld_vlr="KnowledgeDistillationKLDivLoss",
      loss_kd="KnowledgeDistillationKLDivLoss",
      reg_max=16,
      feat_in_chan=256,
      nms=None,
      nms_pre=1000,
      cell_offset=0,
  ):
      super(LDGFLHead, self).__init__(
          conv_feat=conv_feat,
          dgqp_module=dgqp_module,
          num_classes=num_classes,
          fpn_stride=fpn_stride,
          prior_prob=prior_prob,
          loss_class=loss_class,
          loss_dfl=loss_dfl,
          loss_bbox=loss_bbox,
          reg_max=reg_max,
          feat_in_chan=feat_in_chan,
          nms=nms,
          nms_pre=nms_pre,
          cell_offset=cell_offset,
      )
      self.loss_ld = loss_ld
      self.loss_kd = loss_kd
      self.loss_ld_vlr = loss_ld_vlr
  
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 1 to create a new LDGFLHead object from inherited base classes: GFLHead with example: obj = LDGFLHead(conv_feat, dgqp_module, num_classes, fpn_stride, prior_prob, loss_class, loss_dfl, loss_bbox, loss_ld, loss_ld_vlr, loss_kd, reg_max, feat_in_chan, nms, nms_pre, cell_offset)