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="VarifocalLoss",
      loss_dfl="DistributionFocalLoss",
      loss_bbox="GIoULoss",
      assigner="SimOTAAssigner",
      reg_max=16,
      feat_in_chan=256,
      nms=None,
      nms_pre=1000,
      cell_offset=0,
  ):
      super(OTAVFLHead, 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.conv_feat = conv_feat
      self.dgqp_module = dgqp_module
      self.num_classes = num_classes
      self.fpn_stride = fpn_stride
      self.prior_prob = prior_prob
      self.loss_vfl = loss_class
      self.loss_dfl = loss_dfl
      self.loss_bbox = loss_bbox
      self.reg_max = reg_max
      self.feat_in_chan = feat_in_chan
      self.nms = nms
      self.nms_pre = nms_pre
      self.cell_offset = cell_offset
      self.use_sigmoid = self.loss_vfl.use_sigmoid
  
      self.assigner = assigner
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  from functools import partial
  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
  from ppdet.data.transform.atss_assigner import bbox_overlaps
  from .gfl_head import GFLHead
label_desc: Use image node_id 1 to create a new OTAVFLHead object from inherited base classes: OTAHead with example: obj = OTAVFLHead(conv_feat, dgqp_module, num_classes, fpn_stride, prior_prob, loss_class, loss_dfl, loss_bbox, assigner, reg_max, feat_in_chan, nms, nms_pre, cell_offset)