code: def __init__(
      self,
      num_classes=80,
      conv_feat="YOLOFFeat",
      anchor_generator="AnchorGenerator",
      bbox_assigner="UniformAssigner",
      loss_class="FocalLoss",
      loss_bbox="GIoULoss",
      ctr_clip=32.0,
      delta_mean=[0.0, 0.0, 0.0, 0.0],
      delta_std=[1.0, 1.0, 1.0, 1.0],
      nms="MultiClassNMS",
      prior_prob=0.01,
      nms_pre=1000,
      use_inside_anchor=False,
      trt=False,
      exclude_nms=False,
  ):
      super(YOLOFHead, self).__init__()
      self.num_classes = num_classes
      self.conv_feat = conv_feat
      self.anchor_generator = anchor_generator
      self.na = self.anchor_generator.num_anchors
      self.bbox_assigner = bbox_assigner
      self.loss_class = loss_class
      self.loss_bbox = loss_bbox
      self.ctr_clip = ctr_clip
      self.delta_mean = delta_mean
      self.delta_std = delta_std
      self.nms = nms
      self.nms_pre = nms_pre
      self.use_inside_anchor = use_inside_anchor
      if isinstance(self.nms, MultiClassNMS) and trt:
          self.nms.trt = trt
      self.exclude_nms = exclude_nms
  
      bias_init_value = -math.log((1 - prior_prob) / prior_prob)
      self.cls_score = self.add_sublayer(
          "cls_score",
          nn.Conv2D(
              in_channels=conv_feat.feat_out,
              out_channels=self.num_classes * self.na,
              kernel_size=3,
              stride=1,
              padding=1,
              weight_attr=ParamAttr(
                  initializer=Normal(mean=0.0, std=0.01)
              ),
              bias_attr=ParamAttr(
                  initializer=Constant(value=bias_init_value)
              ),
          ),
      )
  
      self.bbox_pred = self.add_sublayer(
          "bbox_pred",
          nn.Conv2D(
              in_channels=conv_feat.feat_out,
              out_channels=4 * self.na,
              kernel_size=3,
              stride=1,
              padding=1,
              weight_attr=ParamAttr(
                  initializer=Normal(mean=0.0, std=0.01)
              ),
              bias_attr=ParamAttr(initializer=Constant(value=0)),
          ),
      )
  
      self.object_pred = self.add_sublayer(
          "object_pred",
          nn.Conv2D(
              in_channels=conv_feat.feat_out,
              out_channels=self.na,
              kernel_size=3,
              stride=1,
              padding=1,
              weight_attr=ParamAttr(
                  initializer=Normal(mean=0.0, std=0.01)
              ),
              bias_attr=ParamAttr(initializer=Constant(value=0)),
          ),
      )
  
imports: import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Normal, Constant
  from ppdet.modeling.layers import MultiClassNMS
  from ppdet.core.workspace import register
  from ppdet.modeling.bbox_utils import delta2bbox_v2
label_desc: Use image node_id 1 to create a new YOLOFHead object from inherited base classes: nn with example: obj = YOLOFHead(num_classes, conv_feat, anchor_generator, bbox_assigner, loss_class, loss_bbox, ctr_clip, delta_mean, delta_std, nms, prior_prob, nms_pre, use_inside_anchor, trt, exclude_nms)