code: def __init__(
      self,
      feat_in=256,
      feat_out=256,
      num_cls_convs=2,
      num_reg_convs=4,
      norm_type="bn",
  ):
      super(YOLOFFeat, self).__init__()
      assert norm_type == "bn", "YOLOFFeat only support BN now."
      self.feat_in = feat_in
      self.feat_out = feat_out
      self.num_cls_convs = num_cls_convs
      self.num_reg_convs = num_reg_convs
      self.norm_type = norm_type
  
      cls_subnet, reg_subnet = [], []
      for i in range(self.num_cls_convs):
          feat_in = self.feat_in if i == 0 else self.feat_out
          cls_subnet.append(
              nn.Conv2D(
                  feat_in,
                  self.feat_out,
                  3,
                  stride=1,
                  padding=1,
                  weight_attr=ParamAttr(
                      initializer=Normal(mean=0.0, std=0.01)
                  ),
                  bias_attr=ParamAttr(initializer=Constant(value=0.0)),
              )
          )
          cls_subnet.append(
              nn.BatchNorm2D(
                  self.feat_out,
                  weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
                  bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
              )
          )
          cls_subnet.append(nn.ReLU())
  
      for i in range(self.num_reg_convs):
          feat_in = self.feat_in if i == 0 else self.feat_out
          reg_subnet.append(
              nn.Conv2D(
                  feat_in,
                  self.feat_out,
                  3,
                  stride=1,
                  padding=1,
                  weight_attr=ParamAttr(
                      initializer=Normal(mean=0.0, std=0.01)
                  ),
                  bias_attr=ParamAttr(initializer=Constant(value=0.0)),
              )
          )
          reg_subnet.append(
              nn.BatchNorm2D(
                  self.feat_out,
                  weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
                  bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
              )
          )
          reg_subnet.append(nn.ReLU())
  
      self.cls_subnet = nn.Sequential(*cls_subnet)
      self.reg_subnet = nn.Sequential(*reg_subnet)
  
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 YOLOFFeat object from inherited base classes: nn with example: obj = YOLOFFeat(feat_in, feat_out, num_cls_convs, num_reg_convs, norm_type)