code: def __init__(
      self,
      num_classes=80,
      feat_in=288,
      feat_out=288,
      num_convs=1,
      fpn_strides=[32, 16, 8, 4],
      norm_type="gn",
      act="LeakyReLU",
      prior_prob=0.01,
      reg_max=16,
  ):
      super(SimpleConvHead, self).__init__()
      self.num_classes = num_classes
      self.feat_in = feat_in
      self.feat_out = feat_out
      self.num_convs = num_convs
      self.fpn_strides = fpn_strides
      self.reg_max = reg_max
  
      self.cls_convs = nn.LayerList()
      self.reg_convs = nn.LayerList()
      for i in range(self.num_convs):
          in_c = feat_in if i == 0 else feat_out
          self.cls_convs.append(
              ConvNormLayer(
                  in_c,
                  feat_out,
                  3,
                  stride=1,
                  padding=1,
                  norm_type=norm_type,
                  activation=act,
              )
          )
          self.reg_convs.append(
              ConvNormLayer(
                  in_c,
                  feat_out,
                  3,
                  stride=1,
                  padding=1,
                  norm_type=norm_type,
                  activation=act,
              )
          )
  
      bias_cls = bias_init_with_prob(prior_prob)
      self.gfl_cls = nn.Conv2D(
          feat_out,
          self.num_classes,
          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_cls)),
      )
      self.gfl_reg = nn.Conv2D(
          feat_out,
          4 * (self.reg_max + 1),
          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.scales = nn.LayerList()
      for i in range(len(self.fpn_strides)):
          self.scales.append(ScaleReg(1.0))
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from paddle import ParamAttr
  from paddle.nn.initializer import KaimingNormal
  from paddle.nn.initializer import Normal, Constant
  from ..bbox_utils import batch_distance2bbox
  from ..losses import GIoULoss
  from ..initializer import bias_init_with_prob, constant_, normal_
  from ..assigners.utils import generate_anchors_for_grid_cell
  from ppdet.modeling.backbones.cspresnet import ConvBNLayer, RepVggBlock
  from ppdet.modeling.ops import get_static_shape, get_act_fn
  from ppdet.modeling.layers import MultiClassNMS
label_desc: Use image node_id 1 to create a new SimpleConvHead object from inherited base classes: nn with example: obj = SimpleConvHead(num_classes, feat_in, feat_out, num_convs, fpn_strides, norm_type, act, prior_prob, reg_max)