code: def __init__(
      self,
      num_classes=80,
      width_mult=1.0,
      depthwise=False,
      in_channels=[256, 512, 1024],
      feat_channels=256,
      fpn_strides=(8, 16, 32),
      l1_epoch=285,
      act="silu",
      assigner=SimOTAAssigner(use_vfl=False),
      nms="MultiClassNMS",
      loss_weight={
          "cls": 1.0,
          "obj": 1.0,
          "iou": 5.0,
          "l1": 1.0,
      },
      trt=False,
      exclude_nms=False,
  ):
      super(YOLOXHead, self).__init__()
      self._dtype = paddle.framework.get_default_dtype()
      self.num_classes = num_classes
      assert len(in_channels) > 0, "in_channels length should > 0"
      self.in_channels = in_channels
      feat_channels = int(feat_channels * width_mult)
      self.fpn_strides = fpn_strides
      self.l1_epoch = l1_epoch
      self.assigner = assigner
      self.nms = nms
      if isinstance(self.nms, MultiClassNMS) and trt:
          self.nms.trt = trt
      self.exclude_nms = exclude_nms
      self.loss_weight = loss_weight
      self.iou_loss = IouLoss(
          loss_weight=1.0
      )  # default loss_weight 2.5
  
      ConvBlock = DWConv if depthwise else BaseConv
  
      self.stem_conv = nn.LayerList()
      self.conv_cls = nn.LayerList()
      self.conv_reg = nn.LayerList()  # reg [x,y,w,h] + obj
      for in_c in self.in_channels:
          self.stem_conv.append(
              BaseConv(in_c, feat_channels, 1, 1, act=act)
          )
  
          self.conv_cls.append(
              nn.Sequential(
                  *[
                      ConvBlock(
                          feat_channels, feat_channels, 3, 1, act=act
                      ),
                      ConvBlock(
                          feat_channels, feat_channels, 3, 1, act=act
                      ),
                      nn.Conv2D(
                          feat_channels,
                          self.num_classes,
                          1,
                          bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
                      ),
                  ]
              )
          )
  
          self.conv_reg.append(
              nn.Sequential(
                  *[
                      ConvBlock(
                          feat_channels, feat_channels, 3, 1, act=act
                      ),
                      ConvBlock(
                          feat_channels, feat_channels, 3, 1, act=act
                      ),
                      nn.Conv2D(
                          feat_channels,
                          4 + 1,  # reg [x,y,w,h] + obj
                          1,
                          bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
                      ),
                  ]
              )
          )
  
      self._init_weights()
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from ppdet.core.workspace import register
  import math
  import numpy
  from ..initializer import bias_init_with_prob, constant_
  from ..backbones.csp_darknet import BaseConv, DWConv
  from ..losses import IouLoss
  from ppdet.modeling.assigners.simota_assigner import SimOTAAssigner
  from ppdet.modeling.bbox_utils import bbox_overlaps
  from ppdet.modeling.layers import MultiClassNMS
label_desc: Use image node_id 1 to create a new YOLOXHead object from inherited base classes: nn with example: obj = YOLOXHead(num_classes, width_mult, depthwise, in_channels, feat_channels, fpn_strides, l1_epoch, act, assigner, nms, loss_weight, trt, exclude_nms)