class_docstr: YOLO CSP-PAN, used in YOLOv5 and YOLOX.
class_docstr_tok: ["YOLO","CSP-PAN",",","used","in","YOLOv5","and","YOLOX","."]
code: def forward(self, feats, for_mot=False):
      assert len(feats) == len(self.in_channels)
  
      # top-down fpn
      inner_outs = [feats[-1]]
      for idx in range(len(self.in_channels) - 1, 0, -1):
          feat_heigh = inner_outs[0]
          feat_low = feats[idx - 1]
          feat_heigh = self.lateral_convs[
              len(self.in_channels) - 1 - idx
          ](feat_heigh)
          inner_outs[0] = feat_heigh
  
          upsample_feat = F.interpolate(
              feat_heigh,
              scale_factor=2.0,
              mode="nearest",
              data_format=self.data_format,
          )
          inner_out = self.fpn_blocks[len(self.in_channels) - 1 - idx](
              paddle.concat([upsample_feat, feat_low], axis=1)
          )
          inner_outs.insert(0, inner_out)
  
      # bottom-up pan
      outs = [inner_outs[0]]
      for idx in range(len(self.in_channels) - 1):
          feat_low = outs[-1]
          feat_height = inner_outs[idx + 1]
          downsample_feat = self.downsample_convs[idx](feat_low)
          out = self.pan_blocks[idx](
              paddle.concat([downsample_feat, feat_height], axis=1)
          )
          outs.append(out)
  
      return outs
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
  from ppdet.modeling.layers import DropBlock
  from ppdet.modeling.ops import get_act_fn
  from ..backbones.darknet import ConvBNLayer
  from ..shape_spec import ShapeSpec
  from ..backbones.csp_darknet import BaseConv, DWConv, CSPLayer
label_desc: Use image node_id 2 for calling the YOLOCSPPAN obj's underlying member method code with example usage: obj.forward(feats, for_mot) and returns: outs