code: def forward(self, feats, for_mot=False, is_teacher=False):
      assert len(feats) == len(self.in_channels)
      # get projection features
      proj_feats = [
          self.input_proj[i](feat) for i, feat in enumerate(feats)
      ]
      # encoder
      if self.num_encoder_layers > 0:
          for i, enc_ind in enumerate(self.use_encoder_idx):
              h, w = proj_feats[enc_ind].shape[2:]
              # flatten [B, C, H, W] to [B, HxW, C]
              src_flatten = (
                  proj_feats[enc_ind].flatten(2).transpose([0, 2, 1])
              )
              if self.training or self.eval_size is None or is_teacher:
                  pos_embed = self.build_2d_sincos_position_embedding(
                      w, h, self.hidden_dim, self.pe_temperature
                  )
              else:
                  pos_embed = getattr(self, f"pos_embed{enc_ind}", None)
              memory = self.encoder[i](src_flatten, pos_embed=pos_embed)
              proj_feats[enc_ind] = memory.transpose([0, 2, 1]).reshape(
                  [-1, self.hidden_dim, h, w]
              )
  
      # top-down fpn
      inner_outs = [proj_feats[-1]]
      for idx in range(len(self.in_channels) - 1, 0, -1):
          feat_heigh = inner_outs[0]
          feat_low = proj_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"
          )
          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.ops import get_act_fn
  from ..shape_spec import ShapeSpec
  from ..backbones.csp_darknet import BaseConv
  from ..backbones.cspresnet import RepVggBlock
  from ppdet.modeling.transformers.detr_transformer import TransformerEncoder
  from ..initializer import xavier_uniform_, linear_init_
  from ..layers import MultiHeadAttention
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
label_desc: Use image node_id 4 for calling the HybridEncoder obj's underlying member method code with example usage: obj.forward(feats, for_mot, is_teacher) and returns: outs