class_docstr: Implementation of `LAD <https://arxiv.org/pdf/2108.10520.pdf>`_.
docstr: Directly extract teacher features from the backbone+neck.
docstr_tok: ["Directly","extract","teacher","features","from","the","backbone+neck","."]
code: def extract_teacher_feat(self, batch_inputs: Tensor) -> Tensor:
      """Directly extract teacher features from the backbone+neck."""
      x = self.teacher_model.backbone(batch_inputs)
      if self.with_teacher_neck:
          x = self.teacher_model.neck(x)
      return x
  
file_path: mmdetection/mmdet/models/detectors/lad.py
imports: from typing import Optional
  import torch
  import torch.nn
  from mmengine.runner import load_checkpoint
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
  from mmdet.utils import ConfigType, OptConfigType
  from ..utils.misc import unpack_gt_instances
  from .kd_one_stage import KnowledgeDistillationSingleStageDetector
label_desc: Use image node_id 3 for calling the LAD obj's underlying member method code with example usage: obj.extract_teacher_feat(batch_inputs) and returns: x