class_docstr: Base class for semi-supervised detectors.
  
  Semi-supervised detectors typically consisting of a teacher model
  updated by exponential moving average and a student model updated
  by gradient descent.
  
  Args:
      detector (:obj:`ConfigDict` or dict): The detector config.
      semi_train_cfg (:obj:`ConfigDict` or dict, optional):
          The semi-supervised training config.
      semi_test_cfg (:obj:`ConfigDict` or dict, optional):
          The semi-supervised testing config.
      data_preprocessor (:obj:`ConfigDict` or dict, optional): Config of
          :class:`DetDataPreprocessor` to process the input data.
          Defaults to None.
      init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or
          list[dict], optional): Initialization config dict.
          Defaults to None.
docstr: Extract features.
  
  Args:
      batch_inputs (Tensor): Image tensor with shape (N, C, H ,W).
  
  Returns:
      tuple[Tensor]: Multi-level features that may have
      different resolutions.
docstr_tok: ["Extract","features",".","Args",":","batch_inputs","(","Tensor",")",":","Image","tensor","with","shape","(","N",",","C",",","H",",","W",")",".","Returns",":","tuple","[","Tensor","]",":","Multi-level","features","that","may","have","different","resolutions","."]
code: def extract_feat(self, batch_inputs: Tensor) -> Tuple[Tensor]:
      """Extract features.
  
      Args:
          batch_inputs (Tensor): Image tensor with shape (N, C, H ,W).
  
      Returns:
          tuple[Tensor]: Multi-level features that may have
          different resolutions.
      """
      if (
          self.semi_test_cfg.get("extract_feat_on", "teacher")
          == "teacher"
      ):
          return self.teacher.extract_feat(batch_inputs)
      else:
          return self.student.extract_feat(batch_inputs)
  
imports: import copy
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from torch import Tensor
  from mmdet.models.utils import filter_gt_instances, rename_loss_dict, reweight_loss_dict
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import bbox_project
  from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
  from .base import BaseDetector
label_desc: Use image node_id 10 for calling the SemiBaseDetector obj's underlying member method code with example usage: obj.extract_feat(batch_inputs) and returns: self, self