class_docstr: Base class for single-stage instance segmentors.
class_docstr_tok: ["Base","class","for","single-stage","instance","segmentors","."]
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.
      """
      x = self.backbone(batch_inputs)
      if self.with_neck:
          x = self.neck(x)
      return x
  
file_path: mmdetection/mmdet/models/detectors/single_stage_instance_seg.py
imports: import copy
  from typing import Tuple
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures import OptSampleList, SampleList
  from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
  from .base import BaseDetector
label_desc: Use image node_id 2 for calling the SingleStageInstanceSegmentor obj's underlying member method code with example usage: obj.extract_feat(batch_inputs) and returns: x