class_docstr: Implementation of the ObjectSeeker certifiable robust defense applied to object detection models.
  The original implementation is https://github.com/inspire-group/ObjectSeeker
  
  | Paper link: https://arxiv.org/abs/2202.01811
class_docstr_tok: ["Implementation","of","the","ObjectSeeker","certifiable","robust","defense","applied","to","object","detection","models",".","The","original","implementation","is","https",":","\/\/github.com\/inspire-group\/ObjectSeeker","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/2202.01811"]
docstr: Return a boolean to indicate the index of the color channels for each image.
  
  :return: Boolean to indicate the index of the color channels for each image.
docstr_tok: ["Return","a","boolean","to","indicate","the","index","of","the","color","channels","for","each","image",".",":","return",":","Boolean","to","indicate","the","index","of","the","color","channels","for","each","image","."]
code: def channels_first(self) -> bool:
      """
      Return a boolean to indicate the index of the color channels for each image.
  
      :return: Boolean to indicate the index of the color channels for each image.
      """
      return self._channels_first
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import sys
  from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from art.estimators.certification.object_seeker.object_seeker import ObjectSeekerMixin
  from art.estimators.object_detection import ObjectDetectorMixin, PyTorchObjectDetector, PyTorchFasterRCNN, PyTorchYolo
  from art.estimators.pytorch import PyTorchEstimator
label_desc: Use image node_id 4 for calling the PyTorchObjectSeeker obj's underlying member method code with example usage: obj.channels_first() and returns: self