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 are the native labels in PyTorch format [x1, y1, x2, y2]?
  
  :return: Are the native labels in PyTorch format [x1, y1, x2, y2]?
docstr_tok: ["Return","are","the","native","labels","in","PyTorch","format","[","x1",",","y1",",","x2",",","y2","]","?",":","return",":","Are","the","native","labels","in","PyTorch","format","[","x1",",","y1",",","x2",",","y2","]","?"]
code: def native_label_is_pytorch_format(self) -> bool:
      """
      Return are the native labels in PyTorch format [x1, y1, x2, y2]?
  
      :return: Are the native labels in PyTorch format [x1, y1, x2, y2]?
      """
      return True
  
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 2 for calling the PyTorchObjectSeeker obj's underlying member method code with example usage: obj.native_label_is_pytorch_format() and returns: True