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: Shape of one input sample.
docstr_tok: [":","return",":","Shape","of","one","input","sample","."]
code: def input_shape(self) -> Tuple[int, ...]:
      """
      :return: Shape of one input sample.
      """
      pass
  
file_path: adversarial-robustness-toolbox/art/estimators/certification/object_seeker/object_seeker.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import abc
  import logging
  from typing import Dict, List, Tuple
  import numpy
  from sklearn.cluster import DBSCAN
  from tqdm.auto import tqdm
  from art.utils import intersection_over_area, non_maximum_suppression
label_desc: Use image node_id 3 for calling the ObjectSeekerMixin obj's underlying member method code with example usage: obj.input_shape() without return types