class_docstr: This class implements a model-specific object detector using DEtection TRansformer (DETR)
  and PyTorch following the input and output formats of torchvision.
class_docstr_tok: ["This","class","implements","a","model-specific","object","detector","using","DEtection","TRansformer","(","DETR",")","and","PyTorch","following","the","input","and","output","formats","of","torchvision","."]
docstr: Return the shape of one input sample.
  
  :return: Shape of one input sample.
docstr_tok: ["Return","the","shape","of","one","input","sample",".",":","return",":","Shape","of","one","input","sample","."]
code: def input_shape(self) -> Tuple[int, ...]:
      """
      Return the shape of one input sample.
  
      :return: Shape of one input sample.
      """
      return self._input_shape
  
file_path: adversarial-robustness-toolbox/art/estimators/object_detection/pytorch_detection_transformer.py
imports: import logging
  from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union, Any
  import numpy
  from art.estimators.object_detection.object_detector import ObjectDetectorMixin
  from art.estimators.pytorch import PyTorchEstimator
label_desc: Use image node_id 3 for calling the PyTorchDetectionTransformer obj's underlying member method code with example usage: obj.input_shape() and returns: self