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