class_docstr: This class implements a model-specific object detector using Faster-RCNN and TensorFlowV2.
docstr: Download, extract and load a model from a URL if it is not already in the cache. The file indicated by `url`
  is downloaded to the path ~/.art/data and given the name `filename`. Files in tar, tar.gz, tar.bz, and zip
  formats will also be extracted. Then the model is loaded, pipelined and its outputs are returned as a tuple
  of (predictions, losses, detections).
  
  :param filename: Name of the file.
  :param url: Download URL.
  :param is_training: A boolean indicating whether the training version of the computation graph should be
                      constructed.
  :return: the object detection model restored from checkpoint
docstr_tok: ["Download",",","extract","and","load","a","model","from","a","URL","if","it","is","not","already","in","the","cache",".","The","file","indicated","by","`","url","`","is","downloaded","to","the","path","~\/.art\/data","and","given","the","name","`","filename","`",".","Files","in","tar",",","tar.gz",",","tar.bz",",","and","zip","formats","will","also","be","extracted",".","Then","the","model","is","loaded",",","pipelined","and","its","outputs","are","returned","as","a","tuple","of","(","predictions",",","losses",",","detections",")",".",":","param","filename",":","Name","of","the","file",".",":","param","url",":","Download","URL",".",":","param","is_training",":","A","boolean","indicating","whether","the","training","version","of","the","computation","graph","should","be","constructed",".",":","return",":","the","object","detection","model","restored","from","checkpoint"]
code: def _load_model(
      filename: Optional[str] = None,
      url: Optional[str] = None,
      is_training: bool = False,
  ) -> Tuple[Dict[str, "tf.Tensor"], ...]:
      """
      Download, extract and load a model from a URL if it is not already in the cache. The file indicated by `url`
      is downloaded to the path ~/.art/data and given the name `filename`. Files in tar, tar.gz, tar.bz, and zip
      formats will also be extracted. Then the model is loaded, pipelined and its outputs are returned as a tuple
      of (predictions, losses, detections).
  
      :param filename: Name of the file.
      :param url: Download URL.
      :param is_training: A boolean indicating whether the training version of the computation graph should be
                          constructed.
      :return: the object detection model restored from checkpoint
      """
      import tensorflow as tf
  
      from object_detection.utils import config_util
      from object_detection.builders import model_builder
  
      # If obj_detection_model is None, then we need to have parameters filename and url to download, extract
      # and load the object detection model
      if filename is None or url is None:  # pragma: no cover
          raise ValueError(
              "Need input parameters `filename` and `url` to download, "
              "extract and load the object detection model."
          )
  
      # Download and extract
      path = get_file(
          filename=filename,
          path=config.ART_DATA_PATH,
          url=url,
          extract=True,
      )
      # Load model config
      pipeline_config = path + "/pipeline.config"
      configs = config_util.get_configs_from_pipeline_file(
          pipeline_config
      )
      configs["model"].faster_rcnn.second_stage_batch_size = configs[
          "model"
      ].faster_rcnn.first_stage_max_proposals
  
      # Load model
      obj_detection_model = model_builder.build(
          model_config=configs["model"],
          is_training=is_training,
          add_summaries=False,
      )
  
      # Restore checkpoint
      ckpt = tf.compat.v2.train.Checkpoint(model=obj_detection_model)
      ckpt.restore(path + "/checkpoint/ckpt-0").expect_partial()
  
      return obj_detection_model
  
imports: import logging
  from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from art.estimators.object_detection.object_detector import ObjectDetectorMixin
  from art.estimators.tensorflow import TensorFlowV2Estimator
  from art.utils import get_file
  from art import config
label_desc: Use image node_id 4 for calling the TensorFlowV2FasterRCNN obj's underlying member method code with example usage: obj._load_model(filename, url, is_training) and returns: obj_detection_model