class_docstr: This class implements a model-specific object detector using Faster-RCNN and TensorFlow.
class_docstr_tok: ["This","class","implements","a","model-specific","object","detector","using","Faster-RCNN","and","TensorFlow","."]
docstr: Get the `_losses` attribute.
  
  :return: A dictionary mapping loss keys (`Loss/RPNLoss/localization_loss`, `Loss/RPNLoss/objectness_loss`,
           `Loss/BoxClassifierLoss/localization_loss`, `Loss/BoxClassifierLoss/classification_loss`) to scalar
           tensors representing corresponding loss values.
docstr_tok: ["Get","the","`","_losses","`","attribute",".",":","return",":","A","dictionary","mapping","loss","keys","(","`","Loss\/RPNLoss\/localization_loss","`",",","`","Loss\/RPNLoss\/objectness_loss","`",",","`","Loss\/BoxClassifierLoss\/localization_loss","`",",","`","Loss\/BoxClassifierLoss\/classification_loss","`",")","to","scalar","tensors","representing","corresponding","loss","values","."]
code: def losses(self) -> Dict[str, "tf.Tensor"]:
      """
      Get the `_losses` attribute.
  
      :return: A dictionary mapping loss keys (`Loss/RPNLoss/localization_loss`, `Loss/RPNLoss/objectness_loss`,
               `Loss/BoxClassifierLoss/localization_loss`, `Loss/BoxClassifierLoss/classification_loss`) to scalar
               tensors representing corresponding loss values.
      """
      return self._losses
  
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 TensorFlowEstimator
  from art.utils import get_file
  from art import config
label_desc: Use image node_id 9 for calling the TensorFlowFasterRCNN obj's underlying member method code with example usage: obj.losses() and returns: self