class_docstr: Mask RCNN loss is computed inside the MaskRCNN model. This class is a wrapper to extract
  loss values for different heads (RPN, classification, etc.) and compute the weighted sum.
  
  Args:
      opts: command-line arguments
class_docstr_tok: ["Mask","RCNN","loss","is","computed","inside","the","MaskRCNN","model",".","This","class","is","a","wrapper","to","extract","loss","values","for","different","heads","(","RPN",",","classification",",","etc",".",")","and","compute","the","weighted","sum",".","Args",":","opts",":","command-line","arguments"]
code: def extra_repr(self) -> str:
      return (
          f"\n\t classifier_wt={self.classifier_weight}"
          f"\n\t box_reg_weight={self.box_reg_weight}"
          f"\n\t mask_weight={self.mask_weight}"
          f"\n\t objectness_weight={self.objectness_weight}"
          f"\n\t rpn_box_reg={self.rpn_box_reg}"
      )
  
file_path: ml-cvnets/loss_fn/detection/mask_rcnn_loss.py
imports: import argparse
  from typing import Any, Dict
  import torch
  from torch import Tensor
  from loss_fn import LOSS_REGISTRY
  from loss_fn.detection.base_detection_criteria import BaseDetectionCriteria
  from utils import logger
label_desc: Use image node_id 4 for calling the MaskRCNNLoss obj's underlying member method code with example usage: obj.extra_repr() and returns: str, self, str, self, str, self, str, self, str, self