class_docstr: Compute the augmentation loss, as described in the
  `RangeAugment <https://arxiv.org/abs/2212.10553>`_ paper.
  
  Args:
      opts: command line arguments
class_docstr_tok: ["Compute","the","augmentation","loss",",","as","described","in","the","`","RangeAugment","<","https",":","\/\/arxiv.org\/abs\/2212.10553",">","`","_","paper",".","Args",":","opts",":","command","line","arguments"]
code: def extra_repr(self) -> str:
      return (
          "\n\t target_metric={}"
          "\n\t target_value={}"
          "\n\t curriculum_learning={}"
          "\n\t alpha={}".format(
              self.perceptual_metric,
              self.target_str,
              self.curriculumn_learning,
              self.alpha,
          )
      )
  
file_path: ml-cvnets/loss_fn/neural_augmentation.py
imports: import argparse
  import math
  from typing import List, Mapping, Union
  import torch
  from torch import Tensor
  from torch.nn import functional
  from loss_fn import LOSS_REGISTRY, BaseCriteria
  from utils import logger
  from utils.ddp_utils import is_master
label_desc: Use image node_id 6 for calling the NeuralAugmentation obj's underlying member method code with example usage: obj.extra_repr() and returns: str