class_docstr: Soft KL Loss for classification tasks. Given an input sample, soft-labels (or probabilities)
  are generated from a teacher and KL loss is computed between soft-labels and student model's output.
  
  Args:
      opts: command-line arguments
class_docstr_tok: ["Soft","KL","Loss","for","classification","tasks",".","Given","an","input","sample",",","soft-labels","(","or","probabilities",")","are","generated","from","a","teacher","and","KL","loss","is","computed","between","soft-labels","and","student","model","'s","output",".","Args",":","opts",":","command-line","arguments"]
code: def extra_repr(self) -> str:
      return f"\n\t temperature={self.temperature}"
  
file_path: ml-cvnets/loss_fn/distillation/soft_kl_distillation.py
imports: import argparse
  import torch
  from torch import Tensor
  from torch.nn import functional
  from loss_fn import LOSS_REGISTRY
  from loss_fn.distillation.base_distillation import BaseDistillationCriteria
  from utils import logger
label_desc: Use image node_id 4 for calling the SoftKLLoss obj's underlying member method code with example usage: obj.extra_repr() and returns: str, self