code: def display_samples_once_in_a_while(self, smp, mono_lang, other_lang):
      self._show_samples_ctr += 1
      if self._show_samples_ctr < self.SHOW_SAMPLES_INTERVAL:
          return
      self._show_samples_ctr = 0
  
      ln = smp["net_input"]["src_tokens"].shape[0]
  
      logger.info(
          f"(r:{self.args.distributed_rank}) : "
          f"{other_lang} ---> {mono_lang} "
          f"({other_lang} was generated by back-translation.) {ln} samples"
      )
  
      for i in range(min(ln, self.SHOW_SAMPLES_NUMBER)):
          src_tokens = smp["net_input"]["src_tokens"][i]
          tgt_tokens = smp["target"][i]
  
          src_str = self.dictionary.string(src_tokens, "sentencepiece")
          tgt_str = self.dictionary.string(tgt_tokens, "sentencepiece")
          logger.info(
              f"\n{i}\t\t[{other_lang} generated]  {src_str}\n"
              f"\t\t[{mono_lang} original ]  {tgt_str}\n"
              f"\t\t[ src tokens]  {src_tokens}\n"
          )
  
imports: import contextlib
  import json
  import logging
  import math
  import os
  from argparse import Namespace
  from collections import OrderedDict, defaultdict
  from pathlib import Path
  from typing import Dict, Sequence, Tuple
  from argparse import ArgumentError
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import fairseq
  from fairseq import options, utils
  from fairseq.logging import metrics
  from fairseq.data import FairseqDataset, LanguagePairDataset, NoisingDataset, PrependTokenDataset, RoundRobinZipDatasets, TransformEosLangPairDataset, data_utils, encoders
  from fairseq.sequence_generator import SequenceGenerator
  from fairseq.tasks import register_task
  from fairseq.tasks.translation import TranslationTask, load_langpair_dataset
label_desc: Use image node_id 15 for calling the OnlineBackTranslationTask obj's underlying member method code with example usage: obj.display_samples_once_in_a_while(smp, mono_lang, other_lang) without return types