docstr: * WARNING: smp is modified in place.
  * At the start of this function, `smp` has the same input and target:
    |--------------------------------------------------------|
    | smp['net_input']['src_tokens'] |  smp['target']        |
    | (from data) __en__ hello world |  __en__ hello world   |
    |--------------------------------------------------------|
  
  * We call generator.generate(smp, bos_token = token("ro")),
  and copy the result as input
  * At the end, `smp` has the translation to other language.
    |--------------------------------------------------------|
    | smp['net_input']['src_tokens'] |  smp['target']        |
    | (generated) __ro__ salut lume  |  __en__ hello world   |
    |--------------------------------------------------------|
code: def backtranslate_sample(self, smp, orig_lang, other_lang) -> None:
      """
      * WARNING: smp is modified in place.
      * At the start of this function, `smp` has the same input and target:
        |--------------------------------------------------------|
        | smp['net_input']['src_tokens'] |  smp['target']        |
        | (from data) __en__ hello world |  __en__ hello world   |
        |--------------------------------------------------------|
  
      * We call generator.generate(smp, bos_token = token("ro")),
      and copy the result as input
      * At the end, `smp` has the translation to other language.
        |--------------------------------------------------------|
        | smp['net_input']['src_tokens'] |  smp['target']        |
        | (generated) __ro__ salut lume  |  __en__ hello world   |
        |--------------------------------------------------------|
  
      """
      bos_token = _lang_token_index(self.dictionary, other_lang)
      generated = self.sequence_generators[orig_lang].generate(
          models=[], sample=smp, bos_token=bos_token
      )
  
      max_lngth = max([gn[0]["tokens"].size(0) for gn in generated])
      net_input = smp["net_input"]
      n_src_tokens = torch.empty(
          size=(len(generated), max_lngth + 1),
          dtype=net_input["src_tokens"].dtype,
      )
      n_src_lengths = torch.empty(
          len(generated), dtype=net_input["src_lengths"].dtype
      )
  
      for i, gn in enumerate(generated):
          tokens = gn[0]["tokens"]
          tokens_size = tokens.size(0)
          padding_needed = max_lngth - tokens_size
          tokens = torch.cat([tokens.new([bos_token]), tokens])
          tokens = F.pad(
              tokens, (0, padding_needed), value=self.dictionary.pad()
          )
          n_src_tokens[i] = tokens
          n_src_lengths[i] = tokens_size + 1
  
      device = net_input["src_tokens"].device
      # This seems to be important
      del net_input["src_tokens"]
      del net_input["src_lengths"]
      net_input["src_tokens"] = n_src_tokens.to(device)
      net_input["src_lengths"] = n_src_lengths.to(device)
  
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 16 for calling the OnlineBackTranslationTask obj's underlying member method code with example usage: obj.backtranslate_sample(smp, orig_lang, other_lang) without return types