code: def preprocess_corpora_multilingual_many_to_one(
      args, dictionary_cls=Dictionary
  ):
      source_dicts = build_vocab_multicorpus(
          args.multiling_source_lang,
          args.multiling_train_source_text_file,
          args.multiling_encoder_lang,
          args.multiling_source_vocab_file,
          args.source_max_vocab_size,
          dictionary_cls,
      )
      source_corpus_lang_ids = [
          args.multiling_encoder_lang.index(l)
          for l in args.multiling_source_lang
      ]
      source_corpus_dicts = [
          source_dicts[l] for l in args.multiling_source_lang
      ]
      binarize_text_file_multilingual(
          corpus_configs=make_multiling_corpus_configs(
              source_corpus_lang_ids,
              args.multiling_train_source_text_file,
              source_corpus_dicts,
              args.multiling_train_oversampling,
          ),
          output_path=args.train_source_binary_path,
          append_eos=args.append_eos_to_source,
          reverse_order=args.reverse_source,
          prepend_language_id=False,
      )
      binarize_text_file_multilingual(
          corpus_configs=make_multiling_corpus_configs(
              source_corpus_lang_ids,
              args.multiling_eval_source_text_file,
              source_corpus_dicts,
              args.multiling_train_oversampling,
          ),
          output_path=args.eval_source_binary_path,
          append_eos=args.append_eos_to_source,
          reverse_order=args.reverse_source,
          prepend_language_id=False,
      )
  
      target_dicts = build_vocab_multicorpus(
          args.multiling_target_lang,
          args.multiling_train_target_text_file,
          args.multiling_decoder_lang,
          args.multiling_target_vocab_file,
          args.target_max_vocab_size,
          args.penalized_target_tokens_file,
          dictionary_cls,
      )
      target_corpus_lang_ids = [
          args.multiling_decoder_lang.index(l)
          for l in args.multiling_target_lang
      ]
      target_corpus_dicts = [
          target_dicts[l] for l in args.multiling_target_lang
      ]
      binarize_text_file_multilingual(
          corpus_configs=make_multiling_corpus_configs(
              target_corpus_lang_ids,
              args.multiling_train_target_text_file,
              target_corpus_dicts,
              args.multiling_train_oversampling,
          ),
          output_path=args.train_target_binary_path,
          append_eos=True,
          reverse_order=False,
          prepend_language_id=True,
      )
      binarize_text_file_multilingual(
          corpus_configs=make_multiling_corpus_configs(
              target_corpus_lang_ids,
              args.multiling_eval_target_text_file,
              target_corpus_dicts,
              args.multiling_train_oversampling,
          ),
          output_path=args.eval_target_binary_path,
          append_eos=True,
          reverse_order=False,
          prepend_language_id=True,
      )
  
imports: import argparse
  import os
  import tempfile
  from typing import Dict, List, Optional
  from pytorch_translate import constants, multilingual_utils, options, utils
  from pytorch_translate.data import char_data, data
  from pytorch_translate.data.dictionary import Dictionary
label_desc: Use image node_id 11 for calling a global function with example usage: preprocess_corpora_multilingual_many_to_one(args, dictionary_cls) without return types