code: def _load_dataset_single_path(
      self,
      split: str,
      src_bin_path: str,
      tgt_bin_path: str,
      weights_file=None,
      is_npz=True,
  ):
      corpus = pytorch_translate_data.ParallelCorpusConfig(
          source=pytorch_translate_data.CorpusConfig(
              dialect=self.args.source_lang, data_file=src_bin_path
          ),
          target=pytorch_translate_data.CorpusConfig(
              dialect=self.args.target_lang, data_file=tgt_bin_path
          ),
          weights_file=weights_file,
      )
  
      if getattr(self.args, "log_verbose", False):
          print("Starting to load binarized data files.", flush=True)
      append_bos = getattr(self.args, "append_bos", False)
      data_utils.validate_corpus_exists(
          corpus=corpus, split=split, is_npz=is_npz
      )
  
      if self.char_target_dict is not None:
          dst_dataset = (
              char_data.InMemoryNumpyWordCharDataset.create_from_file(
                  corpus.target.data_file
              )
          )
      else:
          dst_dataset = pytorch_translate_data.InMemoryIndexedDataset.create_from_file(
              corpus.target.data_file, is_npz=is_npz
          )
  
      if getattr(self.args, "reverse_target", None):
          dst_dataset.reverse()
      weights_dataset = None
      if corpus.weights_file and os.path.exists(corpus.weights_file):
          weights_dataset = weighted_data.IndexedWeightsDataset(
              corpus.weights_file
          )
          print(
              f"dst: {len(dst_dataset)} lines, weights: {len(weights_dataset)} lines."
          )
          assert len(dst_dataset) == len(weights_dataset)
  
      if self.char_source_dict is not None:
          src_dataset = (
              char_data.InMemoryNumpyWordCharDataset.create_from_file(
                  corpus.source.data_file
              )
          )
          char_data_class = (
              char_data.LanguagePairCharDataset
              if self.char_target_dict is not None
              else char_data.LanguagePairSourceCharDataset
          )
          self.datasets[split] = char_data_class(
              src=src_dataset,
              src_sizes=src_dataset.sizes,
              src_dict=self.source_dictionary,
              tgt=dst_dataset,
              tgt_sizes=dst_dataset.sizes,
              tgt_dict=self.target_dictionary,
              weights=weights_dataset,
          )
      else:
          src_dataset = pytorch_translate_data.InMemoryIndexedDataset.create_from_file(
              corpus.source.data_file, is_npz=is_npz
          )
          if getattr(self.args, "train_weights_path", None):
              self.datasets[
                  split
              ] = weighted_data.WeightedLanguagePairDataset(
                  src=src_dataset,
                  src_sizes=src_dataset.sizes,
                  src_dict=self.source_dictionary,
                  tgt=dst_dataset,
                  tgt_sizes=dst_dataset.sizes,
                  tgt_dict=self.target_dictionary,
                  weights=weights_dataset,
                  left_pad_source=False,
              )
          else:
              self.datasets[split] = LanguagePairDataset(
                  src=src_dataset,
                  src_sizes=src_dataset.sizes,
                  src_dict=self.source_dictionary,
                  tgt=dst_dataset,
                  tgt_sizes=dst_dataset.sizes,
                  tgt_dict=self.target_dictionary,
                  append_bos=append_bos,
                  left_pad_source=False,
              )
  
imports: import os
  from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple
  import numpy
  from fairseq import data, options
  from fairseq.data import ConcatDataset, LanguagePairDataset, NoisingDataset
  from fairseq.data.multi_corpus_sampled_dataset import MultiCorpusSampledDataset
  from fairseq.data.noising import UnsupervisedMTNoising
  from fairseq.tasks import LegacyFairseqTask, register_task
  from pytorch_translate import constants, utils
  from pytorch_translate.data import char_data, data, dictionary, utils, weighted_data
  from pytorch_translate.data.language_pair_upsampling_dataset import LanguagePairUpsamplingDataset
  from pytorch_translate.research.multisource import multisource_data
label_desc: Use image node_id 5 for calling the PytorchTranslateTask obj's underlying member method code with example usage: obj._load_dataset_single_path(split, src_bin_path, tgt_bin_path, weights_file, is_npz) without return types