code: def load_indexed_dataset(
      path,
      dictionary=None,
      dataset_impl=None,
      combine=False,
      default="cached",
  ):
      """A helper function for loading indexed datasets.
  
      Args:
          path (str): path to indexed dataset (e.g., 'data-bin/train')
          dictionary (~fairseq.data.Dictionary): data dictionary
          dataset_impl (str, optional): which dataset implementation to use. If
              not provided, it will be inferred automatically. For legacy indexed
              data we use the 'cached' implementation by default.
          combine (bool, optional): automatically load and combine multiple
              datasets. For example, if *path* is 'data-bin/train', then we will
              combine 'data-bin/train', 'data-bin/train1', ... and return a
              single ConcatDataset instance.
      """
      import fairseq.data.indexed_dataset as indexed_dataset
      from fairseq.data.concat_dataset import ConcatDataset
  
      datasets = []
      for k in itertools.count():
          path_k = path + (str(k) if k > 0 else "")
          try:
              path_k = indexed_dataset.get_indexed_dataset_to_local(
                  path_k
              )
          except Exception as e:
              if "StorageException: [404] Path not found" in str(e):
                  logger.warning(f"path_k: {e} not found")
              else:
                  raise e
  
          dataset_impl_k = dataset_impl
          if dataset_impl_k is None:
              dataset_impl_k = indexed_dataset.infer_dataset_impl(
                  path_k
              )
          dataset = indexed_dataset.make_dataset(
              path_k,
              impl=dataset_impl_k or default,
              fix_lua_indexing=True,
              dictionary=dictionary,
          )
          if dataset is None:
              break
          logger.info(
              "loaded {:,} examples from: {}".format(
                  len(dataset), path_k
              )
          )
          datasets.append(dataset)
          if not combine:
              break
      if len(datasets) == 0:
          return None
      elif len(datasets) == 1:
          return datasets[0]
      else:
          return ConcatDataset(datasets)
  
imports: import contextlib
  import itertools
  import logging
  import re
  import warnings
  from typing import Optional, Tuple
  import math
  import numpy
  import torch
  from fairseq.file_io import PathManager
  from fairseq import utils
  import os
label_desc: Use image node_id 3 for calling a global function with example usage: load_indexed_dataset(path, dictionary, dataset_impl, combine, default) and returns: None, datasets, ConcatDataset