code: def _load_fake_dataset(cfg_data, details, path=None):
      tokenizer = load_tokenizer(
          cfg_data.tokenizer,
          cfg_data.seq_length,
          cfg_data.vocab_size,
          cache_dir=path,
      )
      tokenizer.model_max_length = cfg_data.seq_length
      generator = torch.Generator()
      generator.manual_seed(details.randgen_seed)
      dataset = torch.randint(
          0,
          cfg_data.vocab_size,
          (details.size, cfg_data.seq_length),
          generator=generator,
      )
      return dataset, tokenizer
  
imports: import torch
  import datasets
  import hydra
  import os
  import contextlib
  import logging
  import tempfile
  from itertools import chain
  from collections import defaultdict
  import json
  from omegaconf import OmegaConf
  from .tokenizer_preparation import construct_tokenizer, load_tokenizer
  from .curriculum_sorting import _sort_tokenized_dataset_by_unigram, _sort_tokenized_dataset_by_token, _sort_tokenized_dataset_by_word_length
  from .deduplicate import deduplicate_huggingface_dataset
  from .utils import checksum_config, stage_dataset, detailed_OSError
label_desc: Use image node_id 5 for calling a global function with example usage: _load_fake_dataset(cfg_data, details, path) and returns: dataset,  tokenizer