class_docstr: Summarize a fraction of CNN/DM articles using the Wiki 64k vocab.
class_docstr_tok: ["Summarize","a","fraction","of","CNN\/DM","articles","using","the","Wiki","64k","vocab","."]
code: def generate_samples(self, data_dir, tmp_dir, dataset_split):
      del data_dir
      all_data = []
      all_files, urls_path = _maybe_download_corpora(
          tmp_dir, dataset_split
      )
      write_raw_text_to_files(
          all_files, urls_path, dataset_split, tmp_dir
      )
      for example in example_generator(
          all_files, urls_path, sum_token=True
      ):
          story, summary = _story_summary_split(example)
          all_data.append((story, summary))
  
      if dataset_split == problem.DatasetSplit.TRAIN:
          random.shuffle(all_data)
          fractional_len = int(self.fraction_of_data() * len(all_data))
          all_data = all_data[:fractional_len]
  
      for story, summary in all_data:
          yield {"inputs": story, "targets": summary}
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import hashlib
  import io
  import os
  import random
  import tarfile
  from tensor2tensor.data_generators import generator_utils
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.data_generators import wiki_lm
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 3 for calling the SummarizeFracCnnDailymailWikiLMSharedVocab64k obj's underlying member method code with example usage: obj.generate_samples(data_dir, tmp_dir, dataset_split) without return types