class_docstr: IMDB movie reviews dataset.
code: def _split_generators(self, dl_manager):
      arch_path = dl_manager.download(_DOWNLOAD_URL)
      archive = lambda: dl_manager.iter_archive(arch_path)
  
      # Generate vocabulary from training data if SubwordTextEncoder configured
      self.info.features["text"].maybe_build_from_corpus(
          self._vocab_text_gen(archive())
      )
  
      return [
          tfds.core.SplitGenerator(
              name=tfds.Split.TRAIN,
              gen_kwargs={
                  "archive": archive(),
                  "directory": os.path.join("aclImdb", "train"),
              },
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.TEST,
              gen_kwargs={
                  "archive": archive(),
                  "directory": os.path.join("aclImdb", "test"),
              },
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split("unsupervised"),
              gen_kwargs={
                  "archive": archive(),
                  "directory": os.path.join("aclImdb", "train"),
                  "labeled": False,
              },
          ),
      ]
  
file_path: datasets/tensorflow_datasets/datasets/imdb_reviews/imdb_reviews_dataset_builder.py
imports: import os
  import re
  import tensorflow_datasets.public_api
label_desc: Use image node_id 3 for calling the Builder obj's underlying member method code with example usage: obj._split_generators(dl_manager) and returns: list