class_docstr: Omniglot dataset.
class_docstr_tok: ["Omniglot","dataset","."]
code: def _split_generators(self, dl_manager):
      extracted_dirs = dl_manager.download_and_extract(_DL_URLS)
  
      # Get all alphabets and labels
      alphabets, label_names = _get_names(extracted_dirs.values())
      self.info.features["alphabet"].names = sorted(alphabets)
      self.info.features["label"].names = label_names
  
      return [
          tfds.core.SplitGenerator(
              name=tfds.Split.TRAIN,
              gen_kwargs={
                  "directory": extracted_dirs["train"],
              },
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.TEST,
              gen_kwargs={
                  "directory": extracted_dirs["eval"],
              },
          ),
          tfds.core.SplitGenerator(
              name="small1",
              gen_kwargs={
                  "directory": extracted_dirs["small1"],
              },
          ),
          tfds.core.SplitGenerator(
              name="small2",
              gen_kwargs={
                  "directory": extracted_dirs["small2"],
              },
          ),
      ]
  
file_path: datasets/tensorflow_datasets/image_classification/omniglot.py
imports: import os
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling the Omniglot obj's underlying member method code with example usage: obj._split_generators(dl_manager) and returns: list