class_docstr: DAS beamformed phantom images and paired post-processed images.
class_docstr_tok: ["DAS","beamformed","phantom","images","and","paired","post-processed","images","."]
code: def _split_generators(self, dl_manager):
      downloads = _DEFAULT_SPLITS.copy()
      downloads.update(_DATA_URL)
      dl_paths = dl_manager.download_and_extract(downloads)
      splits = [
          tfds.core.SplitGenerator(  # pylint:disable=g-complex-comprehension
              name=name,
              gen_kwargs={
                  "datapath": {
                      "mark_data": dl_paths["mark_data"],
                      "phantom_data": dl_paths["phantom_data"],
                  },
                  "csvpath": dl_paths[name],
              },
          )
          for name, _ in _DEFAULT_SPLITS.items()
      ]
  
      for name, csv_path in self.custom_csv_splits.items():
          splits.append(
              tfds.core.SplitGenerator(
                  name=name,
                  gen_kwargs={
                      "datapath": dl_paths["data"],
                      "csvpath": csv_path,
                  },
              )
          )
  
      return splits
  
file_path: datasets/tensorflow_datasets/datasets/duke_ultrasound/duke_ultrasound_dataset_builder.py
imports: import csv
  import os
  from etils import epath
  import numpy
  from tensorflow_datasets.core.utils import bool_utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  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: splits