class_docstr: DAS beamformed phantom images and paired post-processed images.
class_docstr_tok: ["DAS","beamformed","phantom","images","and","paired","post-processed","images","."]
docstr: custom_csv_splits is a dictionary of { 'name': 'csvpaths'}.
docstr_tok: ["custom_csv_splits","is","a","dictionary","of","{","'name","'",":","'csvpaths","'","}","."]
code: def __init__(self, custom_csv_splits=None, **kwargs):
      """custom_csv_splits is a dictionary of { 'name': 'csvpaths'}."""
      super(Builder, self).__init__(**kwargs)
      self.custom_csv_splits = (
          custom_csv_splits if custom_csv_splits else {}
      )
  
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 1 to create a new Builder object from inherited base classes: tfds.core with example: obj = Builder(custom_csv_splits)