code: def _transform_dataset(
      builder: dataset_builder.DatasetBuilder,
      split_info: splits_lib.SplitInfo,
      transformations: List[
          Callable[[tf.data.Dataset], tf.data.Dataset]
      ],
  ) -> split_builder_lib.SplitGenerator:
      """Yields transformed examples of the given builder and split.
  
      Arguments:
        builder: the builder whose data should be transformed.
        split_info: the split that needs to be transformed.
        transformations: the dataset transformations that need to be applied.
  
      Yields:
        transformed examples of the given builder and split.
      """
      row_read_config = read_config_lib.ReadConfig(add_tfds_id=False)
      ds = builder.as_dataset(
          split=split_info.name, read_config=row_read_config
      )
      for transformation in transformations:
          ds = transformation(ds)
      for i, example in enumerate(dataset_utils.as_numpy(ds)):
          key = i
          yield key, example
  
imports: from __future__ import annotations
  import functools
  from typing import Callable, Dict, Iterator, List, Optional, Union
  from tensorflow_datasets.core import beam_utils
  from tensorflow_datasets.core import dataset_builder
  from tensorflow_datasets.core import dataset_utils
  from tensorflow_datasets.core import load
  from tensorflow_datasets.core import naming
  from tensorflow_datasets.core import registered
  from tensorflow_datasets.core import split_builder
  from tensorflow_datasets.core import splits
  from tensorflow_datasets.core import transform
  from tensorflow_datasets.core.download import download_manager
  from tensorflow_datasets.core.utils import read_config
  from tensorflow_datasets.core.utils.lazy_imports_utils import apache_beam
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: _transform_dataset(builder, split_info, transformations) without return types