code: def _generate_ungrounded_on_grounded_builder_configs():
      """Generate configs for random splits training on ungrounded and testing on grounded data."""
      configs = []
      for size in _NONBLENDED_TRAIN_SET_SIZES["u"]:
          # Out of the usual train set sizes for u-CFQ, there was no split created
          # with 6x u-CFQ train set and an o-CFQ test set.
          if size == 6:
              continue
          configs.append(
              StarCFQConfig(
                  name=f"ungrounded_on_grounded_{size}x",
                  split_archive_path="splits/star-cfq-random-splits-1.0.tar.gz",
                  split_path=f"random_splits/train_{size}x_u_cfq__test_1x_o_cfq.json",
              )
          )
      return configs
  
imports: import json
  import os
  from absl import logging
  from etils import epath
  import tensorflow_datasets.public_api
label_desc: Use image node_id 3 for calling a global function with example usage: _generate_ungrounded_on_grounded_builder_configs() and returns: configs