code: def _generate_blended_split_builder_configs():
      """Generate configs for blended random splits."""
      configs = []
      split_archive_path = "splits/star-cfq-random-splits-1.0.tar.gz"
      for supplementary_pool in _SUPPLEMENTARY_POOLS:
          for initial_size in _BLENDING_INITIAL_SIZES:
              for supplementary_size in _BLENDING_SUPPLEMENTARY_SIZES:
                  # Some splits with 80x n-CFQ/half-n-CFQ sizes are missing, skip them.
                  if supplementary_size == 80 and (
                      supplementary_pool in ["n", "half_n"]
                      or initial_size not in [0, 0.1, 1]
                  ):
                      continue
                  if initial_size != 0:
                      configs.append(
                          StarCFQConfig(
                              name=f"equal_weighting_{initial_size}x_b_cfq_{supplementary_size}x_{supplementary_pool}_cfq",
                              split_archive_path=split_archive_path,
                              split_path=f"random_splits/train_{initial_size}x_b_cfq_{supplementary_size}x_{supplementary_pool}_cfq__test_1x_b_cfq.json",
                          )
                      )
                  else:
                      configs.append(
                          StarCFQConfig(
                              name=f"equal_weighting_0x_b_cfq_{supplementary_size}x_{supplementary_pool}_cfq",
                              split_archive_path=split_archive_path,
                              split_path=f"random_splits/train_{supplementary_size}x_{supplementary_pool}_cfq__test_1x_b_cfq.json",
                          )
                      )
      for unique_initial_size in _UNIQUE_INITIAL_SIZES:
          for supplementary_pool in _SUPPLEMENTARY_POOLS:
              for size in _OVERWEIGHTING_SIZES:
                  # Splits with 80x n-CFQ/half-n-CFQ sizes are missing, skip them for now.
                  if size == 80 and supplementary_pool in [
                      "n",
                      "half_n",
                  ]:
                      continue
                  # This split file is missing, skip it in this version.
                  if (
                      unique_initial_size == 1
                      and size == 1
                      and supplementary_pool == "half_n"
                  ):
                      continue
                  configs.append(
                      StarCFQConfig(
                          name=f"overweighting_{unique_initial_size}x_unique_{size}x_b_cfq_{size}x_{supplementary_pool}_cfq",
                          split_archive_path=split_archive_path,
                          split_path=f"random_splits/train_{unique_initial_size}x_unique_{size}x_b_cfq_{size}x_{supplementary_pool}_cfq__test_1x_b_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 4 for calling a global function with example usage: _generate_blended_split_builder_configs() and returns: configs