code: def get_config(run_local: str = "") -> ml_collections.ConfigDict:
      """Returns the experiment configuration."""
      config = mixin.get_config(run_local)
  
      run_local = bool(run_local)
  
      config.dataset_name = "bit"
  
      config.dataset_configs = ml_collections.ConfigDict()
  
      # We need to re-add this here in case we merge a config with an eval config in
      # which the datasets have different values for them.
      config.dataset_configs.visual_key = "image"
      config.dataset_configs.is_video = False
      config.dataset_configs.text_in_key = "texts"
  
      config.dataset_configs.train_cache = None
      config.dataset_configs.val_cache = "batched"
  
      config.dataset_configs.train_split_name = "full"
      config.dataset_configs.train_split_start = "50000"
      config.dataset_configs.train_split_end = ""
      config.dataset_configs.train_split = _split(
          name=config.dataset_configs.get_ref("train_split_name"),
          start=config.dataset_configs.get_ref("train_split_start"),
          end=config.dataset_configs.get_ref("train_split_end"),
      )
  
      config.dataset_configs.val_split_name = "full"
      config.dataset_configs.val_split_start = ""
      config.dataset_configs.val_split_end = (
          "4" if run_local else "50000"
      )
      config.dataset_configs.val_split = _split(
          name=config.dataset_configs.get_ref("val_split_name"),
          start=config.dataset_configs.get_ref("val_split_start"),
          end=config.dataset_configs.get_ref("val_split_end"),
      )
  
      return config
  
label_desc: Use image node_id 2 for calling a global function with example usage: get_config(run_local) and returns: config