code: def _parse_labelstudio_arguments(
      export_json: str = None,
      train_export_json: str = None,
      val_export_json: str = None,
      test_export_json: str = None,
      predict_export_json: str = None,
      data_folder: str = None,
      train_data_folder: str = None,
      val_data_folder: str = None,
      test_data_folder: str = None,
      predict_data_folder: str = None,
      val_split: Optional[float] = None,
      multi_label: Optional[bool] = False,
  ):
      train_data = None
      val_data = None
      test_data = None
      predict_data = None
      data = {
          "data_folder": data_folder,
          "export_json": export_json,
          "multi_label": multi_label,
      }
  
      if (train_data_folder or data_folder) and train_export_json:
          train_data = {
              "data_folder": train_data_folder or data_folder,
              "export_json": train_export_json,
              "multi_label": multi_label,
          }
      if (val_data_folder or data_folder) and val_export_json:
          val_data = {
              "data_folder": val_data_folder or data_folder,
              "export_json": val_export_json,
              "multi_label": multi_label,
          }
      if (test_data_folder or data_folder) and test_export_json:
          test_data = {
              "data_folder": test_data_folder or data_folder,
              "export_json": test_export_json,
              "multi_label": multi_label,
          }
      if (predict_data_folder or data_folder) and predict_export_json:
          predict_data = {
              "data_folder": predict_data_folder or data_folder,
              "export_json": predict_export_json,
              "multi_label": multi_label,
          }
  
      train_data = train_data if train_data else data
  
      # TODO: Extract test from data if present.
  
      if val_split and val_data is None:
          train_data, val_data = LabelStudioInput._split_train_val_data(
              train_data, val_split
          )
  
      return train_data, val_data, test_data, predict_data
  
imports: import json
  import os
  from copy import deepcopy
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Any, Dict, List, Mapping, Optional, Sequence, Set, Type
  import numpy
  import torch
  from pytorch_lightning.utilities.cloud_io import get_filesystem
  from torch.utils.data import Sampler
  from flash.core.data.io.input import DataKeys, Input, IterableInput
  from flash.core.data.properties import Properties
  from flash.core.data.utilities.loading import load_image
  from flash.core.utilities.imports import _PYTORCHVIDEO_AVAILABLE
  from flash.core.utilities.stages import RunningStage
label_desc: Use image node_id 3 for calling a global function with example usage: _parse_labelstudio_arguments(export_json, train_export_json, val_export_json, test_export_json, predict_export_json, data_folder, train_data_folder, val_data_folder, test_data_folder, predict_data_folder, val_split, multi_label) and returns: train_data,  val_data,  test_data,  predict_data