code: def getDatasetSplits(pawsx_tasks):
      datasets = {}
      train_dfs = {}
      val_dfs = {}
      test_dfs = {}
      for task in pawsx_tasks:
          datasets[task] = load_dataset("paws-x", task)
          train_dfs[task] = datasets[task]["train"].to_pandas()
          val_dfs[task] = datasets[task]["validation"].to_pandas()
          test_dfs[task] = datasets[task]["test"].to_pandas()
          print(
              "task %s: train %d, val %d, test %d"
              % (
                  task,
                  len(train_dfs[task]),
                  len(val_dfs[task]),
                  len(test_dfs[task]),
              )
          )
      train_df = pd.concat(train_dfs)
      val_df = pd.concat(val_dfs)
      test_dfs["all"] = pd.concat(test_dfs)
  
      return train_df, val_df, test_dfs
  
file_path: autogluon/examples/automm/distillation/automm_distillation_pawsx.py
imports: import argparse
  from autogluon.multimodal import MultiModalPredictor
  from datasets import load_dataset
  from time import time
  import os
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: getDatasetSplits(pawsx_tasks) and returns: train_df,  val_df,  test_dfs