code: def main(args):
      model_path = args.model_path
      pawsx_tasks = args.pawsx_tasks
      assert all(task in PAWS_TASKS for task in pawsx_tasks)
  
      datasets = {}
      val_dfs = {}
      test_dfs = {}
      for task in args.pawsx_tasks:
          datasets[task] = load_dataset("paws-x", task)
          val_dfs[task] = datasets[task]["validation"].to_pandas()
          test_dfs[task] = datasets[task]["test"].to_pandas()
          print(
              "task %s: val %d, test %d"
              % (task, len(val_dfs[task]), len(test_dfs[task]))
          )
      val_df = pd.concat(val_dfs)
      test_dfs["all"] = pd.concat(test_dfs)
      test_dfs["val"] = val_df
  
      result = {}
      predictor = MultiModalPredictor.load(model_path)
      start = time()
      for test_name, test_df in test_dfs.items():
          result[test_name] = predictor.evaluate(
              data=test_df, metrics="accuracy"
          )
      usedtime = time() - start
  
      for test_name in test_dfs.keys():
          print("Distillation Result (%s):" % test_name)
          print("Model: %s" % model_path)
          for k in result[test_name]:
              print(f"For metric {k}:")
              print("Model's %s: %.6f" % (k, result[test_name][k]))
      print("Teacher Model's time: %.6f" % usedtime)
  
file_path: autogluon/examples/automm/distillation/eval_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: main(args) without return types