code: def main(args):
      assert args.glue_task in (
          list(GLUE_METRICS.keys()) + ["mnlim", "mnlimm"]
      ), "Unsupported dataset name."
  
      ### Dataset Loading
      if args.glue_task == "mnlimm":
          glue_task = "mnli"
          mnli_mismatched = True
      elif args.glue_task in ["mnli" or "mnlim"]:
          glue_task = "mnli"
          mnli_mismatched = False
      else:
          glue_task = args.glue_task
      dataset = load_dataset("glue", glue_task)
      train_df = dataset["train"].to_pandas().drop("idx", axis=1)
      if args.glue_task == "mnli":
          if mnli_mismatched:
              valid_df = dataset["validation_matched"].to_pandas()
          else:
              valid_df = dataset["validation_mismatched"].to_pandas()
      else:
          valid_df = dataset["validation"].to_pandas()
  
      teacher_predictor_name = (
          f"{args.glue_task}-{args.teacher_model.replace('/', '-')}"
      )
      teacher_predictor_path = os.path.join(
          args.save_path, teacher_predictor_name
      )
      nodistill_predictor_name = (
          f"{args.glue_task}-{args.student_model.replace('/', '-')}"
      )
      nodistill_predictor_path = os.path.join(
          args.save_path, nodistill_predictor_name
      )
  
      ### Train and evaluate the teacher model
      resume_teacher = args.resume
      try:
          teacher_predictor = MultiModalPredictor.load(
              teacher_predictor_path
          )
          print(
              "Using pretrained teacher model: %s"
              % teacher_predictor_path
          )
      except:
          resume_teacher = False
          print("No pretrained model at: %s" % teacher_predictor_path)
      if not resume_teacher:
          teacher_predictor = MultiModalPredictor(
              label="label", eval_metric=GLUE_METRICS[glue_task]["val"]
          )
          teacher_predictor.fit(
              train_df,
              hyperparameters={
                  "env.num_gpus": args.num_gpu,
                  "model.hf_text.checkpoint_name": args.teacher_model,
                  "optimization.learning_rate": 1.0e-4,
                  "optimization.weight_decay": 1.0e-3,
              },
              time_limit=args.time_limit,
              seed=args.seed,
          )
          teacher_predictor.save(teacher_predictor_path)
      start = time()
      teacher_result = teacher_predictor.evaluate(
          data=valid_df, metrics=GLUE_METRICS[glue_task]["eval"]
      )
      teacher_usedtime = time() - start
  
      ### Train and evaluate a smaller pretrained model
      resume_nodistill = args.resume
      try:
          nodistill_predictor = MultiModalPredictor.load(
              nodistill_predictor_path
          )
          print(
              "Using pretrained nodistill model: %s"
              % nodistill_predictor_path
          )
      except:
          print("No pretrained model at: %s" % nodistill_predictor_path)
          resume_nodistill = False
      if not resume_nodistill and args.train_nodistill:
          nodistill_predictor = MultiModalPredictor(
              label="label", eval_metric=GLUE_METRICS[glue_task]["val"]
          )
          nodistill_predictor.fit(
              train_df,
              hyperparameters={
                  "env.num_gpus": args.num_gpu,
                  "optimization.max_epochs": args.max_epochs,
                  "model.hf_text.checkpoint_name": args.student_model,
                  "optimization.learning_rate": 1.0e-4,
                  "optimization.weight_decay": 1.0e-3,
              },
              time_limit=args.time_limit,
              seed=args.seed,
          )
          nodistill_predictor.save(nodistill_predictor_path)
          nodistill_result = nodistill_predictor.evaluate(
              data=valid_df, metrics=GLUE_METRICS[glue_task]["eval"]
          )
  
      ### Distill and evaluate a student model
  
      student_predictor = MultiModalPredictor(
          label="label", eval_metric=GLUE_METRICS[glue_task]["val"]
      )
      student_predictor.fit(
          train_df,
          hyperparameters={
              "env.num_gpus": args.num_gpu,
              "optimization.max_epochs": args.max_epochs,
              "model.hf_text.checkpoint_name": args.student_model,
              "optimization.learning_rate": 1.0e-4,
              "optimization.weight_decay": 1.0e-3,
              "distiller.temperature": args.temperature,
              "distiller.hard_label_weight": args.hard_label_weight,
              "distiller.soft_label_weight": args.soft_label_weight,
              "distiller.softmax_regression_weight": args.softmax_regression_weight,
              "distiller.output_feature_loss_weight": args.output_feature_loss_weight,
              "distiller.rkd_distance_loss_weight": args.rkd_distance_loss_weight,
              "distiller.rkd_angle_loss_weight": args.rkd_angle_loss_weight,
              "distiller.soft_label_loss_type": args.soft_label_loss_type,
              "distiller.softmax_regression_loss_type": args.softmax_regression_loss_type,
              "distiller.output_feature_loss_type": args.output_feature_loss_type,
              "model.hf_text.text_trivial_aug_maxscale": 0.0,
          },
          teacher_predictor=teacher_predictor,
          time_limit=args.time_limit,
          seed=args.seed,
      )
      start = time()
      student_result = student_predictor.evaluate(
          data=valid_df, metrics=GLUE_METRICS[glue_task]["eval"]
      )
      student_usedtime = time() - start
  
      ### Print distillation's performance
      print("Distillation Result:")
      print("Teacher Model: %s" % args.teacher_model)
      print("Student Model: %s" % args.student_model)
      for k in teacher_result:
          print(f"For metric {k}:")
          print("Teacher Model's %s: %.6f" % (k, teacher_result[k]))
          print(
              "Pretrained Model's %s: %.6f" % (k, nodistill_result[k])
          )
          print("Student Model's %s: %.6f" % (k, student_result[k]))
          print(
              "Distillation Ratio (the fraction of the teacher's performance achieved by the student): %.6f"
              % (
                  float(student_result[k] - nodistill_result[k])
                  / float(teacher_result[k] - nodistill_result[k])
              )
          )
      print("Teacher Model's time: %.6f" % teacher_usedtime)
      print("Student Model's time: %.6f" % student_usedtime)
      print("speed up: %.6fx" % (teacher_usedtime / student_usedtime))
  
imports: import argparse
  from autogluon.multimodal import MultiModalPredictor
  from datasets import load_dataset
  from time import time
  import os
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types