code: def test_distillation():
      dataset = PetFinderDataset()
  
      teacher_hyperparameters = {
          "optimization.max_epochs": 1,
          "model.names": ["hf_text", "timm_image", "fusion_mlp"],
          "model.hf_text.checkpoint_name": "google/electra-small-discriminator",
          "model.timm_image.checkpoint_name": "swin_tiny_patch4_window7_224",
          "env.num_workers": 0,
          "env.num_workers_evaluation": 0,
      }
  
      # test for distillation with different model structures
      student_hyperparameters = {
          "optimization.max_epochs": 1,
          "model.names": ["hf_text", "timm_image", "fusion_mlp"],
          "model.hf_text.checkpoint_name": "sentence-transformers/all-MiniLM-L6-v2",
          "model.timm_image.checkpoint_name": "swin_tiny_patch4_window7_224",
          "env.num_workers": 0,
          "env.num_workers_evaluation": 0,
          "distiller.temperature": 5.0,
          "distiller.hard_label_weight": 0.1,
          "distiller.soft_label_weight": 1.0,
          "distiller.output_feature_loss_weight": 0.01,
          "distiller.rkd_distance_loss_weight": 0.01,
          "distiller.rkd_angle_loss_weight": 0.02,
          "distiller.output_feature_loss_type": "mse",
      }
  
      teacher_predictor = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=dataset.metric,
      )
  
      teacher_save_path = os.path.join(
          get_home_dir(), "petfinder", "teacher"
      )
      if os.path.exists(teacher_save_path):
          shutil.rmtree(teacher_save_path)
  
      teacher_predictor = teacher_predictor.fit(
          train_data=dataset.train_df,
          hyperparameters=teacher_hyperparameters,
          time_limit=10,
          save_path=teacher_save_path,
      )
  
      # test for distillation
      predictor = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=dataset.metric,
      )
  
      student_save_path = os.path.join(
          get_home_dir(), "petfinder", "student"
      )
      if os.path.exists(student_save_path):
          shutil.rmtree(student_save_path)
  
      predictor = predictor.fit(
          train_data=dataset.train_df,
          teacher_predictor=teacher_predictor,
          hyperparameters=student_hyperparameters,
          time_limit=10,
          save_path=student_save_path,
      )
      verify_predictor_save_load(predictor, dataset.test_df)
  
      # test for distillation with teacher predictor path
      predictor = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=dataset.metric,
      )
  
      student_save_path = os.path.join(
          get_home_dir(), "petfinder", "student"
      )
      if os.path.exists(student_save_path):
          shutil.rmtree(student_save_path)
  
      predictor = predictor.fit(
          train_data=dataset.train_df,
          teacher_predictor=teacher_predictor.path,
          hyperparameters=student_hyperparameters,
          time_limit=10,
          save_path=student_save_path,
      )
  
      verify_predictor_save_load(predictor, dataset.test_df)
  
imports: import os
  import shutil
  from autogluon.multimodal import MultiModalPredictor
  from ..predictor.test_predictor import verify_predictor_save_load
  from ..utils.unittest_datasets import AEDataset, HatefulMeMesDataset, PetFinderDataset
  from ..utils.utils import get_home_dir
label_desc: Use image node_id 1 for calling a global function with example usage: test_distillation() without return types