code: def test_modifying_duplicate_model_names():
      dataset = ALL_DATASETS["petfinder"]
      metric_name = dataset.metric
  
      teacher_predictor = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=metric_name,
      )
  
      hyperparameters = {
          "optimization.max_epochs": 1,
          "model.names": [
              "numerical_mlp",
              "categorical_mlp",
              "timm_image",
              "hf_text",
              "fusion_mlp",
          ],
          "model.hf_text.checkpoint_name": "nlpaueb/legal-bert-small-uncased",
          "model.timm_image.checkpoint_name": "swin_tiny_patch4_window7_224",
          "env.num_workers": 0,
          "env.num_workers_evaluation": 0,
      }
  
      teacher_predictor.fit(
          train_data=dataset.train_df,
          hyperparameters=hyperparameters,
          time_limit=1,
      )
      student_predictor = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=metric_name,
      )
      student_predictor.fit(
          train_data=dataset.train_df,
          time_limit=0,
      )
  
      teacher_predictor._learner = modify_duplicate_model_names(
          learner=teacher_predictor._learner,
          postfix="teacher",
          blacklist=student_predictor._learner._config.model.names,
      )
  
      # verify teacher and student have no duplicate model names
      assert all(
          [
              n not in teacher_predictor._learner._config.model.names
              for n in student_predictor._learner._config.model.names
          ]
      ), (
          f"teacher model names {teacher_predictor._learner._config.model.names} and"
          f" student model names {student_predictor._learner._config.model.names} have duplicates."
      )
  
      # verify each model name prefix is valid
      assert (
          teacher_predictor._learner._model.prefix
          in teacher_predictor._learner._config.model.names
      )
      if isinstance(
          teacher_predictor._learner._model.model, nn.ModuleList
      ):
          for per_model in teacher_predictor._learner._model.model:
              assert (
                  per_model.prefix
                  in teacher_predictor._learner._config.model.names
              )
  
      # verify each data processor's prefix is valid
      for (
          per_modality_processors
      ) in teacher_predictor._learner._data_processors.values():
          for per_processor in per_modality_processors:
              assert (
                  per_processor.prefix
                  in teacher_predictor._learner._config.model.names
              )
  
imports: import copy
  import os
  import shutil
  import tempfile
  import uuid
  import numpy.testing
  import pytest
  from omegaconf import OmegaConf
  from torch import nn
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.multimodal.constants import BEST, BINARY, BIT_FIT, DATA, DISTILLER, ENVIRONMENT, GREEDY_SOUP, IA3, LORA, LORA_BIAS, LORA_NORM, MODEL, MULTICLASS, NORM_FIT, OPTIMIZATION, UNIFORM_SOUP
  from autogluon.multimodal.utils import modify_duplicate_model_names
  from autogluon.multimodal.utils.misc import shopee_dataset
  from ..utils.unittest_datasets import AEDataset, HatefulMeMesDataset, PetFinderDataset
  from ..utils.utils import get_home_dir
label_desc: Use image node_id 8 for calling a global function with example usage: test_modifying_duplicate_model_names() without return types