code: def test_predictor_standalone():  # test standalone feature in MultiModalPredictor.save()
      from unittest import mock
  
      import torch
  
      requests_gag = mock.patch(
          "requests.Session.request",
          mock.Mock(
              side_effect=RuntimeError(
                  "Please use the `responses` library to mock HTTP in your tests."
              )
          ),
      )
  
      dataset = ALL_DATASETS["petfinder"]
  
      hyperparameters = {
          "optimization.max_epochs": 1,
          "model.names": [
              "numerical_mlp",
              "categorical_mlp",
              "timm_image",
              "hf_text",
              "fusion_mlp",
              "t_few",
          ],
          "model.hf_text.checkpoint_name": "nlpaueb/legal-bert-small-uncased",
          "model.timm_image.checkpoint_name": "swin_tiny_patch4_window7_224",
          "model.t_few.checkpoint_name": "t5-small",
          "env.num_workers": 0,
          "env.num_workers_evaluation": 0,
      }
  
      predictor = MultiModalPredictor(
          label=dataset.label_columns[0],
          problem_type=dataset.problem_type,
          eval_metric=dataset.metric,
      )
  
      save_path = os.path.join(get_home_dir(), "standalone", "false")
      if os.path.exists(save_path):
          shutil.rmtree(save_path)
  
      predictor.fit(
          train_data=dataset.train_df,
          hyperparameters=hyperparameters,
          time_limit=30,
          save_path=save_path,
      )
  
      save_path_standalone = os.path.join(
          get_home_dir(), "standalone", "true"
      )
  
      predictor.save(
          path=save_path_standalone,
          standalone=True,
      )
  
      # make sure the dumping doesn't affect predictor loading
      predictor.dump_model()
  
      del predictor
      torch.cuda.empty_cache()
  
      loaded_online_predictor = MultiModalPredictor.load(path=save_path)
      online_predictions = loaded_online_predictor.predict(
          dataset.test_df, as_pandas=False
      )
      del loaded_online_predictor
  
      # Check if the predictor can be loaded from an offline environment.
      with requests_gag:
          # No internet connection here. If any command require internet connection, a RuntimeError will be raised.
          with tempfile.TemporaryDirectory() as tmpdirname:
              torch.hub.set_dir(
                  tmpdirname
              )  # block reading files in `.cache`
              loaded_offline_predictor = MultiModalPredictor.load(
                  path=save_path_standalone
              )
  
      offline_predictions = loaded_offline_predictor.predict(
          dataset.test_df, as_pandas=False
      )
      del loaded_offline_predictor
  
      # check if save with standalone=True coincide with standalone=False
      npt.assert_equal(online_predictions, offline_predictions)
  
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 6 for calling a global function with example usage: test_predictor_standalone() without return types