code: def test_doc_classification(checkpoint_name):
      predictor = MultiModalPredictor(label="label")
      train_data, test_data = get_rvl_cdip_sample_data()
      predictor.fit(
          train_data=train_data,
          hyperparameters={
              "model.document_transformer.checkpoint_name": checkpoint_name,
              "env.num_workers": 0,
          },
          time_limit=30,
      )
  
      predictor.evaluate(test_data)
  
      doc_path = test_data.iloc[0][DOC_PATH_COL]
      # make predictions
      predictions = predictor.predict({DOC_PATH_COL: [doc_path]})
      # output probability
      proba = predictor.predict_proba({DOC_PATH_COL: [doc_path]})
      # extract embeddings
      feature = predictor.extract_embedding({DOC_PATH_COL: [doc_path]})
  
file_path: autogluon/multimodal/tests/unittests/others_2/test_classify_doc.py
imports: import os
  import shutil
  import tempfile
  from unittest import mock
  import numpy.testing
  import pandas
  import pytest
  import torch
  from autogluon.core.utils.loaders import load_zip
  from autogluon.multimodal import MultiModalPredictor
  from ..utils.utils import get_home_dir
label_desc: Use image node_id 3 for calling a global function with example usage: test_doc_classification(checkpoint_name) without return types