code: def test_multi_column_ner(checkpoint_name):
      train_data = get_data()
      train_data["add_text"] = train_data.text_snippet
      label_col = "entity_annotations"
      predictor = MultiModalPredictor(
          problem_type="ner", label=label_col
      )
      predictor.fit(
          train_data=train_data,
          time_limit=10,
          column_types={"text_snippet": "text_ner"},
          hyperparameters={
              "model.ner_text.checkpoint_name": checkpoint_name
          },
      )
      scores = predictor.evaluate(train_data)
      test_predict = train_data.drop(label_col, axis=1)
      test_predict = test_predict.head(2)
      predictions = predictor.predict(test_predict)
      proba = predictor.predict_proba(test_predict)
      embeddings = predictor.extract_embedding(test_predict)
  
file_path: autogluon/multimodal/tests/unittests/others/test_ner.py
imports: import os
  import shutil
  import tempfile
  from unittest import mock
  import numpy.testing
  import pandas
  import pytest
  import torch
  from ray import tune
  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_multi_column_ner(checkpoint_name) without return types