code: def test_ner(checkpoint_name, searcher, scheduler):
      train_data = get_data()
      label_col = "entity_annotations"
  
      if searcher is None:
          lr = 0.0001
          hyperparameter_tune_kwargs = None
      else:
          lr = tune.uniform(0.0001, 0.01)
          hyperparameter_tune_kwargs = {
              "num_trials": 2,
              "searcher": searcher,
              "scheduler": scheduler,
          }
      predictor = MultiModalPredictor(
          problem_type="ner", label=label_col
      )
      predictor.fit(
          train_data=train_data,
          time_limit=60,
          hyperparameters={
              "model.ner_text.checkpoint_name": checkpoint_name,
              "optimization.learning_rate": lr,
          },
          hyperparameter_tune_kwargs=hyperparameter_tune_kwargs,
      )
  
      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)
      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 2 for calling a global function with example usage: test_ner(checkpoint_name, searcher, scheduler) without return types