code: def test_predictor_with_spearman_pearson_eval(eval_metric):
      train_df = load_dataset("SetFit/stsb", split="train").to_pandas()
      predictor = MultiModalPredictor(
          label="label", eval_metric=eval_metric
      )
      predictor.fit(train_df, presets="medium_quality", time_limit=5)
      assert predictor.eval_metric == eval_metric
  
file_path: autogluon/multimodal/tests/unittests/others_2/test_predictor_advanced.py
imports: import os
  import shutil
  import numpy.testing
  import pytest
  from datasets import load_dataset
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.multimodal.constants import BIT_FIT, IA3, IA3_BIAS, IA3_LORA, LORA_BIAS, LORA_NORM, NORM_FIT
  from autogluon.multimodal.models.timm_image import TimmAutoModelForImagePrediction
  from autogluon.multimodal.utils.misc import shopee_dataset
  from ..utils.unittest_datasets import AmazonReviewSentimentCrossLingualDataset
label_desc: Use image node_id 4 for calling a global function with example usage: test_predictor_with_spearman_pearson_eval(eval_metric) without return types