code: def test_sentence_transformer_embedding(model_name):
      predictor = MultiModalPredictor(
          problem_type="feature_extraction",
          hyperparameters={"model.hf_text.checkpoint_name": model_name},
      )
      case1 = {"sentence": ["Hello world"]}
      case2 = {"sentence": ["Hello world", "Test Hello World"]}
  
      outputs_case1_from_df = predictor.extract_embedding(
          pd.DataFrame(case1)
      )
      outputs_case1_from_dict = predictor.extract_embedding(case1)
  
      npt.assert_allclose(
          outputs_case1_from_dict["sentence"],
          outputs_case1_from_df["sentence"],
      )
  
      outputs_case2_from_df = predictor.extract_embedding(
          pd.DataFrame(case2)
      )
      outputs_case2_from_dict = predictor.extract_embedding(case2)
      npt.assert_allclose(
          outputs_case2_from_df["sentence"],
          outputs_case2_from_dict["sentence"],
          1e-3,
          1e-3,
      )
      npt.assert_allclose(
          outputs_case2_from_df["sentence"][:1],
          outputs_case1_from_df["sentence"],
          1e-3,
          1e-3,
      )
  
imports: import numpy.testing
  import pandas
  import pytest
  from autogluon.multimodal import MultiModalPredictor
label_desc: Use image node_id 1 for calling a global function with example usage: test_sentence_transformer_embedding(model_name) without return types