code: def test_transform_config():
      """Tests that the transform_config of
      the Preprocessor is respected during transform."""
  
      batch_size = 2
  
      class DummyPreprocessor(Preprocessor):
          _is_fittable = False
  
          def _transform_numpy(self, data):
              assert len(data["value"]) == batch_size
              return data
  
          def _transform_pandas(self, data):
              raise RuntimeError(
                  "Pandas transform should not be called with numpy batch format."
              )
  
          def _get_transform_config(self):
              return {"batch_size": 2}
  
          def _determine_transform_to_use(self):
              return "numpy"
  
      prep = DummyPreprocessor()
      ds = ray.data.from_pandas(pd.DataFrame({"value": list(range(4))}))
      prep.transform(ds)
  
imports: import re
  import warnings
  from typing import Dict, Union
  from unittest.mock import patch
  import numpy
  import pandas
  import pyarrow
  import pytest
  import ray
  from ray.air.constants import MAX_REPR_LENGTH
  from ray.air.util.data_batch_conversion import BatchFormat
  from ray.data.preprocessor import Preprocessor
  from ray.data.preprocessors import BatchMapper, Categorizer, Chain, Concatenator, CountVectorizer, FeatureHasher, HashingVectorizer, LabelEncoder, MaxAbsScaler, MinMaxScaler, MultiHotEncoder, Normalizer, OneHotEncoder, OrdinalEncoder, PowerTransformer, RobustScaler, SimpleImputer, StandardScaler, Tokenizer
label_desc: Use image node_id 6 for calling a global function with example usage: test_transform_config() and returns: data, dict, str