code: def test_numpy_pandas_support_transform_batch_arrow(
      create_dummy_preprocessors,
  ):
      # Case 3: arrow dataset
      (
          with_nothing,
          with_pandas,
          with_numpy,
          with_pandas_and_numpy,
          with_pandas_and_numpy_preferred,
      ) = create_dummy_preprocessors
  
      df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"])
      df_single_column = pd.DataFrame([1, 2, 3], columns=["A"])
  
      table = pyarrow.Table.from_pandas(df)
      table_single_column = pyarrow.Table.from_pandas(df_single_column)
      with pytest.raises(NotImplementedError):
          with_nothing.transform_batch(table)
      with pytest.raises(NotImplementedError):
          with_nothing.transform_batch(table_single_column)
  
      assert isinstance(
          with_pandas.transform_batch(table), pd.DataFrame
      )
      assert isinstance(
          with_pandas.transform_batch(table_single_column), pd.DataFrame
      )
  
      assert isinstance(
          with_numpy.transform_batch(table), (np.ndarray, dict)
      )
      # We can get pyarrow.Table after returning numpy data from UDF
      assert isinstance(
          with_numpy.transform_batch(table_single_column),
          (np.ndarray, dict),
      )
  
      assert isinstance(
          with_pandas_and_numpy.transform_batch(table), pd.DataFrame
      )
      assert isinstance(
          with_pandas_and_numpy.transform_batch(table_single_column),
          pd.DataFrame,
      )
  
      assert isinstance(
          with_pandas_and_numpy_preferred.transform_batch(table),
          (np.ndarray, dict),
      )
      assert isinstance(
          with_pandas_and_numpy_preferred.transform_batch(
              table_single_column
          ),
          (np.ndarray, dict),
      )
  
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 10 for calling a global function with example usage: test_numpy_pandas_support_transform_batch_arrow(create_dummy_preprocessors) without return types