code: def test_predict_feature_columns_pandas():
      pandas_data = pd.DataFrame(dummy_data, columns=["A", "B"])
      pandas_target = pd.Series(dummy_target)
      pandas_model = RandomForestClassifier(
          n_estimators=10, random_state=0
      ).fit(pandas_data, pandas_target)
      preprocessor = DummyPreprocessor()
      predictor = SklearnPredictor(
          estimator=pandas_model, preprocessor=preprocessor
      )
      data_batch = pd.DataFrame(
          np.array([[1, 2, 7], [3, 4, 8], [5, 6, 9]]),
          columns=["A", "B", "C"],
      )
      predictions = predictor.predict(
          data_batch, feature_columns=["A", "B"]
      )
  
      assert len(predictions) == 3
      assert predictor.get_preprocessor().has_preprocessed
  
file_path: ray/python/ray/train/tests/test_sklearn_predictor.py
imports: import re
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import RandomForestClassifier
  from ray.air.constants import MAX_REPR_LENGTH
  from ray.air.util.data_batch_conversion import _convert_pandas_to_batch_type
  from ray.train.predictor import TYPE_TO_ENUM
  from ray.train.sklearn import SklearnCheckpoint, SklearnPredictor
  from ray.train.tests.dummy_preprocessor import DummyPreprocessor
label_desc: Use image node_id 6 for calling a global function with example usage: test_predict_feature_columns_pandas() without return types