code: def test_autoconfig_preprocessing_balanced():
      df = _get_sample_df(np.array([0.33, 0.33, 0.34]))
  
      config = create_auto_config(
          dataset=df, target="category", time_limit_s=1
      )
  
      # Ensure our configs are using the latest Ludwig schema
      ModelConfig.from_dict(config)
  
      assert PREPROCESSING not in config
  
imports: import os
  import tempfile
  from typing import List, Set
  from unittest import mock
  import numpy
  import pandas
  import pytest
  from ludwig.api import LudwigModel
  from ludwig.constants import COLUMN, ENCODER, INPUT_FEATURES, NAME, OUTPUT_FEATURES, PREPROCESSING, SPLIT, TYPE
  from ludwig.schema.model_types.base import ModelConfig
  from ludwig.types import FeatureConfigDict, ModelConfigDict
  from ludwig.utils.misc_utils import merge_dict
  from tests.integration_tests.utils import binary_feature, category_feature, generate_data, image_feature, minio_test_creds, number_feature, private_param, remote_tmpdir, text_feature
  import dask.dataframe
  from ray.tune.experiment.trial import Trial
  from ludwig.automl import auto_train, create_auto_config, train_with_config
  from ludwig.automl.automl import OUTPUT_DIR
  from ludwig.hyperopt.execution import RayTuneExecutor
label_desc: Use image node_id 12 for calling a global function with example usage: test_autoconfig_preprocessing_balanced() without return types