code: def check_class_balance_preprocessing(
      config: "ModelConfig",
  ) -> None:  # noqa: F821
      """Class balancing is only available for datasets with a single output feature."""
      if (
          config.preprocessing.oversample_minority
          or config.preprocessing.undersample_majority
      ):
          if len(config.output_features) != 1:
              raise ConfigValidationError(
                  "Class balancing is only available for datasets with a single output feature."
              )
          if config.output_features[0].type != BINARY:
              raise ConfigValidationError(
                  "Class balancing is only supported for binary output features."
              )
  
imports: from abc import ABC, abstractmethod
  from re import findall
  from typing import Callable, TYPE_CHECKING
  from transformers import AutoConfig
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import AUDIO, BINARY, CATEGORY, IMAGE, IN_MEMORY, MIN_QUANTIZATION_BITS_FOR_MERGE_AND_UNLOAD, MODEL_ECD, MODEL_GBM, MODEL_LLM, NUMBER, SEQUENCE, SET, TEXT, TIMESERIES, VECTOR
  from ludwig.error import ConfigValidationError
  from ludwig.utils.metric_utils import get_feature_to_metric_names_map_from_feature_collection
  from ludwig.utils.misc_utils import merge_dict
label_desc: Use image node_id 11 for calling a global function with example usage: check_class_balance_preprocessing(config) without return types