code: def register_config_transformation(
      version: str, prefixes: Union[str, List[str]] = []
  ) -> Callable:
      """This decorator registers a transformation function for a config version. Version is the first version which
      requires the transform. For example, since "training" is renamed to "trainer" in 0.5, this change should be
      registered with 0.5.  from_version < version <= to_version.
  
      Args:
          version: The version to register this transformation with. The earliest ludwig version which requires this
                   transformation.
          prefixes: A list of keypath prefixes to apply this transformation to. If not specified, transforms the entire
                    config dict. If a prefix indicates a list, i.e. "input_features", the transformation is applied to
                    each element of the list (each input feature).
      """
      if isinstance(prefixes, str):
          prefixes = [prefixes]
  
      def wrap(fn: Callable[[Dict], Dict]):
          config_transformation_registry.register(
              VersionTransformation(
                  transform=fn, version=version, prefixes=prefixes
              )
          )
          return fn
  
      return wrap
  
imports: import copy
  import logging
  import warnings
  from typing import Any, Callable, Dict, List, Union
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import AUDIO, BIAS, CLASS_WEIGHTS, COLUMN, CONV_BIAS, CONV_USE_BIAS, DECODER, DEFAULT_BIAS, DEFAULT_USE_BIAS, DEFAULTS, ENCODER, EVAL_BATCH_SIZE, EXECUTOR, FORCE_SPLIT, HEIGHT, HYPEROPT, IMAGE, INPUT_FEATURES, LOSS, MISSING_VALUE_STRATEGY, MODEL_ECD, MODEL_GBM, MODEL_TYPE, NAME, NUM_SAMPLES, NUMBER, OUTPUT_FEATURES, PARAMETERS, PREPROCESSING, PROBABILITIES, RANDOM, RAY, SAMPLER, SCHEDULER, SEARCH_ALG, SEQUENCE, SPLIT, SPLIT_PROBABILITIES, STRATIFY, TEXT, TIMESERIES, TRAINER, TRAINING, TYPE, USE_BIAS, WIDTH
  from ludwig.features.feature_registries import get_base_type_registry, get_input_type_registry, get_output_type_registry
  from ludwig.globals import LUDWIG_VERSION
  from ludwig.schema.defaults.gbm import GBMDefaultsConfig
  from ludwig.schema.encoders.utils import get_encoder_cls
  from ludwig.types import FeatureConfigDict, FeatureTypeDefaultsDict, HyperoptConfigDict, ModelConfigDict, PreprocessingConfigDict, TrainerConfigDict, TrainingSetMetadataDict
  from ludwig.utils.metric_utils import TrainerMetric
  from ludwig.utils.misc_utils import get_from_registry, merge_dict
  from ludwig.utils.version_transformation import VersionTransformation, VersionTransformationRegistry
label_desc: Use image node_id 1 for calling a global function with example usage: register_config_transformation(version, prefixes) and returns: wrap, fn