code: def learning_rate_scheduler(
      trainer: TrainerConfigDict,
  ) -> TrainerConfigDict:
      key_mapping = {
          "reduce_learning_rate_on_plateau": "reduce_on_plateau",
          "reduce_learning_rate_on_plateau_patience": "reduce_on_plateau_patience",
          "reduce_learning_rate_on_plateau_rate": "reduce_on_plateau_rate",
          "reduce_learning_rate_eval_metric": "reduce_eval_metric",
          "reduce_learning_rate_eval_split": "reduce_eval_split",
          "decay": "decay",
          "decay_steps": "decay_steps",
          "decay_rate": "decay_rate",
          "staircase": "staircase",
          "learning_rate_warmup_epochs": "warmup_evaluations",
      }
  
      lr_scheduler = trainer.get("learning_rate_scheduler", {})
      for old_key, new_key in key_mapping.items():
          if old_key in trainer:
              warnings.warn(
                  f"Config param `trainer.{old_key}` has been moved to `trainer.learning_rate_scheduler.{new_key}`.",
                  DeprecationWarning,
              )
              if new_key in lr_scheduler:
                  warnings.warn(
                      f"`trainer.learning_rate_scheduler.{new_key}` config param already set. "
                      f"Discarding `trainer.{old_key}`."
                  )
              else:
                  value = trainer[old_key]
                  if old_key == "decay" and isinstance(value, bool):
                      # Decay has changed from a bool to an optional enum
                      lr_scheduler[new_key] = (
                          "exponential" if value else None
                      )
                  elif old_key == "reduce_learning_rate_on_plateau":
                      lr_scheduler[new_key] = int(value)
                  else:
                      lr_scheduler[new_key] = value
              del trainer[old_key]
  
      if lr_scheduler:
          trainer["learning_rate_scheduler"] = lr_scheduler
  
      return trainer
  
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 23 for calling a global function with example usage: learning_rate_scheduler(trainer) and returns: trainer