code: def set_validation_parameters(config: "ModelConfig"):
      """Sets validation-related parameters used for early stopping, determining the best hyperopt trial, etc."""
      if not config.output_features:
          return
  
      # First set the validation field so we know what feature we're validating on
      if not config.trainer.validation_field:
          if (
              config.trainer.validation_metric is None
              or config.trainer.validation_metric == LOSS
          ):
              # Loss is valid for all features.
              config.trainer.validation_field = config.output_features[
                  0
              ].name
          else:
              # Determine the proper validation field for the user, like if the user specifies "accuracy" but forgets to
              # change the validation field from "combined" to the name of the feature that produces accuracy metrics.
              from ludwig.utils.metric_utils import (
                  get_feature_to_metric_names_map,
              )
  
              feature_to_metric_names_map = (
                  get_feature_to_metric_names_map(
                      config.output_features.to_list()
                  )
              )
              validation_field = None
              for (
                  feature_name,
                  metric_names,
              ) in feature_to_metric_names_map.items():
                  if config.trainer.validation_metric in metric_names:
                      if validation_field is None:
                          validation_field = feature_name
                      else:
                          raise ValidationError(
                              f"The validation_metric: '{config.trainer.validation_metric}' corresponds to multiple "
                              f"possible validation_fields, '{validation_field}' and '{feature_name}'. Please explicitly "
                              "specify the validation_field that should be used with the validation_metric "
                              f"'{config.trainer.validation_metric}'."
                          )
              if validation_field is None:
                  raise ValidationError(
                      "User-specified trainer.validation_metric is not valid for any output feature."
                  )
  
              config.trainer.validation_field = validation_field
  
      # If the field is combined, then make sure the metric is loss and then return
      if config.trainer.validation_field == COMBINED:
          # Only loss is supported for combined
          if not config.trainer.validation_metric:
              config.trainer.validation_metric = LOSS
          elif config.trainer.validation_metric != LOSS:
              raise ValidationError(
                  f"Must set validation_metric=loss when validation_field=combined, "
                  f"found validation_metric={config.trainer.validation_metric}"
              )
          return
  
      # Field is not combined, so use the default validation metric for the single feature
      validation_features = [
          f
          for f in config.output_features
          if f.name == config.trainer.validation_field
      ]
      if len(validation_features) > 1:
          raise ValidationError(
              f"Found more than one feature matching validation field: {config.trainer.validation_field}"
          )
      if len(validation_features) == 0:
          raise ValidationError(
              f"No output feature found matching validation field: {config.trainer.validation_field}"
          )
  
      validation_feature = validation_features[0]
      if not config.trainer.validation_metric:
          # The user has not explicitly set any validation fields.
          # Default to using the first output feature's default validation metric.
          out_type = validation_feature.type
          config.trainer.validation_metric = output_config_registry(
              config.model_type
          )[out_type].default_validation_metric
  
imports: import copy
  import logging
  import sys
  import warnings
  from typing import Any, Dict, List, Mapping, Set, TYPE_CHECKING
  from marshmallow import ValidationError
  from transformers import AutoConfig
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import CATEGORY, COMBINED, DECODER, DEFAULTS, ENCODER, GRID_SEARCH, INPUT_FEATURES, LOSS, MODEL_ECD, MODEL_LLM, OUTPUT_FEATURES, PARAMETERS, PREPROCESSING, SEQUENCE, SPACE, TEXT, TYPE
  from ludwig.features.feature_utils import compute_feature_hash
  from ludwig.schema.features.utils import output_config_registry
  from ludwig.schema.hyperopt.scheduler import BaseHyperbandSchedulerConfig
  from ludwig.schema.llms.generation import LLMGenerationConfig
  from ludwig.schema.trainer import ECDTrainerConfig
  from ludwig.types import HyperoptConfigDict, ModelConfigDict
  from ludwig.utils.data_utils import get_sanitized_feature_name
  from ludwig.utils.llm_utils import get_context_len
label_desc: Use image node_id 5 for calling a global function with example usage: set_validation_parameters(config) without return types