code: def update_config_with_metadata(
      feature_config, feature_metadata, *args, **kwargs
  ):
      feature_config.decoder.num_classes = feature_metadata[
          "vocab_size"
      ]
      if isinstance(feature_config.loss.class_weights, (list, tuple)):
          if (
              len(feature_config.loss.class_weights)
              != feature_config.decoder.num_classes
          ):
              raise ValueError(
                  f"The length of class_weights ({len(feature_config.loss.class_weights)}) is not compatible with "
                  f"the number of classes ({feature_config.decoder.num_classes}) for feature {feature_config.name}. "
                  "Check the metadata JSON file to see the classes "
                  "and their order and consider there needs to be a weight "
                  "for the <UNK> and <PAD> class too."
              )
  
      if isinstance(feature_config.loss.class_weights, dict):
          if (
              feature_metadata["str2idx"].keys()
              != feature_config.loss.class_weights.keys()
          ):
              raise ValueError(
                  f"The class_weights keys ({feature_config.loss.class_weights.keys()}) are not compatible with "
                  f'the classes ({feature_metadata["str2idx"].keys()}) of feature {feature_config.name}. '
                  "Check the metadata JSON file to see the classes "
                  "and consider there needs to be a weight "
                  "for the <UNK> and <PAD> class too."
              )
          else:
              class_weights = feature_config.loss.class_weights
              idx2str = feature_metadata["idx2str"]
              class_weights_list = [class_weights[s] for s in idx2str]
              feature_config.loss.class_weights = class_weights_list
  
imports: import logging
  from typing import Any, Dict, List, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROBABILITIES, PROC_COLUMN, SET
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.features.feature_utils import set_str_to_idx
  from ludwig.schema.features.set_feature import SetInputFeatureConfig, SetOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import output_feature_utils
  from ludwig.utils.strings_utils import create_vocabulary, UNKNOWN_SYMBOL
  from ludwig.utils.tokenizers import get_tokenizer_from_registry, TORCHSCRIPT_COMPATIBLE_TOKENIZERS
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 9 for calling the SetOutputFeature obj's underlying member method code with example usage: obj.update_config_with_metadata(feature_config, feature_metadata) without return types