code: def add_feature_data(
      feature_config,
      input_df,
      proc_df,
      metadata,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
      skip_save_processed_input,
  ):
      proc_df[
          feature_config[PROC_COLUMN]
      ] = BagFeatureMixin.feature_data(
          input_df[feature_config[COLUMN]],
          metadata[feature_config[NAME]],
          preprocessing_parameters,
          backend,
      )
      return proc_df
  
imports: import logging
  from collections import Counter
  import numpy
  import torch
  from ludwig.constants import BAG, COLUMN, NAME, PROC_COLUMN
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature
  from ludwig.features.feature_utils import set_str_to_idx
  from ludwig.features.set_feature import _SetPreprocessing
  from ludwig.schema.features.bag_feature import BagInputFeatureConfig
  from ludwig.types import FeatureMetadataDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils.strings_utils import create_vocabulary
label_desc: Use image node_id 5 for calling the BagFeatureMixin obj's underlying member method code with example usage: obj.add_feature_data(feature_config, input_df, proc_df, metadata, preprocessing_parameters, backend, skip_save_processed_input) and returns: proc_df