code: def add_feature_data(
      feature_config,
      input_df,
      proc_df,
      metadata,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
      skip_save_processed_input,
  ):
      column = input_df[feature_config[COLUMN]]
      if column.dtype == object:
          column = backend.df_engine.map_objects(column, int)
      column = backend.df_engine.map_objects(
          column, H3FeatureMixin.h3_to_list
      )
  
      proc_df[
          feature_config[PROC_COLUMN]
      ] = backend.df_engine.map_objects(
          column, lambda x: np.array(x, dtype=np.uint8)
      )
      return proc_df
  
imports: import logging
  from typing import List
  import numpy
  import torch
  from ludwig.constants import COLUMN, H3, PROC_COLUMN
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature
  from ludwig.schema.features.h3_feature import H3InputFeatureConfig
  from ludwig.types import FeatureMetadataDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils.h3_util import h3_to_components
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 5 for calling the H3FeatureMixin 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