code: def add_feature_data(
      feature_config: FeatureConfigDict,
      input_df: DataFrame,
      proc_df: Dict[str, DataFrame],
      metadata: TrainingSetMetadataDict,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,  # Union[Backend, str]
      skip_save_processed_input: bool,
  ) -> None:
      datetime_format = preprocessing_parameters["datetime_format"]
      proc_df[
          feature_config[PROC_COLUMN]
      ] = backend.df_engine.map_objects(
          input_df[feature_config[COLUMN]],
          lambda x: np.array(
              DateFeatureMixin.date_to_list(
                  x, datetime_format, preprocessing_parameters
              ),
              dtype=np.int16,
          ),
      )
      return proc_df
  
imports: import logging
  from datetime import date, datetime
  from typing import Dict, List
  import numpy
  import torch
  from ludwig.constants import COLUMN, DATE, PROC_COLUMN
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature
  from ludwig.schema.features.date_feature import DateInputFeatureConfig
  from ludwig.types import FeatureConfigDict, FeatureMetadataDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils.date_utils import create_vector_from_datetime_obj, parse_datetime
  from ludwig.utils.types import DataFrame, TorchscriptPreprocessingInput
label_desc: Use image node_id 5 for calling the DateFeatureMixin 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