code: def add_feature_data(
      feature_config: FeatureConfigDict,
      input_df: DataFrame,
      proc_df: Dict[str, DataFrame],
      metadata: TrainingSetMetadataDict,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
      skip_save_processed_input: bool,
  ) -> None:
      column = input_df[feature_config[COLUMN]]
  
      if column.dtype == object:
          metadata = metadata[feature_config[NAME]]
          if "str2bool" in metadata:
              column = backend.df_engine.map_objects(
                  column, lambda x: metadata["str2bool"][str(x)]
              )
          else:
              # No predefined mapping from string to bool, so compute it directly
              column = backend.df_engine.map_objects(
                  column, strings_utils.str2bool
              )
  
      proc_df[feature_config[PROC_COLUMN]] = column.astype(np.bool_)
  
      return proc_df
  
imports: import logging
  from typing import Dict, List, Tuple, Union
  import numpy
  import torch
  from ludwig.constants import BINARY, COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROBABILITIES, PROBABILITY, PROC_COLUMN
  from ludwig.error import InputDataError
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.schema.features.binary_feature import BinaryInputFeatureConfig, BinaryOutputFeatureConfig
  from ludwig.types import FeatureConfigDict, FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import calibration, output_feature_utils, strings_utils
  from ludwig.utils.eval_utils import average_precision_score, ConfusionMatrix, precision_recall_curve, roc_auc_score, roc_curve
  from ludwig.utils.types import DataFrame, TorchscriptPreprocessingInput
label_desc: Use image node_id 4 for calling the BinaryFeatureMixin 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