code: def feature_data(
      column,
      metadata,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
  ):
      def to_dense(x):
          feature_vector = set_str_to_idx(
              x,
              metadata["str2idx"],
              preprocessing_parameters["tokenizer"],
          )
  
          set_vector = np.zeros((len(metadata["str2idx"]),))
          set_vector[feature_vector] = 1
          return set_vector.astype(np.bool_)
  
      return backend.df_engine.map_objects(column, to_dense)
  
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 4 for calling the SetFeatureMixin obj's underlying member method code with example usage: obj.feature_data(column, metadata, preprocessing_parameters, backend) and returns: backend, set_vector