code: def feature_data(
      column,
      metadata,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
  ):
      def to_vector(set_str):
          bag_vector = np.zeros(
              (len(metadata["str2idx"]),), dtype=np.float32
          )
          col_counter = Counter(
              set_str_to_idx(
                  set_str,
                  metadata["str2idx"],
                  preprocessing_parameters["tokenizer"],
              )
          )
  
          bag_vector[list(col_counter.keys())] = list(
              col_counter.values()
          )
          return bag_vector
  
      return backend.df_engine.map_objects(column, to_vector)
  
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 4 for calling the BagFeatureMixin obj's underlying member method code with example usage: obj.feature_data(column, metadata, preprocessing_parameters, backend) and returns: backend, bag_vector