code: def make_sparse_preprocessor(
      feature_config: rlt.ModelFeatureConfig,
      device: torch.device,
      gen_torch_script: bool = True,
  ):
      """Helper to initialize, for scripting SparsePreprocessor"""
      # TODO: Add option for simple modulo and other hash functions
      id2name: Dict[int, str] = feature_config.id2name
      name2id: Dict[str, int] = feature_config.name2id
  
      def _make_id_list_mapper(
          config: rlt.IdListFeatureConfig,
      ) -> MapIDList:
          mapping_config = feature_config.id_mapping_config[
              config.id_mapping_name
          ]
          if mapping_config.hashing:
              return HashingMapIDList(
                  mapping_config.embedding_table_size
              )
          else:
              return ExactMapIDList()
  
      id_list_mappers = {
          config.feature_id: _make_id_list_mapper(config)
          for config in feature_config.id_list_feature_configs
      }
  
      def _make_id_score_list_mapper(
          config: rlt.IdScoreListFeatureConfig,
      ) -> MapIDScoreList:
          mapping_config = feature_config.id_mapping_config[
              config.id_mapping_name
          ]
          if mapping_config.hashing:
              return HashingMapIDScoreList(
                  mapping_config.embedding_table_size
              )
          else:
              return ExactMapIDScoreList()
  
      id_score_list_mappers = {
          config.feature_id: _make_id_score_list_mapper(config)
          for config in feature_config.id_score_list_feature_configs
      }
      sparse_preprocessor = SparsePreprocessor(
          id2name,
          name2id,
          id_list_mappers,
          id_score_list_mappers,
          device,
          gen_torch_script,
      )
      if gen_torch_script:
          return torch.jit.script(sparse_preprocessor)
      else:
          return sparse_preprocessor
  
imports: import abc
  import logging
  from typing import cast, Dict, Tuple
  import reagent.core.types
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: make_sparse_preprocessor(feature_config, device, gen_torch_script) and returns: torch, sparse_preprocessor, HashingMapIDList, ExactMapIDList, HashingMapIDScoreList, ExactMapIDScoreList