docstr: Convert the StringIndex to a dict, with the categorical features as keys and indices
  as values.
  Note that you may only call this if the StringIndex is small.
  
  :return: A dict for the mapping from string to index.
docstr_tok: ["Convert","the","StringIndex","to","a","dict",",","with","the","categorical","features","as","keys","and","indices","as","values",".","Note","that","you","may","only","call","this","if","the","StringIndex","is","small",".",":","return",":","A","dict","for","the","mapping","from","string","to","index","."]
code: def to_dict(self) -> Dict[str, int]:
      """
      Convert the StringIndex to a dict, with the categorical features as keys and indices
      as values.
      Note that you may only call this if the StringIndex is small.
  
      :return: A dict for the mapping from string to index.
      """
      cols = self.df.columns
      index_id = cols.index("id")
      col_id = cols.index(self.col_name)
      rows = self.df.collect()
      res_dict = {}
      for row in rows:
          res_dict[row[col_id]] = row[index_id]
      return res_dict
  
imports: import pyspark.sql.functions
  from pyspark.sql import Row
  from pyspark.sql.types import IntegerType, StructType, StringType, StructField
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca.data import SparkXShards
  from bigdl.orca import OrcaContext
  from pyspark.ml.feature import MinMaxScaler
  from pyspark.ml.feature import StandardScaler
  from pyspark.ml.feature import VectorAssembler
  from pyspark.ml import Pipeline
  from bigdl.orca.data.utils import *
  import uuid
  from typing import TYPE_CHECKING, Tuple, Union, List
label_desc: Use image node_id 4 for calling the StringIndex obj's underlying member method code with example usage: obj.to_dict() and returns: res_dict