docstr: @params:
      col_name: name of the column
      code_len: number of tokens used to code the column.
      start_id: offset for token_id. 
      fillall: if True, reserve space for digit number even the digit number is
      not present in the data_series. Otherwise, only reserve space for the numbers
      in the data_series. 
      hasnan: if True, reserve space for nan
docstr_tok: ["@","params",":","col_name",":","name","of","the","column","code_len",":","number","of","tokens","used","to","code","the","column",".","start_id",":","offset","for","token_id",".","fillall",":","if","True",",","reserve","space","for","digit","number","even","the","digit","number","is","not","present","in","the","data_series",".","Otherwise",",","only","reserve","space","for","the","numbers","in","the","data_series",".","hasnan",":","if","True",",","reserve","space","for","nan"]
code: def __init__(
      self,
      col_name: str,
      code_len: int,
      start_id: int,
      fillall: bool = True,
      hasnan: bool = True,
  ):
      """
      @params:
          col_name: name of the column
          code_len: number of tokens used to code the column.
          start_id: offset for token_id.
          fillall: if True, reserve space for digit number even the digit number is
          not present in the data_series. Otherwise, only reserve space for the numbers
          in the data_series.
          hasnan: if True, reserve space for nan
      """
      self.name = col_name
      self.code_len = code_len
      self.start_id = start_id
      self.end_id = start_id
      self.fillall = fillall
      self.hasnan = hasnan
  
imports: import math
  from typing import Dict, List, Tuple
  import numpy
  from numpy import ndarray
  from sklearn.preprocessing import PowerTransformer, QuantileTransformer, RobustScaler
  from nemo.utils import logging
label_desc: Use image node_id 2 to create a new Code object from inherited base classes: object with example: obj = Code(col_name, code_len, start_id, fillall, hasnan)