code: def compute_code(self, data_series: ndarray):
      significant_val = self.array_convert_to_int(data_series)
  
      digits_id_to_item = [{} for _ in range(self.code_len)]
      digits_item_to_id = [{} for _ in range(self.code_len)]
      for i in range(self.code_len):
          id_to_item = digits_id_to_item[i]
          item_to_id = digits_item_to_id[i]
          v = (significant_val // self.base**i) % self.base
          if self.fillall:
              uniq_items = range(0, self.base)
          else:
              uniq_items = sorted(np.unique(v).tolist())
          for k in range(len(uniq_items)):
              item = str(uniq_items[k])
              item_to_id[item] = self.end_id
              id_to_item[self.end_id] = item
              self.end_id += 1
      self.digits_id_to_item = digits_id_to_item
      self.digits_item_to_id = digits_item_to_id
      self.NA_token = "nan"
      if self.hasnan:
          self.end_id += 1  # add the N/A token
          codes = []
          ranges = self.code_range
          for i in ranges:
              codes.append(i[1] - 1)
          self.NA_token_id = codes
  
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 for calling the IntCode obj's underlying member method code with example usage: obj.compute_code(data_series) without return types