class_docstr: Tokens from the labour data.
docstr: Loads a single indexed year, applies the tokenization logic, then saves
  the result.
docstr_tok: ["Loads","a","single","indexed","year",",","applies","the","tokenization","logic",",","then","saves","the","result","."]
code: def tokenized_part(self, year: int) -> dd.DataFrame:
      """Loads a single indexed year, applies the tokenization logic, then saves
      the result."""
  
      ddf = self.indexed(year)
  
      result = (
          ddf.assign(
              # WORK_MUNICIPALITY=lambda x: "WMUN_" + x.WORK_MUNICIPALITY,
              WORK_INDUSTRY=lambda x: "IND_" + x.WORK_INDUSTRY,
              WORK_SECTOR=lambda x: "ENT_" + x.WORK_SECTOR,
              ATP_RATE_CODE=lambda x: "ATP_" + x.ATP_RATE_CODE,
              RES_MUNICIPALITY=lambda x: "RMUN_" + x.RES_MUNICIPALITY,
              WORK_POSITION=lambda x: "POS_" + x.WORK_POSITION,
              SOC_STATUS=lambda x: "SOC_" + x.SOC_STATUS,
              SOC_MODIFIER=lambda x: "SOM_" + x.SOC_MODIFIER,
          )
          # Remove INCOME_0
          .assign(
              WORK_INCOME=lambda x: x.WORK_INCOME.where(
                  lambda x: x > 0, pd.NA
              )
          )[["START_DATE", "AGE", *self.field_labels()]].astype(
              self.token_dtypes()
          )
      )
  
      assert isinstance(result, dd.DataFrame)
      return result
  
imports: from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Dict, List
  import dask.dataframe
  import pandas
  from ..ops import concat_sorted, sort_partitions
  from ..decorators import save_parquet
  from ..serialize import DATA_ROOT
  from .base import FIELD_TYPE, Binned, TokenSource
label_desc: Use image node_id 2 for calling the LabourTokens obj's underlying member method code with example usage: obj.tokenized_part(year) and returns: result