code: def target(self) -> pd.DataFrame:
      data = dd.read_csv(
          self.target_path,
          blocksize="64MB",
          encoding="latin",
          sep=";",
          usecols=[
              "PERSON_ID",
              "EVENT_CAUSE_FINAL",
              "EVENT_FINAL_DATE",
              "QUALITY_INFORMATION_FINAL",
              "KILDE_FINAL",
              "NUMBER_EVENTS_PERSON",
          ],
          parse_dates=["EVENT_FINAL_DATE"],
          date_parser=dateparser,
      )
  
      population_ids = self.sub_population().index.to_numpy()
  
      result = (
          data.loc[
              lambda x: (x.PERSON_ID.isin(population_ids))
              & (x.EVENT_FINAL_DATE >= self._period_start)
              & (x.EVENT_FINAL_DATE <= self._period_end)
              & (x.EVENT_CAUSE_FINAL.isin(["Udvandret"]))
          ]
          .groupby("PERSON_ID")
          .agg({"EVENT_FINAL_DATE": "max"})
      )
      result = result.compute()
      result["TARGET"] = 1
      positive_ids = result.index.to_numpy()
      negative_ids = np.array(
          list(filter(lambda x: x not in positive_ids, population_ids))
      )
  
      assert (
          population_ids.shape[0]
          == positive_ids.shape[0] + negative_ids.shape[0]
      )
  
      temp_result_1 = (
          data.loc[
              lambda x: (x.PERSON_ID.isin(negative_ids))
              & (x.EVENT_FINAL_DATE >= self._period_start)
              & (x.EVENT_FINAL_DATE <= self._period_end)
              & (x.EVENT_CAUSE_FINAL.isin(["Udvandret", "Forsvundet"]))
          ]
          .groupby("PERSON_ID")
          .agg({"EVENT_FINAL_DATE": "max"})
          .compute()
      )
  
      temp_result_1["TARGET"] = 0
      ##### UNLABALED
      self.unlabeled_ids = temp_result_1.index.to_numpy().tolist()
  
      print(
          "Calculated TEMP1",
          temp_result_1.shape[0],
          negative_ids.shape[0] - temp_result_1.shape[0],
      )
  
      negative_ids = np.array(
          list(
              filter(
                  lambda x: x not in temp_result_1.index.to_numpy(),
                  negative_ids,
              )
          )
      )
      temp_result_2 = pd.DataFrame(
          {
              "PERSON_ID": negative_ids,
              "EVENT_FINAL_DATE": np.full_like(
                  negative_ids,
                  fill_value=dateparser(self._period_end),
                  dtype=result.dtypes["EVENT_FINAL_DATE"],
              ),
              "TARGET": np.full_like(
                  negative_ids,
                  fill_value=0,
                  dtype=result.dtypes["TARGET"],
              ),
          }
      ).set_index("PERSON_ID")
  
      print("Calculated TEMP2", temp_result_2.shape[0])
      result = pd.concat(
          [result, temp_result_1, temp_result_2]
      ).sort_index()
  
      cat_type = pd.api.types.CategoricalDtype(
          categories=[0, 1], ordered=False
      )
      result["TARGET"] = result["TARGET"].astype(cat_type)
  
      assert result.shape[0] == population_ids.shape[0]
      assert isinstance(result, pd.DataFrame)
      return result
  
file_path: life2vec/src/data_new/populations/emm.py
imports: from asyncio.log import logger
  from dataclasses import dataclass
  from multiprocessing import resource_tracker
  from pathlib import Path
  from functools import reduce
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  from ..decorators import save_pickle
  from ..serialize import DATA_ROOT
  from .base import DataSplit, Population
label_desc: Use image node_id 4 for calling the EmmSubPopulation obj's underlying member method code with example usage: obj.target() and returns: result