class_docstr: Builds ensembles out of runs that exist in the Backend
  
  This is used by EnsembleBuilderManager and created in a dask-client
  every time a run finishes and there is currently no EnsembleBuilder active.
docstr: The ensemble targets used for training the ensemble
  
  It will attempt to load and cache them in memory but
  return None if it can't.
  
  Returns
  -------
  np.ndarray | None
      The ensemble targets, if they can be loaded
docstr_tok: ["The","ensemble","targets","used","for","training","the","ensemble","It","will","attempt","to","load","and","cache","them","in","memory","but","return","None","if","it","ca","n't",".","Returns","--","--","--","-","np.ndarray","|","None","The","ensemble","targets",",","if","they","can","be","loaded"]
code: def targets(self, kind: str = "ensemble") -> np.ndarray | None:
      """The ensemble targets used for training the ensemble
  
      It will attempt to load and cache them in memory but
      return None if it can't.
  
      Returns
      -------
      np.ndarray | None
          The ensemble targets, if they can be loaded
      """
      if kind == "ensemble":
          if self._y_ensemble is None:
              if os.path.exists(
                  self.backend._get_targets_ensemble_filename()
              ):
                  self._y_ensemble = (
                      self.backend.load_targets_ensemble()
                  )
          return self._y_ensemble
  
      elif kind == "test":
          return self._y_test
  
      else:
          raise NotImplementedError(kind)
  
imports: from __future__ import annotations
  from typing import Any, Iterable, Mapping, Sequence, Type, cast
  import logging.handlers
  import multiprocessing
  import numbers
  import os
  import pickle
  import time
  import traceback
  from itertools import accumulate
  from pathlib import Path
  import numpy
  import pandas
  import pynisher
  from sklearn.utils.validation import check_random_state
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES
  from autosklearn.data.xy_data_manager import XYDataManager
  from autosklearn.ensemble_building.run import Run, RunID
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
  from autosklearn.ensembles.ensemble_selection import EnsembleSelection
  from autosklearn.metrics import Scorer, calculate_loss, calculate_scores
  from autosklearn.util.disk import rmtree
  from autosklearn.util.functional import cut, findwhere, roundrobin, split
  from autosklearn.util.logging_ import get_named_client_logger
  from autosklearn.util.parallel import preload_modules
label_desc: Use image node_id 5 for calling the EnsembleBuilder obj's underlying member method code with example usage: obj.targets(kind) and returns: self, self