docstr: A dummy implementation of a multi-objective ensemble.
  
  Builds ensembles that are individual models on the Pareto front each.
  
  Parameters
  ----------
  task_type: int
      An identifier indicating which task is being performed.
  
  metrics: Sequence[Scorer] | Scorer
      The metrics used to evaluate the models.
  
  backend : Backend
      Gives access to the backend of Auto-sklearn. Not used.
  
  random_state: int | RandomState | None = None
      Not used.
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code: def __init__(
      self,
      task_type: int,
      metrics: Sequence[Scorer] | Scorer,
      backend: Backend,
      random_state: int | np.random.RandomState | None = None,
  ) -> None:
      """A dummy implementation of a multi-objective ensemble.
  
      Builds ensembles that are individual models on the Pareto front each.
  
      Parameters
      ----------
      task_type: int
          An identifier indicating which task is being performed.
  
      metrics: Sequence[Scorer] | Scorer
          The metrics used to evaluate the models.
  
      backend : Backend
          Gives access to the backend of Auto-sklearn. Not used.
  
      random_state: int | RandomState | None = None
          Not used.
      """
      self.task_type = task_type
      if isinstance(metrics, Sequence):
          if len(metrics) == 1:
              warnings.warn(
                  "Passed only a single metric to a multi-objective ensemble. "
                  "Please use a single-objective ensemble in such cases."
              )
          self.metrics = metrics
      else:
          self.metric = [metrics]
      self.random_state = random_state
      self.backend = backend
  
imports: from __future__ import annotations
  from typing import Sequence
  import warnings
  import numpy
  from sklearn.exceptions import NotFittedError
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.constants import TASK_TYPES
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES
  from autosklearn.ensemble_building.run import Run
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble, AbstractMultiObjectiveEnsemble
  from autosklearn.ensembles.singlebest_ensemble import SingleModelEnsemble
  from autosklearn.metrics import Scorer, calculate_losses
  from autosklearn.pipeline.base import BasePipeline
  from autosklearn.util.multiobjective import pareto_front
label_desc: Use image node_id 1 to create a new MultiObjectiveDummyEnsemble object from inherited base classes: AbstractMultiObjectiveEnsemble with example: obj = MultiObjectiveDummyEnsemble(task_type, metrics, backend, random_state)