class_docstr: Ensemble consisting of a single model.
  
  This class is used by the :class:`MultiObjectiveDummyEnsemble` to represent
  ensembles consisting of a single model, and this class should not be used
  on its own.
  
  Do not use by yourself!
  
  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.
  
  model_index : int
      Index of the model that constitutes the ensemble. This index will
      be used to select the correct predictions that will be passed during
      ``fit`` and ``predict``.
  
  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,
      model_index: int,
      random_state: int | np.random.RandomState | None = None,
  ):
      super().__init__(
          task_type=task_type,
          metrics=metrics,
          random_state=random_state,
          backend=backend,
      )
      self.indices_ = [model_index]
  
imports: from __future__ import annotations
  from typing import Sequence
  import os
  import numpy
  from smac.runhistory.runhistory import RunHistory
  from autosklearn.automl_common.common.utils.backend import Backend
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES
  from autosklearn.ensemble_building.run import Run
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
  from autosklearn.metrics import Scorer, calculate_losses
  from autosklearn.pipeline.base import BasePipeline
label_desc: Use image node_id 1 to create a new SingleModelEnsemble object from inherited base classes: AbstractSingleModelEnsemble with example: obj = SingleModelEnsemble(task_type, metrics, backend, model_index, random_state)