class_docstr: Ensemble consisting of a single model.
  
  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.
class_docstr_tok: ["Ensemble","consisting","of","a","single","model",".","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","."]
docstr: Return validation performance of ensemble.
  
  In case of multi-objective problem, only the first metric will be returned.
  
  Return
  ------
  float
docstr_tok: ["Return","validation","performance","of","ensemble",".","In","case","of","multi-objective","problem",",","only","the","first","metric","will","be","returned",".","Return","--","--","--","float"]
code: def get_validation_performance(self) -> float:
      """Return validation performance of ensemble.
  
      In case of multi-objective problem, only the first metric will be returned.
  
      Return
      ------
      float
      """
      return self.best_model_score_
  
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 8 for calling the AbstractSingleModelEnsemble obj's underlying member method code with example usage: obj.get_validation_performance() and returns: self