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 a (identifier, weight)-pairs for the model selected by this ensemble.
  
  Parameters
  ----------
  models : dict {identifier : model object}
      The identifiers are the same as the one presented to the fit()
      method. Models can be used for nice printing.
  
  Returns
  -------
  list[tuple[tuple[int, int, float], float]
docstr_tok: ["Return","a","(","identifier",",","weight",")","-pairs","for","the","model","selected","by","this","ensemble",".","Parameters","--","--","--","--","--","models",":","dict","{","identifier",":","model","object","}","The","identifiers","are","the","same","as","the","one","presented","to","the","fit","(",")","method",".","Models","can","be","used","for","nice","printing",".","Returns","--","--","--","-","list","[","tuple","[","tuple","[","int",",","int",",","float","]",",","float","]"]
code: def get_identifiers_with_weights(
      self,
  ) -> list[tuple[tuple[int, int, float], float]]:
      """Return a (identifier, weight)-pairs for the model selected by this ensemble.
  
      Parameters
      ----------
      models : dict {identifier : model object}
          The identifiers are the same as the one presented to the fit()
          method. Models can be used for nice printing.
  
      Returns
      -------
      list[tuple[tuple[int, int, float], float]
      """
      return list(zip(self.identifiers_, self.weights_))
  
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 6 for calling the AbstractSingleModelEnsemble obj's underlying member method code with example usage: obj.get_identifiers_with_weights() and returns: list