docstr: Return a (identifier, weight)-pairs for all models that were passed to the
  ensemble builder based on the ensemble that is best for the 1st metric.
  
  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]
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code: def get_identifiers_with_weights(
      self,
  ) -> list[tuple[tuple[int, int, float], float]]:
      """Return a (identifier, weight)-pairs for all models that were passed to the
      ensemble builder based on the ensemble that is best for the 1st metric.
  
      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 self.pareto_set[0].get_identifiers_with_weights()
  
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 7 for calling the MultiObjectiveDummyEnsemble obj's underlying member method code with example usage: obj.get_identifiers_with_weights() and returns: self