docstr: List of (weight, model) pairs for all models included in the 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[float, BasePipeline]]
docstr_tok: ["List","of","(","weight",",","model",")","pairs","for","all","models","included","in","the","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","[","float",",","BasePipeline","]","]"]
code: def get_models_with_weights(
      self, models: Dict[Tuple[int, int, float], BasePipeline]
  ) -> List[Tuple[float, BasePipeline]]:
      """List of (weight, model) pairs for all models included in the 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[float, BasePipeline]]
      """
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Any, Dict, List, Sequence, Tuple, Union
  import numpy
  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.metrics import Scorer
  from autosklearn.pipeline.base import BasePipeline
label_desc: Use image node_id 5 for calling the AbstractEnsemble obj's underlying member method code with example usage: obj.get_models_with_weights(models) without return types