docstr: Returns a dictionary containing dictionaries of ensemble models.
  
  Each model in the ensemble can be accessed by giving its ``model_id`` as key.
  
  A model dictionary contains the following:
  
  * ``"model_id"`` - The id given to a model by ``autosklearn``.
  
  * ``"rank"`` - The rank of the model based on it's ``"cost"``.
  
  * ``"cost"`` - The loss of the model on the validation set.
  
  * ``"ensemble_weight"`` - The weight given to the model in the ensemble.
  
  * ``"voting_model"`` - The ``cv_voting_ensemble`` model (for 'cv' resampling).
  
  * ``"estimators"`` - List of models (dicts) in ``cv_voting_ensemble``
      ('cv' resampling).
  
  * ``"data_preprocessor"`` - The preprocessor used on the data.
  
  * ``"balancing"`` - The balancing used on the data (for classification).
  
  * ``"feature_preprocessor"`` - The preprocessor for features types.
  
  * ``"classifier"`` / ``"regressor"``
    - The autosklearn wrapped classifier or regressor.
  
  * ``"sklearn_classifier"`` or ``"sklearn_regressor"``
    - The sklearn classifier or regressor.
  
  **Example**
  
  .. code-block:: python
  
      import sklearn.datasets
      import sklearn.metrics
      import autosklearn.regression
  
      X, y = sklearn.datasets.load_diabetes(return_X_y=True)
  
      automl = autosklearn.regression.AutoSklearnRegressor(
          time_left_for_this_task=120
          )
      automl.fit(X_train, y_train, dataset_name='diabetes')
  
      ensemble_dict = automl.show_models()
      print(ensemble_dict)
  
  Output:
  
  .. code-block:: text
  
      {
          25: {'model_id': 25.0,
               'rank': 1,
               'cost': 0.43667876507897496,
               'ensemble_weight': 0.38,
               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing....>,
               'feature_preprocessor': <autosklearn.pipeline.components....>,
               'regressor': <autosklearn.pipeline.components.regression....>,
               'sklearn_regressor': SGDRegressor(alpha=0.0006517033225329654,...)
              },
          6: {'model_id': 6.0,
              'rank': 2,
              'cost': 0.4550418898836528,
              'ensemble_weight': 0.3,
              'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing....>,
              'feature_preprocessor': <autosklearn.pipeline.components....>,
              'regressor': <autosklearn.pipeline.components.regression....>,
              'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788,...)
              }...
      }
  
  Returns
  -------
  Dict(int, Any) : dictionary of length = number of models in the ensemble
      A dictionary of models in the ensemble, where ``model_id`` is the key.
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code: def show_models(self):
      """Returns a dictionary containing dictionaries of ensemble models.
  
      Each model in the ensemble can be accessed by giving its ``model_id`` as key.
  
      A model dictionary contains the following:
  
      * ``"model_id"`` - The id given to a model by ``autosklearn``.
  
      * ``"rank"`` - The rank of the model based on it's ``"cost"``.
  
      * ``"cost"`` - The loss of the model on the validation set.
  
      * ``"ensemble_weight"`` - The weight given to the model in the ensemble.
  
      * ``"voting_model"`` - The ``cv_voting_ensemble`` model (for 'cv' resampling).
  
      * ``"estimators"`` - List of models (dicts) in ``cv_voting_ensemble``
          ('cv' resampling).
  
      * ``"data_preprocessor"`` - The preprocessor used on the data.
  
      * ``"balancing"`` - The balancing used on the data (for classification).
  
      * ``"feature_preprocessor"`` - The preprocessor for features types.
  
      * ``"classifier"`` / ``"regressor"``
        - The autosklearn wrapped classifier or regressor.
  
      * ``"sklearn_classifier"`` or ``"sklearn_regressor"``
        - The sklearn classifier or regressor.
  
      **Example**
  
      .. code-block:: python
  
          import sklearn.datasets
          import sklearn.metrics
          import autosklearn.regression
  
          X, y = sklearn.datasets.load_diabetes(return_X_y=True)
  
          automl = autosklearn.regression.AutoSklearnRegressor(
              time_left_for_this_task=120
              )
          automl.fit(X_train, y_train, dataset_name='diabetes')
  
          ensemble_dict = automl.show_models()
          print(ensemble_dict)
  
      Output:
  
      .. code-block:: text
  
          {
              25: {'model_id': 25.0,
                   'rank': 1,
                   'cost': 0.43667876507897496,
                   'ensemble_weight': 0.38,
                   'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing....>,
                   'feature_preprocessor': <autosklearn.pipeline.components....>,
                   'regressor': <autosklearn.pipeline.components.regression....>,
                   'sklearn_regressor': SGDRegressor(alpha=0.0006517033225329654,...)
                  },
              6: {'model_id': 6.0,
                  'rank': 2,
                  'cost': 0.4550418898836528,
                  'ensemble_weight': 0.3,
                  'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing....>,
                  'feature_preprocessor': <autosklearn.pipeline.components....>,
                  'regressor': <autosklearn.pipeline.components.regression....>,
                  'sklearn_regressor': ARDRegression(alpha_1=0.0003701926442639788,...)
                  }...
          }
  
      Returns
      -------
      Dict(int, Any) : dictionary of length = number of models in the ensemble
          A dictionary of models in the ensemble, where ``model_id`` is the key.
  
      """  # noqa: E501
  
      return self.automl_.show_models()
  
imports: from __future__ import annotations
  from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple, Type, Union
  import warnings
  import dask.distributed
  import joblib
  import numpy
  import pandas
  from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
  from scipy.sparse import spmatrix
  from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
  from sklearn.ensemble import VotingClassifier, VotingRegressor
  from sklearn.utils.multiclass import type_of_target
  from smac.runhistory.runhistory import RunInfo, RunValue
  from typing_extensions import Literal
  from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES, convert_if_sparse
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
  from autosklearn.ensembles.ensemble_selection import EnsembleSelection
  from autosklearn.ensembles.multiobjective_dummy_ensemble import MultiObjectiveDummyEnsemble
  from autosklearn.metrics import Scorer
  from autosklearn.pipeline.base import BasePipeline
  from autosklearn.util.smac_wrap import SMACCallback
label_desc: Use image node_id 12 for calling the AutoSklearnEstimator obj's underlying member method code with example usage: obj.show_models() and returns: self