docstr: Refit all models found with fit to new data.
  
  Necessary when using cross-validation. During training, auto-sklearn
  fits each model k times on the dataset, but does not keep any trained
  model and can therefore not be used to predict for new data points.
  This methods fits all models found during a call to fit on the data
  given. This method may also be used together with holdout to avoid
  only using 66% of the training data to fit the final model.
  
  Parameters
  ----------
  
  X : array-like or sparse matrix of shape = [n_samples, n_features]
      The training input samples.
  
  y : array-like, shape = [n_samples] or [n_samples, n_outputs]
      The targets.
  
  Returns
  -------
  
  self
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code: def refit(self, X, y):
      """Refit all models found with fit to new data.
  
      Necessary when using cross-validation. During training, auto-sklearn
      fits each model k times on the dataset, but does not keep any trained
      model and can therefore not be used to predict for new data points.
      This methods fits all models found during a call to fit on the data
      given. This method may also be used together with holdout to avoid
      only using 66% of the training data to fit the final model.
  
      Parameters
      ----------
  
      X : array-like or sparse matrix of shape = [n_samples, n_features]
          The training input samples.
  
      y : array-like, shape = [n_samples] or [n_samples, n_outputs]
          The targets.
  
      Returns
      -------
  
      self
  
      """
      self.automl_.refit(X, y)
      return self
  
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 8 for calling the AutoSklearnEstimator obj's underlying member method code with example usage: obj.refit(X, y) and returns: self