docstr: Fits and individual pipeline configuration and returns
  the result to the user.
  
  The Estimator constraints are honored, for example the resampling
  strategy, or memory constraints, unless directly provided to the method.
  By default, this method supports the same signature as fit(), and any extra
  arguments are redirected to the TAE evaluation function, which allows for
  further customization while building a pipeline.
  
  Any additional argument provided is directly passed to the
  worker exercising the run.
  
  Parameters
  ----------
  X: array-like, shape = (n_samples, n_features)
      The features used for training
  y: array-like
      The labels used for training
  X_test: Optionalarray-like, shape = (n_samples, n_features)
      If provided, the testing performance will be tracked on this features.
  y_test: array-like
      If provided, the testing performance will be tracked on this labels
  config: Union[Configuration,  Dict[str, Union[str, float, int]]]
      A configuration object used to define the pipeline steps.
      If a dict is passed, a configuration is created based on this dict.
  dataset_name: Optional[str]
      Name that will be used to tag the Auto-Sklearn run and identify the
      Auto-Sklearn run
  feat_type : list, optional (default=None)
      List of str of `len(X.shape[1])` describing the attribute type.
      Possible types are `Categorical` and `Numerical`. `Categorical`
      attributes will be automatically One-Hot encoded. The values
      used for a categorical attribute must be integers, obtained for
      example by `sklearn.preprocessing.LabelEncoder
      <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
  
  Returns
  -------
  pipeline: Optional[BasePipeline]
      The fitted pipeline. In case of failure while fitting the pipeline,
      a None is returned.
  run_info: RunInFo
      A named tuple that contains the configuration launched
  run_value: RunValue
      A named tuple that contains the result of the run
docstr_tok: ["Fits","and","individual","pipeline","configuration","and","returns","the","result","to","the","user",".","The","Estimator","constraints","are","honored",",","for","example","the","resampling","strategy",",","or","memory","constraints",",","unless","directly","provided","to","the","method",".","By","default",",","this","method","supports","the","same","signature","as","fit","(",")",",","and","any","extra","arguments","are","redirected","to","the","TAE","evaluation","function",",","which","allows","for","further","customization","while","building","a","pipeline",".","Any","additional","argument","provided","is","directly","passed","to","the","worker","exercising","the","run",".","Parameters","--","--","--","--","--","X",":","array-like",",","shape","=","(","n_samples",",","n_features",")","The","features","used","for","training","y",":","array-like","The","labels","used","for","training","X_test",":","Optionalarray-like",",","shape","=","(","n_samples",",","n_features",")","If","provided",",","the","testing","performance","will","be","tracked","on","this","features",".","y_test",":","array-like","If","provided",",","the","testing","performance","will","be","tracked","on","this","labels","config",":","Union","[","Configuration",",","Dict","[","str",",","Union","[","str",",","float",",","int","]","]","]","A","configuration","object","used","to","define","the","pipeline","steps",".","If","a","dict","is","passed",",","a","configuration","is","created","based","on","this","dict",".","dataset_name",":","Optional","[","str","]","Name","that","will","be","used","to","tag","the","Auto-Sklearn","run","and","identify","the","Auto-Sklearn","run","feat_type",":","list",",","optional","(","default=None",")","List","of","str","of","`","len","(","X.shape","[","1","]",")","`","describing","the","attribute","type",".","Possible","types","are","`","Categorical","`","and","`","Numerical","`",".","`","Categorical","`","attributes","will","be","automatically","One-Hot","encoded",".","The","values","used","for","a","categorical","attribute","must","be","integers",",","obtained","for","example","by","`","sklearn.preprocessing.LabelEncoder","<","https",":","\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.LabelEncoder.html",">","`","_",".","Returns","--","--","--","-","pipeline",":","Optional","[","BasePipeline","]","The","fitted","pipeline",".","In","case","of","failure","while","fitting","the","pipeline",",","a","None","is","returned",".","run_info",":","RunInFo","A","named","tuple","that","contains","the","configuration","launched","run_value",":","RunValue","A","named","tuple","that","contains","the","result","of","the","run"]
code: def fit_pipeline(
      self,
      X: SUPPORTED_FEAT_TYPES,
      y: Union[SUPPORTED_TARGET_TYPES, spmatrix],
      config: Union[Configuration, Dict[str, Union[str, float, int]]],
      dataset_name: Optional[str] = None,
      X_test: Optional[SUPPORTED_FEAT_TYPES] = None,
      y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None,
      feat_type: Optional[List[str]] = None,
      *args,
      **kwargs: Dict,
  ) -> Tuple[Optional[BasePipeline], RunInfo, RunValue]:
      """Fits and individual pipeline configuration and returns
      the result to the user.
  
      The Estimator constraints are honored, for example the resampling
      strategy, or memory constraints, unless directly provided to the method.
      By default, this method supports the same signature as fit(), and any extra
      arguments are redirected to the TAE evaluation function, which allows for
      further customization while building a pipeline.
  
      Any additional argument provided is directly passed to the
      worker exercising the run.
  
      Parameters
      ----------
      X: array-like, shape = (n_samples, n_features)
          The features used for training
      y: array-like
          The labels used for training
      X_test: Optionalarray-like, shape = (n_samples, n_features)
          If provided, the testing performance will be tracked on this features.
      y_test: array-like
          If provided, the testing performance will be tracked on this labels
      config: Union[Configuration,  Dict[str, Union[str, float, int]]]
          A configuration object used to define the pipeline steps.
          If a dict is passed, a configuration is created based on this dict.
      dataset_name: Optional[str]
          Name that will be used to tag the Auto-Sklearn run and identify the
          Auto-Sklearn run
      feat_type : list, optional (default=None)
          List of str of `len(X.shape[1])` describing the attribute type.
          Possible types are `Categorical` and `Numerical`. `Categorical`
          attributes will be automatically One-Hot encoded. The values
          used for a categorical attribute must be integers, obtained for
          example by `sklearn.preprocessing.LabelEncoder
          <https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html>`_.
  
      Returns
      -------
      pipeline: Optional[BasePipeline]
          The fitted pipeline. In case of failure while fitting the pipeline,
          a None is returned.
      run_info: RunInFo
          A named tuple that contains the configuration launched
      run_value: RunValue
          A named tuple that contains the result of the run
      """
      if self.automl_ is None:
          self.automl_ = self.build_automl()
      return self.automl_.fit_pipeline(
          X=X,
          y=y,
          dataset_name=dataset_name,
          config=config,
          feat_type=feat_type,
          X_test=X_test,
          y_test=y_test,
          *args,
          **kwargs,
      )
  
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 5 for calling the AutoSklearnEstimator obj's underlying member method code with example usage: obj.fit_pipeline(X, y, config, dataset_name, X_test, y_test, feat_type) and returns: self