class_docstr: This class implements the classification task.
docstr: Fit *auto-sklearn* to given training set (X, y).
  
  Fit both optimizes the machine learning models and builds an ensemble
  out of them.
  
  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 target classes.
  
  X_test : array-like or sparse matrix of shape = [n_samples, n_features]
      Test data input samples. Will be used to save test predictions for
      all models. This allows to evaluate the performance of Auto-sklearn
      over time.
  
  y_test : array-like, shape = [n_samples] or [n_samples, n_outputs]
      Test data target classes. Will be used to calculate the test error
      of all models. This allows to evaluate the performance of
      Auto-sklearn over time.
  
  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>`_.
  
  dataset_name : str, optional (default=None)
      Create nicer output. If None, a string will be determined by the
      md5 hash of the dataset.
  
  Returns
  -------
  self
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code: def fit(
      self,
      X,
      y,
      X_test=None,
      y_test=None,
      feat_type=None,
      dataset_name=None,
  ):
      """Fit *auto-sklearn* to given training set (X, y).
  
      Fit both optimizes the machine learning models and builds an ensemble
      out of them.
  
      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 target classes.
  
      X_test : array-like or sparse matrix of shape = [n_samples, n_features]
          Test data input samples. Will be used to save test predictions for
          all models. This allows to evaluate the performance of Auto-sklearn
          over time.
  
      y_test : array-like, shape = [n_samples] or [n_samples, n_outputs]
          Test data target classes. Will be used to calculate the test error
          of all models. This allows to evaluate the performance of
          Auto-sklearn over time.
  
      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>`_.
  
      dataset_name : str, optional (default=None)
          Create nicer output. If None, a string will be determined by the
          md5 hash of the dataset.
  
      Returns
      -------
      self
      """
      # AutoSklearn does not handle sparse y for now
      y = convert_if_sparse(y)
  
      # Before running anything else, first check that the
      # type of data is compatible with auto-sklearn. Legal target
      # types are: binary, multiclass, multilabel-indicator.
      target_type = type_of_target(y)
      supported_types = ["binary", "multiclass", "multilabel-indicator"]
      if target_type not in supported_types:
          raise ValueError(
              "Classification with data of type {} is "
              "not supported. Supported types are {}. "
              "You can find more information about scikit-learn "
              "data types in: "
              "https://scikit-learn.org/stable/modules/multiclass.html"
              "".format(target_type, supported_types)
          )
  
      # remember target type for using in predict_proba later.
      self.target_type = target_type
  
      super().fit(
          X=X,
          y=y,
          X_test=X_test,
          y_test=y_test,
          feat_type=feat_type,
          dataset_name=dataset_name,
      )
  
      # After fit, a classifier is expected to define classes_
      # A list of class labels known to the classifier, mapping each label
      # to a numerical index used in the model representation our output.
      self.classes_ = (
          self.automl_.InputValidator.target_validator.classes_
      )
  
      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 1 for calling the AutoSklearnClassifier obj's underlying member method code with example usage: obj.fit(X, y, X_test, y_test, feat_type, dataset_name) and returns: self