class_docstr: This class implements the classification task.
docstr: Predict probabilities of classes for all samples X.
  
  Parameters
  ----------
  X : array-like or sparse matrix of shape = [n_samples, n_features]
  
  batch_size : int (optional)
      Number of data points to predict for (predicts all points at once
      if ``None``.
  n_jobs : int
  
  Returns
  -------
  y : array of shape = [n_samples, n_classes] or [n_samples, n_labels]
      The predicted class probabilities.
docstr_tok: ["Predict","probabilities","of","classes","for","all","samples","X.","Parameters","--","--","--","--","--","X",":","array-like","or","sparse","matrix","of","shape","=","[","n_samples",",","n_features","]","batch_size",":","int","(","optional",")","Number","of","data","points","to","predict","for","(","predicts","all","points","at","once","if","``","None","``",".","n_jobs",":","int","Returns","--","--","--","-","y",":","array","of","shape","=","[","n_samples",",","n_classes","]","or","[","n_samples",",","n_labels","]","The","predicted","class","probabilities","."]
code: def predict_proba(self, X, batch_size=None, n_jobs=1):
      """Predict probabilities of classes for all samples X.
  
      Parameters
      ----------
      X : array-like or sparse matrix of shape = [n_samples, n_features]
  
      batch_size : int (optional)
          Number of data points to predict for (predicts all points at once
          if ``None``.
      n_jobs : int
  
      Returns
      -------
      y : array of shape = [n_samples, n_classes] or [n_samples, n_labels]
          The predicted class probabilities.
      """
      pred_proba = super().predict_proba(
          X, batch_size=batch_size, n_jobs=n_jobs
      )
  
      # Check if all probabilities sum up to 1.
      # Assert only if target type is not multilabel-indicator.
      if self.target_type not in ["multilabel-indicator"]:
          assert np.allclose(
              np.sum(pred_proba, axis=1), np.ones_like(pred_proba[:, 0])
          ), "prediction probability does not sum up to 1!"
  
      # Check that all probability values lie between 0 and 1.
      assert (pred_proba >= 0).all() and (
          pred_proba <= 1
      ).all(), "found prediction probability value outside of [0, 1]!"
  
      return pred_proba
  
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 3 for calling the AutoSklearnClassifier obj's underlying member method code with example usage: obj.predict_proba(X, batch_size, n_jobs) and returns: pred_proba