class_docstr: Multi target classification
  
  This strategy consists of fitting one classifier per target. This is a
  simple strategy for extending classifiers that do not natively support
  multi-target classification
  
  Parameters
  ----------
  estimator : estimator object
      An estimator object implementing `fit`, `score` and `predict_proba`.
  
  n_jobs : int, optional, default=1
      The number of jobs to use for the computation. If -1 all CPUs are used.
      If 1 is given, no parallel computing code is used at all, which is
      useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are
      used. Thus for n_jobs = -2, all CPUs but one are used.
      The number of jobs to use for the computation.
      It does each target variable in y in parallel.
  
  Attributes
  ----------
  estimators_ : list of `n_output` estimators
      Estimators used for predictions.
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code: def __init__(self, estimator, n_jobs=1):
      super(MultiOutputClassifier, self).__init__(estimator, n_jobs)
  
imports: import numpy
  from abc import ABCMeta
  from .base import BaseEstimator, clone
  from .base import RegressorMixin, ClassifierMixin
  from .utils import check_array, check_X_y
  from .utils.fixes import parallel_helper
  from .utils.validation import check_is_fitted, has_fit_parameter
  from .externals.joblib import Parallel, delayed
  from .externals import six
label_desc: Use image node_id 1 to create a new MultiOutputClassifier object from inherited base classes: MultiOutputEstimator, ClassifierMixin with example: obj = MultiOutputClassifier(estimator, n_jobs)