class_docstr: Multi target regression
  
  This strategy consists of fitting one regressor per target. This is a
  simple strategy for extending regressors that do not natively support
  multi-target regression.
  
  Parameters
  ----------
  estimator : estimator object
      An estimator object implementing `fit` and `predict`.
  
  n_jobs : int, optional, default=1
      The number of jobs to run in parallel for `fit`. If -1,
      then the number of jobs is set to the number of cores.
      When individual estimators are fast to train or predict
      using `n_jobs>1` can result in slower performance due
      to the overhead of spawning processes.
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code: def __init__(self, estimator, n_jobs=1):
      super(MultiOutputRegressor, 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 MultiOutputRegressor object from inherited base classes: MultiOutputEstimator, RegressorMixin with example: obj = MultiOutputRegressor(estimator, n_jobs)