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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docstr: "Returns the mean accuracy on the given test data and labels.
  
  Parameters
  ----------
  X : array-like, shape [n_samples, n_features]
      Test samples
  
  y : array-like, shape [n_samples, n_outputs]
      True values for X
  
  Returns
  -------
  scores : float
      accuracy_score of self.predict(X) versus y
docstr_tok: ["``","Returns","the","mean","accuracy","on","the","given","test","data","and","labels",".","Parameters","--","--","--","--","--","X",":","array-like",",","shape","[","n_samples",",","n_features","]","Test","samples","y",":","array-like",",","shape","[","n_samples",",","n_outputs","]","True","values","for","X","Returns","--","--","--","-","scores",":","float","accuracy_score","of","self.predict","(","X",")","versus","y"]
code: def score(self, X, y):
      """ "Returns the mean accuracy on the given test data and labels.
  
      Parameters
      ----------
      X : array-like, shape [n_samples, n_features]
          Test samples
  
      y : array-like, shape [n_samples, n_outputs]
          True values for X
  
      Returns
      -------
      scores : float
          accuracy_score of self.predict(X) versus y
      """
      check_is_fitted(self, "estimators_")
      n_outputs_ = len(self.estimators_)
      if y.ndim == 1:
          raise ValueError(
              "y must have at least two dimensions for "
              "multi target classification but has only one"
          )
      if y.shape[1] != n_outputs_:
          raise ValueError(
              "The number of outputs of Y for fit {0} and"
              " score {1} should be same".format(n_outputs_, y.shape[1])
          )
      y_pred = self.predict(X)
      return np.mean(np.all(y == y_pred, axis=1))
  
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 3 for calling the MultiOutputClassifier obj's underlying member method code with example usage: obj.score(X, y) and returns: np