docstr: Predict multi-output variable using a model
   trained for each target variable.
  
  Parameters
  ----------
  X : (sparse) array-like, shape (n_samples, n_features)
      Data.
  
  Returns
  -------
  y : (sparse) array-like, shape (n_samples, n_outputs)
      Multi-output targets predicted across multiple predictors.
      Note: Separate models are generated for each predictor.
docstr_tok: ["Predict","multi-output","variable","using","a","model","trained","for","each","target","variable",".","Parameters","--","--","--","--","--","X",":","(","sparse",")","array-like",",","shape","(","n_samples",",","n_features",")","Data",".","Returns","--","--","--","-","y",":","(","sparse",")","array-like",",","shape","(","n_samples",",","n_outputs",")","Multi-output","targets","predicted","across","multiple","predictors",".","Note",":","Separate","models","are","generated","for","each","predictor","."]
code: def predict(self, X):
      """Predict multi-output variable using a model
       trained for each target variable.
  
      Parameters
      ----------
      X : (sparse) array-like, shape (n_samples, n_features)
          Data.
  
      Returns
      -------
      y : (sparse) array-like, shape (n_samples, n_outputs)
          Multi-output targets predicted across multiple predictors.
          Note: Separate models are generated for each predictor.
      """
      check_is_fitted(self, "estimators_")
      if not hasattr(self.estimator, "predict"):
          raise ValueError(
              "The base estimator should implement a predict method"
          )
  
      X = check_array(X, accept_sparse=True)
  
      y = Parallel(n_jobs=self.n_jobs)(
          delayed(parallel_helper)(e, "predict", X)
          for e in self.estimators_
      )
  
      return np.asarray(y).T
  
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 MultiOutputEstimator obj's underlying member method code with example usage: obj.predict(X) and returns: np