docstr: Predict multi-output variable using model for each target variable.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      The input data.
  
  Returns
  -------
  y : {array-like, sparse matrix} of 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 model for each target variable.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          The input data.
  
      Returns
      -------
      y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
          Multi-output targets predicted across multiple predictors.
          Note: Separate models are generated for each predictor.
      """
      check_is_fitted(self)
      if not hasattr(self.estimators_[0], "predict"):
          raise ValueError(
              "The base estimator should implement a predict method"
          )
  
      y = Parallel(n_jobs=self.n_jobs)(
          delayed(e.predict)(X) for e in self.estimators_
      )
  
      return np.asarray(y).T
  
imports: from abc import ABCMeta, abstractmethod
  from numbers import Integral
  import numpy
  import scipy.sparse
  from .base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, _fit_context, clone, is_classifier
  from .model_selection import cross_val_predict
  from .utils import Bunch, _print_elapsed_time, check_random_state
  from .utils._param_validation import HasMethods, StrOptions
  from .utils.metadata_routing import MetadataRouter, MethodMapping, _routing_enabled, process_routing
  from .utils.metaestimators import available_if
  from .utils.multiclass import check_classification_targets
  from .utils.parallel import Parallel, delayed
  from .utils.validation import _check_fit_params, check_is_fitted, has_fit_parameter
label_desc: Use image node_id 4 for calling the _MultiOutputEstimator obj's underlying member method code with example usage: obj.predict(X) and returns: np