code: def _predict_skl(self, X, queue=None):
      neigh_dist, neigh_ind = self.kneighbors(X, queue=queue)
  
      weights = self._get_weights(neigh_dist, self.weights)
  
      _y = self._y
      if _y.ndim == 1:
          _y = _y.reshape((-1, 1))
  
      if weights is None:
          y_pred = np.mean(_y[neigh_ind], axis=1)
      else:
          y_pred = np.empty((X.shape[0], _y.shape[1]), dtype=np.float64)
          denom = np.sum(weights, axis=1)
  
          for j in range(_y.shape[1]):
              num = np.sum(_y[neigh_ind, j] * weights, axis=1)
              y_pred[:, j] = num / denom
  
      if self._y.ndim == 1:
          y_pred = y_pred.ravel()
  
      return y_pred
  
imports: from abc import ABCMeta
  from numbers import Integral
  import numpy
  from daal4py import bf_knn_classification_prediction, bf_knn_classification_training, kdtree_knn_classification_prediction, kdtree_knn_classification_training
  from onedal import _backend
  from ..common._estimator_checks import _check_is_fitted, _is_classifier, _is_regressor
  from ..common._mixin import ClassifierMixin, RegressorMixin
  from ..common._policy import _get_policy
  from ..datatypes import _convert_to_supported, from_table, to_table
  from ..utils import _check_array, _check_classification_targets, _check_n_features, _check_X_y, _column_or_1d, _num_samples
label_desc: Use image node_id 9 for calling the KNeighborsRegressor obj's underlying member method code with example usage: obj._predict_skl(X, queue) and returns: y_pred