code: def _onedal_fit(self, X, y, queue=None):
      onedal_params = {
          "n_neighbors": self.n_neighbors,
          "weights": self.weights,
          "algorithm": self.algorithm,
          "metric": self.effective_metric_,
          "p": self.effective_metric_params_["p"],
      }
  
      try:
          requires_y = self._get_tags()["requires_y"]
      except KeyError:
          requires_y = False
  
      self._onedal_estimator = onedal_KNeighborsRegressor(
          **onedal_params
      )
      self._onedal_estimator.requires_y = requires_y
      self._onedal_estimator.effective_metric_ = self.effective_metric_
      self._onedal_estimator.effective_metric_params_ = (
          self.effective_metric_params_
      )
      self._onedal_estimator.fit(X, y, queue=queue)
  
      self._save_attributes()
  
imports: import warnings
  from sklearn.neighbors._ball_tree import BallTree
  from sklearn.neighbors._base import NeighborsBase
  from sklearn.neighbors._kd_tree import KDTree
  from daal4py.sklearn._utils import control_n_jobs, run_with_n_jobs, sklearn_check_version
  import numpy
  from sklearn.neighbors._base import VALID_METRICS
  from sklearn.neighbors._regression import KNeighborsRegressor
  from sklearn.neighbors._unsupervised import NearestNeighbors
  from sklearn.utils.validation import _deprecate_positional_args, check_is_fitted
  from onedal.neighbors import KNeighborsRegressor
  from onedal.utils import _check_array, _num_features, _num_samples
  from .._device_offload import dispatch, wrap_output_data
  from .common import KNeighborsDispatchingBase
label_desc: Use image node_id 5 for calling the KNeighborsRegressor obj's underlying member method code with example usage: obj._onedal_fit(X, y, queue) without return types