code: def __init__(
      self,
      n_neighbors=5,
      *,
      weights="uniform",
      algorithm="auto",
      p=2,
      metric="minkowski",
      metric_params=None,
      **kwargs,
  ):
      super().__init__(
          n_neighbors=n_neighbors,
          algorithm=algorithm,
          metric=metric,
          p=p,
          metric_params=metric_params,
          **kwargs,
      )
      self.weights = weights
  
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 1 to create a new NearestNeighbors object from inherited base classes: NeighborsBase with example: obj = NearestNeighbors(n_neighbors)