code: def __init__(
      self,
      n_neighbors=5,
      radius=1.0,
      algorithm="auto",
      leaf_size=30,
      metric="minkowski",
      p=2,
      metric_params=None,
      n_jobs=None,
  ):
      super().__init__(
          n_neighbors=n_neighbors,
          radius=radius,
          algorithm=algorithm,
          leaf_size=leaf_size,
          metric=metric,
          p=p,
          metric_params=metric_params,
          n_jobs=n_jobs,
      )
  
imports: import warnings
  import numpy
  from sklearn import __version__
  from sklearn.neighbors._ball_tree import BallTree
  from sklearn.neighbors._base import VALID_METRICS
  from sklearn.neighbors._base import NeighborsBase
  from sklearn.neighbors._kd_tree import KDTree
  from sklearn.neighbors._unsupervised import NearestNeighbors
  from sklearn.utils.validation import _deprecate_positional_args, check_is_fitted
  from daal4py.sklearn._utils import control_n_jobs, run_with_n_jobs, sklearn_check_version
  from onedal.neighbors import NearestNeighbors
  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 1 to create a new NearestNeighbors object from inherited base classes: NearestNeighbors_, KNeighborsDispatchingBase with example: obj = NearestNeighbors(n_neighbors, radius, algorithm, leaf_size, metric, p, metric_params, n_jobs)