class_docstr: Classifier implementing a vote among neighbors within a given radius.
  
  Read more in the :ref:`User Guide <classification>`.
  
  Parameters
  ----------
  radius : float, default=1.0
      Range of parameter space to use by default for :meth:`radius_neighbors`
      queries.
  
  weights : {'uniform', 'distance'}, callable or None, default='uniform'
      Weight function used in prediction.  Possible values:
  
      - 'uniform' : uniform weights.  All points in each neighborhood
        are weighted equally.
      - 'distance' : weight points by the inverse of their distance.
        in this case, closer neighbors of a query point will have a
        greater influence than neighbors which are further away.
      - [callable] : a user-defined function which accepts an
        array of distances, and returns an array of the same shape
        containing the weights.
  
      Uniform weights are used by default.
  
  algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
      Algorithm used to compute the nearest neighbors:
  
      - 'ball_tree' will use :class:`BallTree`
      - 'kd_tree' will use :class:`KDTree`
      - 'brute' will use a brute-force search.
      - 'auto' will attempt to decide the most appropriate algorithm
        based on the values passed to :meth:`fit` method.
  
      Note: fitting on sparse input will override the setting of
      this parameter, using brute force.
  
  leaf_size : int, default=30
      Leaf size passed to BallTree or KDTree.  This can affect the
      speed of the construction and query, as well as the memory
      required to store the tree.  The optimal value depends on the
      nature of the problem.
  
  p : float, default=2
      Power parameter for the Minkowski metric. When p = 1, this is
      equivalent to using manhattan_distance (l1), and euclidean_distance
      (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
  
  metric : str or callable, default='minkowski'
      Metric to use for distance computation. Default is "minkowski", which
      results in the standard Euclidean distance when p = 2. See the
      documentation of `scipy.spatial.distance
      <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
      the metrics listed in
      :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
      values.
  
      If metric is "precomputed", X is assumed to be a distance matrix and
      must be square during fit. X may be a :term:`sparse graph`, in which
      case only "nonzero" elements may be considered neighbors.
  
      If metric is a callable function, it takes two arrays representing 1D
      vectors as inputs and must return one value indicating the distance
      between those vectors. This works for Scipy's metrics, but is less
      efficient than passing the metric name as a string.
  
  outlier_label : {manual label, 'most_frequent'}, default=None
      Label for outlier samples (samples with no neighbors in given radius).
  
      - manual label: str or int label (should be the same type as y)
        or list of manual labels if multi-output is used.
      - 'most_frequent' : assign the most frequent label of y to outliers.
      - None : when any outlier is detected, ValueError will be raised.
  
  metric_params : dict, default=None
      Additional keyword arguments for the metric function.
  
  n_jobs : int, default=None
      The number of parallel jobs to run for neighbors search.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      for more details.
  
  Attributes
  ----------
  classes_ : ndarray of shape (n_classes,)
      Class labels known to the classifier.
  
  effective_metric_ : str or callable
      The distance metric used. It will be same as the `metric` parameter
      or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
      'minkowski' and `p` parameter set to 2.
  
  effective_metric_params_ : dict
      Additional keyword arguments for the metric function. For most metrics
      will be same with `metric_params` parameter, but may also contain the
      `p` parameter value if the `effective_metric_` attribute is set to
      'minkowski'.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  n_samples_fit_ : int
      Number of samples in the fitted data.
  
  outlier_label_ : int or array-like of shape (n_class,)
      Label which is given for outlier samples (samples with no neighbors
      on given radius).
  
  outputs_2d_ : bool
      False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit
      otherwise True.
  
  See Also
  --------
  KNeighborsClassifier : Classifier implementing the k-nearest neighbors
      vote.
  RadiusNeighborsRegressor : Regression based on neighbors within a
      fixed radius.
  KNeighborsRegressor : Regression based on k-nearest neighbors.
  NearestNeighbors : Unsupervised learner for implementing neighbor
      searches.
  
  Notes
  -----
  See :ref:`Nearest Neighbors <neighbors>` in the online documentation
  for a discussion of the choice of ``algorithm`` and ``leaf_size``.
  
  https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
  
  Examples
  --------
  >>> X = [[0], [1], [2], [3]]
  >>> y = [0, 0, 1, 1]
  >>> from sklearn.neighbors import RadiusNeighborsClassifier
  >>> neigh = RadiusNeighborsClassifier(radius=1.0)
  >>> neigh.fit(X, y)
  RadiusNeighborsClassifier(...)
  >>> print(neigh.predict([[1.5]]))
  [0]
  >>> print(neigh.predict_proba([[1.0]]))
  [[0.66666667 0.33333333]]
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docstr: Fit the radius neighbors classifier from the training dataset.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features) or                 (n_samples, n_samples) if metric='precomputed'
      Training data.
  
  y : {array-like, sparse matrix} of shape (n_samples,) or                 (n_samples, n_outputs)
      Target values.
  
  Returns
  -------
  self : RadiusNeighborsClassifier
      The fitted radius neighbors classifier.
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code: def fit(self, X, y):
      """Fit the radius neighbors classifier from the training dataset.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
                  (n_samples, n_samples) if metric='precomputed'
              Training data.
  
          y : {array-like, sparse matrix} of shape (n_samples,) or \
                  (n_samples, n_outputs)
              Target values.
  
          Returns
          -------
          self : RadiusNeighborsClassifier
              The fitted radius neighbors classifier.
          """
      self._fit(X, y)
  
      classes_ = self.classes_
      _y = self._y
      if not self.outputs_2d_:
          _y = self._y.reshape((-1, 1))
          classes_ = [self.classes_]
  
      if self.outlier_label is None:
          outlier_label_ = None
  
      elif self.outlier_label == "most_frequent":
          outlier_label_ = []
          # iterate over multi-output, get the most frequent label for each
          # output.
          for k, classes_k in enumerate(classes_):
              label_count = np.bincount(_y[:, k])
              outlier_label_.append(classes_k[label_count.argmax()])
  
      else:
          if _is_arraylike(self.outlier_label) and not isinstance(
              self.outlier_label, str
          ):
              if len(self.outlier_label) != len(classes_):
                  raise ValueError(
                      "The length of outlier_label: {} is "
                      "inconsistent with the output "
                      "length: {}".format(
                          self.outlier_label, len(classes_)
                      )
                  )
              outlier_label_ = self.outlier_label
          else:
              outlier_label_ = [self.outlier_label] * len(classes_)
  
          for classes, label in zip(classes_, outlier_label_):
              if _is_arraylike(label) and not isinstance(label, str):
                  # ensure the outlier label for each output is a scalar.
                  raise TypeError(
                      "The outlier_label of classes {} is "
                      "supposed to be a scalar, got "
                      "{}.".format(classes, label)
                  )
              if np.append(classes, label).dtype != classes.dtype:
                  # ensure the dtype of outlier label is consistent with y.
                  raise TypeError(
                      "The dtype of outlier_label {} is "
                      "inconsistent with classes {} in "
                      "y.".format(label, classes)
                  )
  
      self.outlier_label_ = outlier_label_
  
      return self
  
imports: import warnings
  from numbers import Integral
  import numpy
  from sklearn.neighbors._base import _check_precomputed
  from ..base import ClassifierMixin, _fit_context
  from ..metrics._pairwise_distances_reduction import ArgKminClassMode
  from ..utils._param_validation import StrOptions
  from ..utils.extmath import weighted_mode
  from ..utils.fixes import _mode
  from ..utils.validation import _is_arraylike, _num_samples, check_is_fitted
  from ._base import KNeighborsMixin, NeighborsBase, RadiusNeighborsMixin, _get_weights
label_desc: Use image node_id 2 for calling the RadiusNeighborsClassifier obj's underlying member method code with example usage: obj.fit(X, y) and returns: self