code: def cluster_optics_xi(
      *,
      reachability,
      predecessor,
      ordering,
      min_samples,
      min_cluster_size=None,
      xi=0.05,
      predecessor_correction=True,
  ):
      """Automatically extract clusters according to the Xi-steep method.
  
      Parameters
      ----------
      reachability : ndarray of shape (n_samples,)
          Reachability distances calculated by OPTICS (`reachability_`).
  
      predecessor : ndarray of shape (n_samples,)
          Predecessors calculated by OPTICS.
  
      ordering : ndarray of shape (n_samples,)
          OPTICS ordered point indices (`ordering_`).
  
      min_samples : int > 1 or float between 0 and 1
          The same as the min_samples given to OPTICS. Up and down steep regions
          can't have more then ``min_samples`` consecutive non-steep points.
          Expressed as an absolute number or a fraction of the number of samples
          (rounded to be at least 2).
  
      min_cluster_size : int > 1 or float between 0 and 1, default=None
          Minimum number of samples in an OPTICS cluster, expressed as an
          absolute number or a fraction of the number of samples (rounded to be
          at least 2). If ``None``, the value of ``min_samples`` is used instead.
  
      xi : float between 0 and 1, default=0.05
          Determines the minimum steepness on the reachability plot that
          constitutes a cluster boundary. For example, an upwards point in the
          reachability plot is defined by the ratio from one point to its
          successor being at most 1-xi.
  
      predecessor_correction : bool, default=True
          Correct clusters based on the calculated predecessors.
  
      Returns
      -------
      labels : ndarray of shape (n_samples,)
          The labels assigned to samples. Points which are not included
          in any cluster are labeled as -1.
  
      clusters : ndarray of shape (n_clusters, 2)
          The list of clusters in the form of ``[start, end]`` in each row, with
          all indices inclusive. The clusters are ordered according to ``(end,
          -start)`` (ascending) so that larger clusters encompassing smaller
          clusters come after such nested smaller clusters. Since ``labels`` does
          not reflect the hierarchy, usually ``len(clusters) >
          np.unique(labels)``.
  
      Examples
      --------
      >>> import numpy as np
      >>> from sklearn.cluster import cluster_optics_xi, compute_optics_graph
      >>> X = np.array([[1, 2], [2, 5], [3, 6],
      ...               [8, 7], [8, 8], [7, 3]])
      >>> ordering, core_distances, reachability, predecessor = compute_optics_graph(
      ...     X,
      ...     min_samples=2,
      ...     max_eps=np.inf,
      ...     metric="minkowski",
      ...     p=2,
      ...     metric_params=None,
      ...     algorithm="auto",
      ...     leaf_size=30,
      ...     n_jobs=None
      ... )
      >>> min_samples = 2
      >>> labels, clusters = cluster_optics_xi(
      ...     reachability=reachability,
      ...     predecessor=predecessor,
      ...     ordering=ordering,
      ...     min_samples=min_samples,
      ... )
      >>> labels
      array([0, 0, 0, 1, 1, 1])
      >>> clusters
      array([[0, 2],
             [3, 5],
             [0, 5]])
      """
      n_samples = len(reachability)
      _validate_size(min_samples, n_samples, "min_samples")
      if min_samples <= 1:
          min_samples = max(2, int(min_samples * n_samples))
      if min_cluster_size is None:
          min_cluster_size = min_samples
      _validate_size(min_cluster_size, n_samples, "min_cluster_size")
      if min_cluster_size <= 1:
          min_cluster_size = max(2, int(min_cluster_size * n_samples))
  
      clusters = _xi_cluster(
          reachability[ordering],
          predecessor[ordering],
          ordering,
          xi,
          min_samples,
          min_cluster_size,
          predecessor_correction,
      )
      labels = _extract_xi_labels(ordering, clusters)
      return labels, clusters
  
imports: import warnings
  from numbers import Integral, Real
  import numpy
  from scipy.sparse import SparseEfficiencyWarning, issparse
  from ..base import BaseEstimator, ClusterMixin, _fit_context
  from ..exceptions import DataConversionWarning
  from ..metrics import pairwise_distances
  from ..metrics.pairwise import _VALID_METRICS, PAIRWISE_BOOLEAN_FUNCTIONS
  from ..neighbors import NearestNeighbors
  from ..utils import gen_batches, get_chunk_n_rows
  from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions, validate_params
  from ..utils.validation import check_memory
label_desc: Use image node_id 6 for calling a global function with example usage: cluster_optics_xi() and returns: labels,  clusters