class_docstr: A single linkage format dendrogram tree, with plotting functionality
  and networkX support.
  
  Parameters
  ----------
  linkage : ndarray (n_samples, 4)
      The numpy array that holds the tree structure. As output by
      scipy.cluster.hierarchy, hdbscan, of fastcluster.
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docstr: Return a flat clustering from the single linkage hierarchy.
  
  This represents the result of selecting a cut value for robust single linkage
  clustering. The `min_cluster_size` allows the flat clustering to declare noise
  points (and cluster smaller than `min_cluster_size`).
  
  Parameters
  ----------
  
  cut_distance : float
      The mutual reachability distance cut value to use to generate a flat clustering.
  
  min_cluster_size : int, optional
      Clusters smaller than this value with be called 'noise' and remain unclustered
      in the resulting flat clustering.
  
  Returns
  -------
  
  labels : array [n_samples]
      An array of cluster labels, one per datapoint. Unclustered points are assigned
      the label -1.
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code: def get_clusters(self, cut_distance, min_cluster_size=5):
      """Return a flat clustering from the single linkage hierarchy.
  
      This represents the result of selecting a cut value for robust single linkage
      clustering. The `min_cluster_size` allows the flat clustering to declare noise
      points (and cluster smaller than `min_cluster_size`).
  
      Parameters
      ----------
  
      cut_distance : float
          The mutual reachability distance cut value to use to generate a flat clustering.
  
      min_cluster_size : int, optional
          Clusters smaller than this value with be called 'noise' and remain unclustered
          in the resulting flat clustering.
  
      Returns
      -------
  
      labels : array [n_samples]
          An array of cluster labels, one per datapoint. Unclustered points are assigned
          the label -1.
      """
      return labelling_at_cut(
          self._linkage, cut_distance, min_cluster_size
      )
  
imports: import numpy
  from scipy.cluster.hierarchy import dendrogram
  from sklearn.manifold import TSNE
  from sklearn.decomposition import PCA
  from sklearn.metrics import pairwise_distances
  from warnings import warn
  from ._hdbscan_tree import compute_stability, labelling_at_cut, recurse_leaf_dfs
label_desc: Use image node_id 6 for calling the SingleLinkageTree obj's underlying member method code with example usage: obj.get_clusters(cut_distance, min_cluster_size) and returns: labelling_at_cut