class_docstr: Class for the Wald space, a metric space for phylogenetic forests.
  
  Parameters
  ----------
  n_labels : int
      Integer determining the number of labels in the forests, and thus the shape of
      the correlation matrices: n_labels x n_labels.
  
  Attributes
  ----------
  ambient :
      The ambient space, the positive definite n_labels x n_labels matrices that the
      WaldSpace is embedded into.
class_docstr_tok: ["Class","for","the","Wald","space",",","a","metric","space","for","phylogenetic","forests",".","Parameters","--","--","--","--","--","n_labels",":","int","Integer","determining","the","number","of","labels","in","the","forests",",","and","thus","the","shape","of","the","correlation","matrices",":","n_labels","x","n_labels",".","Attributes","--","--","--","--","--","ambient",":","The","ambient","space",",","the","positive","definite","n_labels","x","n_labels","matrices","that","the","WaldSpace","is","embedded","into","."]
code: def __init__(self, n_labels):
      super().__init__(equip=False)
      self.n_labels = n_labels
      self.ambient = spd.SPDMatrices(n=self.n_labels)
  
imports: import functools
  import itertools
  import geomstats.backend
  import geomstats.geometry.spd_matrices
  from geomstats.geometry.stratified.point_set import Point, PointSet, _vectorize_point
label_desc: Use image node_id 1 to create a new WaldSpace object from inherited base classes: PointSet with example: obj = WaldSpace(n_labels)