class_docstr: Class for the Siegel space.
  
  Parameters
  ----------
  n : int
      Integer representing the shape of the matrices: n x n.
  symmetric : bool
      If symmetric is True, add a symmetry condition
      on the matrices to belong to the Siegel space.
      Optional, default: False.
class_docstr_tok: ["Class","for","the","Siegel","space",".","Parameters","--","--","--","--","--","n",":","int","Integer","representing","the","shape","of","the","matrices",":","n","x","n.","symmetric",":","bool","If","symmetric","is","True",",","add","a","symmetry","condition","on","the","matrices","to","belong","to","the","Siegel","space",".","Optional",",","default",":","False","."]
docstr: Metric to equip the space with if equip is True.
docstr_tok: ["Metric","to","equip","the","space","with","if","equip","is","True","."]
code: def default_metric():
      """Metric to equip the space with if equip is True."""
      return SiegelMetric
  
imports: import geomstats.backend
  from geomstats.geometry.base import ComplexVectorSpaceOpenSet
  from geomstats.geometry.complex_matrices import ComplexMatrices
  from geomstats.geometry.complex_riemannian_metric import ComplexRiemannianMetric
  from geomstats.geometry.hermitian_matrices import expmh, powermh
  from geomstats.geometry.matrices import Matrices
label_desc: Use image node_id 2 for calling the Siegel obj's underlying member method code with example usage: obj.default_metric() and returns: SiegelMetric