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","."]
code: def __init__(self, n, symmetric=False, equip=True):
      super().__init__(
          dim=n**2,
          embedding_space=ComplexMatrices(m=n, n=n),
          equip=equip,
      )
      self.n = n
      self.symmetric = symmetric
  
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 1 to create a new Siegel object from inherited base classes: ComplexVectorSpaceOpenSet with example: obj = Siegel(n, symmetric, equip)