class_docstr: Class for the Siegel metric.
class_docstr_tok: ["Class","for","the","Siegel","metric","."]
docstr: Compute the Siegel logarithm map.
  
  Compute the Riemannian logarithm at point base_point
  of point wrt the metric defined in inner_product.
  Return a tangent vector at point base_point.
  
  Parameters
  ----------
  point : array-like, shape=[..., n, n]
      Point on the Siegel space.
  base_point : array-like, shape=[..., n, n]
      Point on the Siegel space.
  
  Returns
  -------
  log : array-like, shape=[..., n, n]
      Riemannian logarithm at the base point.
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code: def log(self, point, base_point):
      """Compute the Siegel logarithm map.
  
      Compute the Riemannian logarithm at point base_point
      of point wrt the metric defined in inner_product.
      Return a tangent vector at point base_point.
  
      Parameters
      ----------
      point : array-like, shape=[..., n, n]
          Point on the Siegel space.
      base_point : array-like, shape=[..., n, n]
          Point on the Siegel space.
  
      Returns
      -------
      log : array-like, shape=[..., n, n]
          Riemannian logarithm at the base point.
      """
      point_at_zero = self.isometry(
          point=point, point_to_zero=base_point
      )
  
      logarithm_at_zero = self.log_at_zero(point_at_zero)
  
      return self.tangent_vec_from_zero_to_base_point(
          tangent_vec=logarithm_at_zero, base_point=base_point
      )
  
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 8 for calling the SiegelMetric obj's underlying member method code with example usage: obj.log(point, base_point) and returns: self