class_docstr: Class for the manifold of full-rank correlation matrices.
  
  Parameters
  ----------
  n : int
      Integer representing the shape of the matrices: n x n.
docstr: Compute the correlation matrix associated to an SPD matrix.
  
  The correlation matrix associated to an SPD matrix (the covariance)
  :math:`\Sigma` is given by :math:`D \Sigma D` where :math:`D` is
  the inverse square-root of the diagonal of :math:`\Sigma`.
  
  Parameters
  ----------
  point : array-like, shape=[..., n, n]
      Symmetric Positive definite matrix.
  
  Returns
  -------
  corr : array-like, shape=[..., n, n]
      Correlation matrix obtained by dividing all elements by the
      diagonal entries.
docstr_tok: ["Compute","the","correlation","matrix","associated","to","an","SPD","matrix",".","The","correlation","matrix","associated","to","an","SPD","matrix","(","the","covariance",")",":","math",":","`","\\Sigma","`","is","given","by",":","math",":","`","D","\\Sigma","D","`","where",":","math",":","`","D","`","is","the","inverse","square-root","of","the","diagonal","of",":","math",":","`","\\Sigma","`",".","Parameters","--","--","--","--","--","point",":","array-like",",","shape=","[","...",",","n",",","n","]","Symmetric","Positive","definite","matrix",".","Returns","--","--","--","-","corr",":","array-like",",","shape=","[","...",",","n",",","n","]","Correlation","matrix","obtained","by","dividing","all","elements","by","the","diagonal","entries","."]
code: def from_covariance(cls, point):
      r"""Compute the correlation matrix associated to an SPD matrix.
  
      The correlation matrix associated to an SPD matrix (the covariance)
      :math:`\Sigma` is given by :math:`D \Sigma D` where :math:`D` is
      the inverse square-root of the diagonal of :math:`\Sigma`.
  
      Parameters
      ----------
      point : array-like, shape=[..., n, n]
          Symmetric Positive definite matrix.
  
      Returns
      -------
      corr : array-like, shape=[..., n, n]
          Correlation matrix obtained by dividing all elements by the
          diagonal entries.
      """
      diag_vec = Matrices.diagonal(point) ** (-0.5)
      return cls.diag_action(diag_vec, point)
  
imports: import geomstats.backend
  from geomstats.geometry.base import LevelSet
  from geomstats.geometry.diffeo import ComposedDiffeo
  from geomstats.geometry.fiber_bundle import FiberBundle
  from geomstats.geometry.general_linear import GeneralLinear
  from geomstats.geometry.hyperboloid import Hyperboloid
  from geomstats.geometry.matrices import Matrices
  from geomstats.geometry.open_hemisphere import OpenHemispheresProduct, OpenHemisphereToHyperboloidDiffeo
  from geomstats.geometry.positive_lower_triangular_matrices import UnitNormedRowsPLTDiffeo
  from geomstats.geometry.pullback_metric import PullbackDiffeoMetric
  from geomstats.geometry.quotient_metric import QuotientMetric
  from geomstats.geometry.spd_matrices import CholeskyMap, SPDAffineMetric, SPDMatrices
label_desc: Use image node_id 7 for calling the FullRankCorrelationMatrices obj's underlying member method code with example usage: obj.from_covariance(cls, point) and returns: cls