class_docstr: LogNormal Distribution on manifold of SPD Matrices.
docstr: Generate symmetric matrices.
docstr_tok: ["Generate","symmetric","matrices","."]
code: def samples_sym(self, mean_vec, cov, n_samples):
      """Generate symmetric matrices."""
      n = self.mean.shape[-1]
      samples_euclidean = gs.random.multivariate_normal(
          mean_vec, cov, (n_samples,)
      )
      diag = samples_euclidean[:, :n]
      off_diag = samples_euclidean[:, n:] / gs.sqrt(2.0)
      samples_sym = gs.mat_from_diag_triu_tril(
          diag=diag, tri_upp=off_diag, tri_low=off_diag
      )
      return samples_sym
  
file_path: geomstats/geomstats/distributions/lognormal.py
imports: import geomstats.backend
  from geomstats.geometry.euclidean import Euclidean, EuclideanMetric
  from geomstats.geometry.hermitian_matrices import expmh, powermh
  from geomstats.geometry.matrices import Matrices, MatricesMetric
  from geomstats.geometry.spd_matrices import SPDAffineMetric, SPDLogEuclideanMetric, SPDMatrices, logmh
label_desc: Use image node_id 3 for calling the LogNormalSPD obj's underlying member method code with example usage: obj.samples_sym(mean_vec, cov, n_samples) and returns: samples_sym