code: def _iter_log(self, p, matrix_v):
      matrix_lv = gs.zeros_like(matrix_v)
      for _ in range(self.max_iter):
          matrix_lv = gs.linalg.logm(matrix_v)
  
          matrix_c = matrix_lv[..., p:, p:]
          norm_matrix_c = gs.linalg.norm(matrix_c)
          if norm_matrix_c <= self.tol:
              break
  
          matrix_phi = gs.linalg.expm(
              -Matrices.to_skew_symmetric(matrix_c)
          )
          aux_matrix = gs.matmul(matrix_v[..., :, p:], matrix_phi)
          matrix_v = gs.concatenate(
              [matrix_v[..., :, :p], aux_matrix], axis=-1
          )
  
      else:
          warnings.warn("`log` hasn't converged.")
  
      if gs.is_complex(matrix_lv):
          imag_sum = gs.amax(gs.abs(gs.imag(matrix_lv)))
          if imag_sum < self.imag_tol:
              matrix_lv = gs.real(matrix_lv)
          else:
              raise ValueError(
                  f"Non-neglible imaginary part. max is {imag_sum}"
              )
  
      return matrix_lv
  
imports: import warnings
  import geomstats.backend
  import geomstats.errors
  from geomstats import algebra_utils
  from geomstats.geometry.base import LevelSet
  from geomstats.geometry.hermitian_matrices import powermh
  from geomstats.geometry.matrices import Matrices
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.vectorization import repeat_out
label_desc: Use image node_id 6 for calling the _StiefelLogSolver obj's underlying member method code with example usage: obj._iter_log(p, matrix_v) and returns: matrix_lv