code: def _numpy_cholesky(A, B):
      
      L = numpy.linalg.cholesky(A)
      # A=L*numpy.transpose(L).conjugate()
      # Ly = b
      y = numpy.linalg.solve(L,B)
      # Ux = y
      x = numpy.linalg.solve(L.transpose().conjugate(),y)
      return x, L
docs: Solve Ax=B using numpy cholesky solver
  
      A = LU
  
      in the case where A is square and Hermitian, A = L.L* where L* is
      transpoed and conjugate matrix
  
      Ly = b
  
      where
  
      Ux=y
  
      so x = U^{-1} y
      where U = L*
      and y = L^{-1} B