old_contents: import numpy
  
  import cupy
  from cupy import cuda
  from cupy.cuda import device
  from cupy.linalg import util
  import cupy.sparse
  
  
  if cuda.cusolver_enabled:
      from cupy.cuda import cusolver
  
  
  def lschol(A, b):
      """Solves linear system with cholesky decomposition.
  
      Find the solution to a large, sparse, linear system of equations.
      The function solves ``Ax = b``. Given two-dimensional matrix ``A`` is
      decomposed into ``L * L^*``.
  
      Args:
          A (cupy.ndarray or cupy.sparse.csr_matrix): The input matrix with
              dimension ``(N, N)``
          b (cupy.ndarray): Right-hand side vector.
  
      Returns:
          ret (cupy.ndarray): The solution vector ``x``.
  
      """
  
      if not cuda.cusolver_enabled:
          raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
  
      if not cupy.sparse.isspmatrix_csr(A):
          A = cupy.sparse.csr_matrix(A)
      util._assert_nd_squareness(A)
      util._assert_cupy_array(b)
      m = A.shape[0]
      if b.ndim != 1 or len(b) != m:
          raise ValueError('b must be 1-d array whose size is same as A')
  
      # Cast to float32 or float64
      if A.dtype == 'f' or A.dtype == 'd':
          dtype = A.dtype
      else:
          dtype = numpy.find_common_type((A.dtype, 'f'), ())
  
      handle = device.get_cusolver_sp_handle()
      nnz = A.nnz
      tol = 1.0
      reorder = 1
      x = cupy.empty(m, dtype=dtype)
      singularity = numpy.empty(1, numpy.int32)
  
      if dtype == 'f':
          csrlsvchol = cusolver.scsrlsvchol
      else:
          csrlsvchol = cusolver.dcsrlsvchol
      csrlsvchol(
          handle, m, nnz, A._descr.descriptor, A.data.data.ptr,
          A.indptr.data.ptr, A.indices.data.ptr, b.data.ptr, tol, reorder,
          x.data.ptr, singularity.ctypes.data)
  
      # The return type of SciPy is always float64. Therefore, x must be casted.
      x = x.astype(numpy.float64)
  
      return x
  
new_contents: import numpy
  
  import cupy
  from cupy import cuda
  from cupy.cuda import device
  from cupy.linalg import util
  import cupy.sparse
  
  
  if cuda.cusolver_enabled:
      from cupy.cuda import cusolver
  
  
  def lschol(A, b):
      """Solves linear system with cholesky decomposition.
  
      Find the solution to a large, sparse, linear system of equations.
      The function solves ``Ax = b``. Given two-dimensional matrix ``A`` is
      decomposed into ``L * L^*``.
  
      Args:
          A (cupy.ndarray or cupy.sparse.csr_matrix): The input matrix with
              dimension ``(N, N)``
          b (cupy.ndarray): Right-hand side vector.
  
      Returns:
          ret (cupy.ndarray): The solution vector ``x``.
  
      """
  
      if not cuda.cusolver_enabled:
          raise RuntimeError('Current cupy only supports cusolver in CUDA 8.0')
  
      if not cupy.sparse.isspmatrix_csr(A):
          A = cupy.sparse.csr_matrix(A)
      util._assert_nd_squareness(A)
      util._assert_cupy_array(b)
      m = A.shape[0]
      if b.ndim != 1 or len(b) != m:
          raise ValueError('b must be 1-d array whose size is same as A')
  
      # Cast to float32 or float64
      if A.dtype == 'f' or A.dtype == 'd':
          dtype = A.dtype
      else:
          dtype = numpy.find_common_type((A.dtype, 'f'), ())
  
      handle = device.get_cusolver_sp_handle()
      nnz = A.nnz
      tol = 1.0
      reorder = 1
      x = cupy.empty(m, dtype=dtype)
      singularity = numpy.empty(1, numpy.int32)
  
      if dtype == 'f':
          csrlsvchol = cusolver.scsrlsvchol
      else:
          csrlsvchol = cusolver.dcsrlsvchol
      csrlsvchol(
          handle, m, nnz, A._descr.descriptor, A.data.data.ptr,
          A.indptr.data.ptr, A.indices.data.ptr, b.data.ptr, tol, reorder,
          x.data.ptr, singularity.ctypes.data)
  
      # The return type of SciPy is always float64. Maybe we can follow that rule.
      x = x.astype(numpy.float64)
  
      return x
  
subject: modify return type comment