old_contents: import unittest
  
  import numpy
  
  from cupy.cuda import cusolver_enabled
  from cupy import testing
  
  
  @unittest.skipUnless(
      cusolver_enabled, 'Only cusolver in CUDA 8.0 is supported')
  @testing.gpu
  class TestCholeskyDecomposition(unittest.TestCase):
  
      _multiprocess_can_split_ = True
  
      @testing.for_float_dtypes(no_float16=True)
      @testing.numpy_cupy_allclose(atol=1e-5)
      def check_L_float(self, array, xp, dtype):
          a = xp.asarray(array, dtype=dtype)
          return xp.linalg.cholesky(a)
  
      def test_float_dtypes(self):
          # A normal positive definite matrix
          A = numpy.random.randn(5, 5)
          A = numpy.dot(A, A.transpose())
          self.check_L_float(A)
          # np.linalg.cholesky only uses a lower triangle of an array
          self.check_L_float(numpy.array([[1, 2], [1, 9]]))
  
      @testing.for_dtypes([numpy.int32, numpy.int64, numpy.uint32, numpy.uint64])
      @testing.numpy_cupy_allclose(atol=1e-4)
      def check_L_int(self, array, xp, dtype):
          a = xp.asarray(array, dtype=dtype)
          return xp.linalg.cholesky(a)
  
      def test_int_dtypes(self):
          A = numpy.random.randint(0, 100, size=(5, 5))
          A = numpy.dot(A, A.transpose())
          self.check_L_int(A)
          self.check_L_int(numpy.array([[1, 2], [1, 9]]))
  
new_contents: import unittest
  
  import numpy
  
  from cupy.cuda import cusolver_enabled
  from cupy import testing
  
  
  @unittest.skipUnless(
      cusolver_enabled, 'Only cusolver in CUDA 8.0 is supported')
  @testing.gpu
  class TestCholeskyDecomposition(unittest.TestCase):
  
      _multiprocess_can_split_ = True
  
      @testing.for_dtypes([
          numpy.int32, numpy.int64, numpy.uint32, numpy.uint64,
          numpy.float32, numpy.float64])
      @testing.numpy_cupy_allclose(atol=1e-4)
      def check_L(self, array, xp, dtype):
          a = xp.asarray(array, dtype=dtype)
          return xp.linalg.cholesky(a)
  
      def test_decomposition(self):
          # A normal positive definite matrix
          A = numpy.random.randint(0, 100, size=(5, 5))
          A = numpy.dot(A, A.transpose())
          self.check_L(A)
          # np.linalg.cholesky only uses a lower triangle of an array
          self.check_L(numpy.array([[1, 2], [1, 9]]))
  
subject: Simplify tests