old_contents: import unittest
  
  import numpy
  
  import chainer
  from chainer.backends import cuda
  from chainer import functions
  from chainer import gradient_check
  from chainer import testing
  from chainer.testing import attr
  
  
  @testing.parameterize(*testing.product({
      'shape': [(3,), (3, 4)],
      'dtype': [numpy.float16, numpy.float32, numpy.float64],
  }))
  class TestFlipUD(unittest.TestCase):
  
      def setUp(self):
          self.x = numpy.random.uniform(0, 1, self.shape).astype(self.dtype)
          self.gy = numpy.random.uniform(0, 1, self.shape).astype(self.dtype)
          self.ggx = numpy.random.uniform(0, 1, self.shape).astype(self.dtype)
  
      def check_forward(self, x_data):
          x = chainer.Variable(x_data)
          y = functions.flipud(x)
  
          testing.assert_allclose(y.data, numpy.flipud(self.x))
  
      def test_forward_cpu(self):
          self.check_forward(self.x)
  
      @attr.gpu
      def test_forward_gpu(self):
          self.check_forward(cuda.to_gpu(self.x))
  
      def check_backward(self, x_data, y_grad):
          gradient_check.check_backward(
              functions.flipud, x_data, y_grad, dtype=numpy.float64)
  
      def test_backward_cpu(self):
          self.check_backward(self.x, self.gy)
  
      @attr.gpu
      def test_backward_gpu(self):
          self.check_backward(cuda.to_gpu(self.x), cuda.to_gpu(self.gy))
  
      def check_double_backward(self, x_data, y_grad, x_grad_grad):
          gradient_check.check_double_backward(
              functions.flipud, x_data, y_grad, x_grad_grad, dtype=numpy.float64,
              atol=5e-4, rtol=5e-3)
  
      def test_double_backward_cpu(self):
          self.check_double_backward(self.x, self.gy, self.ggx)
  
      @attr.gpu
      def test_double_backward_gpu(self):
          self.check_double_backward(cuda.to_gpu(self.x), cuda.to_gpu(self.gy),
                                     cuda.to_gpu(self.ggx))
  
  
  testing.run_module(__name__, __file__)
  
new_contents: import numpy
  
  from chainer import functions
  from chainer import testing
  
  
  @testing.parameterize(*testing.product({
      'shape': [(3,), (3, 4)],
      'dtype': [numpy.float16, numpy.float32, numpy.float64],
  }))
  @testing.inject_backend_tests(
      None,
      # CPU tests
      [
          {},
      ]
      # GPU tests
      + testing.product({
          'use_cuda': [True],
          'use_cudnn': ['never', 'always'],
          'cuda_device': [0, 1],
      })
      # ChainerX tests
      + testing.product({
          'use_chainerx': [True],
          'chainerx_device': ['native:0', 'cuda:0', 'cuda:1'],
      })
  )
  class TestFlipUD(testing.FunctionTestCase):
  
      def generate_inputs(self):
          x = numpy.random.uniform(0, 1, self.shape).astype(self.dtype)
          return x,
  
      def forward_expected(self, inputs):
          x, = inputs
          y = numpy.flipud(x)
          return y,
  
      def forward(self, inputs, device):
          x, = inputs
          return functions.flipud(x),
  
  
  testing.run_module(__name__, __file__)
  
subject: Simplify `F.flipud` test