old_contents: import unittest
  
  import numpy
  
  import chainer
  from chainer.backends import cuda
  import chainer.functions as F
  from chainer import gradient_check
  from chainer import testing
  from chainer.testing import attr
  
  
  class UnaryFunctionsTestBase(unittest.TestCase):
  
      def make_data(self):
          raise NotImplementedError
  
      def setUp(self):
          self.eps = 1e-3
          while True:
              self.x, self.gy, self.ggy = self.make_data()
              if (numpy.abs(self.x - numpy.round(self.x)) > self.eps * 10).all():
                  break
  
      def check_forward(self, op, op_xp, x_data):
          x = chainer.Variable(x_data)
          y = op(x)
          self.assertEqual(x.data.dtype, y.data.dtype)
          v = op_xp(x_data)
          testing.assert_allclose(
              v, y.data, atol=1e-7, rtol=1e-7)
  
      def check_forward_cpu(self, op, op_xp):
          self.check_forward(op, op_xp, self.x)
  
      def check_forward_gpu(self, op, op_xp):
          self.check_forward(op, op_xp, cuda.to_gpu(self.x))
  
      def check_backward(self, op, x_data, y_grad):
          gradient_check.check_backward(op, x_data, y_grad, atol=5e-4,
                                        rtol=5e-3, dtype=numpy.float64,
                                        eps=self.eps)
  
      def check_backward_cpu(self, op):
          self.check_backward(op, self.x, self.gy)
  
      def check_backward_gpu(self, op):
          self.check_backward(op, cuda.to_gpu(self.x), cuda.to_gpu(self.gy))
  
      def check_double_backward(self, op, x_data, y_grad, y_grad_grad):
          gradient_check.check_double_backward(
              op, x_data, y_grad, y_grad_grad, dtype=numpy.float64,
              atol=1e-7, rtol=1e-7)
  
      def check_double_backward_cpu(self, op):
          self.check_double_backward(op, self.x, self.gy, self.ggy)
  
      def check_double_backward_gpu(self, op):
          self.check_double_backward(op, cuda.to_gpu(
              self.x), cuda.to_gpu(self.gy), cuda.to_gpu(self.ggy))
  
      def check_label(self, op, expected):
          self.assertEqual(op().label, expected)
  
  
  @testing.parameterize(*testing.product({
      'shape': [(3, 2), ()],
      'dtype': [numpy.float16, numpy.float32, numpy.float64],
  }))
  class TestFloor(UnaryFunctionsTestBase):
  
      def make_data(self):
          x = numpy.random.uniform(-10.0, 10.0, self.shape).astype(self.dtype)
          gy = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
          ggy = numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)
          return x, gy, ggy
  
      def test_forward_cpu(self):
          self.check_forward_cpu(F.floor, numpy.floor)
  
      @attr.gpu
      def test_forward_gpu(self):
          self.check_forward_gpu(F.floor, cuda.cupy.floor)
  
  
  testing.run_module(__name__, __file__)
  
new_contents: import numpy
  
  from chainer import functions
  from chainer import testing
  
  
  @testing.parameterize(*testing.product({
      'shape': [(3, 2), ()],
      'dtype': [numpy.float16, numpy.float32, numpy.float64],
  }))
  @testing.fix_random()
  @testing.inject_backend_tests(
      None,
      # CPU tests
      [
          {},
      ]
      # GPU tests
      + testing.product({
          'use_cuda': [True],
          'cuda_device': [0, 1],
      })
      # ChainerX tests
      + testing.product({
          'use_chainerx': [True],
          'chainerx_device': ['native:0', 'cuda:0', 'cuda:1'],
      })
  )
  class TestFloor(testing.FunctionTestCase):
  
      skip_backward_test = True
      skip_double_backward_test = True
  
      def generate_inputs(self):
          while True:
              x = numpy.random.uniform(
                  -10.0, 10.0, self.shape).astype(self.dtype)
              if (numpy.abs(x - numpy.round(x)) > 1e-2).all():
                  return x,
  
      def forward(self, inputs, device):
          x, = inputs
          y = functions.floor(x)
          return y,
  
      def forward_expected(self, inputs):
          x, = inputs
          expected = numpy.floor(x)
          expected = numpy.asarray(expected)
          return expected,
  
  
  testing.run_module(__name__, __file__)
  
subject: Simplify F.floor test