old_contents: import unittest
  
  import numpy
  
  import chainer
  from chainer import cuda
  from chainer import functions
  from chainer import testing
  from chainer.testing import attr
  
  
  # TODO(Kenta OONO): This test fixture check types only. Add more detailed test.
  @testing.parameterize(*testing.product({
      'dtype': [numpy.float16, numpy.float32, numpy.float64],
  }))
  class TestDropout(unittest.TestCase):
  
      def setUp(self):
          self.x = numpy.random.uniform(-1, 1, (2, 3)).astype(self.dtype)
          self.gy = numpy.random.uniform(-1, 1, (2, 3)).astype(self.dtype)
  
      def check_type_forward(self, x_data):
          x = chainer.Variable(x_data)
          y = functions.dropout(x)
          self.assertEqual(y.data.dtype, self.dtype)
  
      def test_type_forward_cpu(self):
          self.check_type_forward(self.x)
  
      @attr.gpu
      def test_type_forward_gpu(self):
          self.check_type_forward(cuda.to_gpu(self.x))
  
  
  testing.run_module(__name__, __file__)
  
new_contents: import unittest
  
  import numpy
  
  import chainer
  from chainer import cuda
  from chainer import functions
  from chainer import gradient_check
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import condition
  
  
  def _dropout(x, creator):
      return x * creator.mask
  
  
  @testing.parameterize(
      {'dtype': numpy.float16, 'ratio': 0.1},
      {'dtype': numpy.float32, 'ratio': 0.3},
      {'dtype': numpy.float64, 'ratio': 0.5},
      {'dtype': numpy.float64, 'ratio': 0.0},
  )
  class TestDropout(unittest.TestCase):
  
      def setUp(self):
          self.x = numpy.random.uniform(-1, 1, (2, 3)).astype(self.dtype)
          self.gy = numpy.random.uniform(-1, 1, (2, 3)).astype(self.dtype)
  
          self.check_backward_options = {}
          if self.dtype == numpy.float16:
              self.check_backward_options = {
                  'atol': 5e-4, 'rtol': 5e-3}
  
      def check_type_forward(self, x_data):
          x = chainer.Variable(x_data)
          y = functions.dropout(x)
          self.assertEqual(y.data.dtype, self.dtype)
  
      def check_forward(self, x_data):
          x = chainer.Variable(x_data)
          y = functions.dropout(x, self.ratio)
          if self.ratio == 0.0:
              y_expect = x_data
          else:
              y_expect = _dropout(x_data, y.creator)
          testing.assert_allclose(y_expect, y.data)
  
      def check_backward(self, x_data, y_grad):
          x = chainer.Variable(x_data)
          y = functions.dropout(x, self.ratio)
          d = {'creator': y.creator}
          y.grad = y_grad
          y.backward()
  
          def f():
              creator = d['creator']
              y = _dropout(x_data, creator)
              return y,
          gx, = gradient_check.numerical_grad(f, (x_data, ), (y.grad, ), eps=0.1)
          testing.assert_allclose(gx, x.grad, **self.check_backward_options)
  
      def test_type_forward_cpu(self):
          self.check_type_forward(self.x)
  
      @attr.gpu
      def test_type_forward_gpu(self):
          self.check_type_forward(cuda.to_gpu(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))
  
      @condition.retry(3)
      def test_backward_cpu(self):
          self.check_backward(self.x, self.gy)
  
      @attr.gpu
      @condition.retry(3)
      def test_backward_gpu(self):
          self.check_backward(cuda.to_gpu(self.x),
                              cuda.to_gpu(self.gy))
  
  
  testing.run_module(__name__, __file__)
  
subject: add forward and backward test to dropout