old_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 condition
  
  
  if cuda.available:
      cuda.init()
  
  
  class TestSoftplus(unittest.TestCase):
  
      def setUp(self):
          self.x = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
          self.gy = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
          self.beta = numpy.random.uniform(1, 2, ())
  
      def check_forward(self, x_data):
          x = chainer.Variable(x_data)
          y = functions.softplus(x, beta=self.beta)
          x_value = cuda.to_cpu(x_data)
          y_exp = numpy.log(1 + numpy.exp(self.beta * x_value)) / self.beta
          gradient_check.assert_allclose(y_exp, y.data)
  
      def check_backward(self, x_data, y_grad):
          x = chainer.Variable(x_data)
          y = functions.softplus(x, beta=self.beta)
          self.assertEqual(y.data.dtype, numpy.float32)
          y.grad = y_grad
          y.backward()
  
          func = y.creator
          f = lambda: func.forward((x.data,))
          gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))
  
          gradient_check.assert_allclose(gx, x.grad)
  
      @condition.retry(3)
      def test_forward_cpu(self):
          self.check_forward(self.x)
  
      @condition.retry(3)
      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)
  
      @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__)
  
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
  
  
  if cuda.available:
      cuda.init()
  
  
  class TestSoftplus(unittest.TestCase):
  
      def setUp(self):
          self.x = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
          self.gy = numpy.random.uniform(-1, 1, (3, 2)).astype(numpy.float32)
          self.beta = numpy.random.uniform(1, 2, ())
  
      def check_forward(self, x_data):
          x = chainer.Variable(x_data)
          y = functions.softplus(x, beta=self.beta)
          x_value = cuda.to_cpu(x_data)
          y_exp = numpy.log(1 + numpy.exp(self.beta * x_value)) / self.beta
          gradient_check.assert_allclose(y_exp, y.data)
  
      def check_backward(self, x_data, y_grad):
          x = chainer.Variable(x_data)
          y = functions.softplus(x, beta=self.beta)
          self.assertEqual(y.data.dtype, numpy.float32)
          y.grad = y_grad
          y.backward()
  
          func = y.creator
          f = lambda: func.forward((x.data,))
          gx, = gradient_check.numerical_grad(f, (x.data,), (y.grad,))
  
          gradient_check.assert_allclose(gx, x.grad)
  
      @condition.retry(3)
      def test_forward_cpu(self):
          self.check_forward(self.x)
  
      @attr.gpu
      @condition.retry(3)
      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 attr for gpu test