old_contents: import unittest
  
  import numpy
  
  import chainer
  from chainer import cuda
  from chainer import functions
  from chainer import gradient_check
  from chainer.testing import attr
  from chainer.testing import condition
  
  
  class TestHardSigmoid(unittest.TestCase):
  
      shape = (3, 4)
  
      def setUp(self):
          self.x = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
          self.g = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
  
      def check_forward(self, x_data):
          x = chainer.Variable(x_data)
          y = functions.hard_sigmoid(x)
          expect = numpy.minimum(1.0, numpy.maximum(0.0, self.x * 0.2 + 0.5))
          gradient_check.assert_allclose(y.data, expect)
  
      @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))
  
      def check_backward(self, x_data, grad):
          gradient_check.check_backward(
              functions.HardSigmoid(), x_data, grad)
  
      @condition.retry(3)
      def test_backward_cpu(self):
          self.check_backward(self.x, self.g)
  
      @condition.retry(3)
      def test_backward_gpu(self):
          self.check_backward(cuda.to_gpu(self.x),
                              cuda.to_gpu(self.g))
  
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
  
  
  class TestHardSigmoid(unittest.TestCase):
  
      shape = (3, 4)
  
      def setUp(self):
          self.x = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
          self.g = numpy.random.uniform(-1, 1, self.shape).astype(numpy.float32)
  
      def check_forward(self, x_data):
          x = chainer.Variable(x_data)
          y = functions.hard_sigmoid(x)
          expect = numpy.minimum(1.0, numpy.maximum(0.0, self.x * 0.2 + 0.5))
          gradient_check.assert_allclose(y.data, expect)
  
      @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))
  
      def check_backward(self, x_data, grad):
          gradient_check.check_backward(
              functions.HardSigmoid(), x_data, grad)
  
      @condition.retry(3)
      def test_backward_cpu(self):
          self.check_backward(self.x, self.g)
  
      @condition.retry(3)
      def test_backward_gpu(self):
          self.check_backward(cuda.to_gpu(self.x),
                              cuda.to_gpu(self.g))
  
  
  testing.run_module(__name__, __file__)
  
subject: Add run_module