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 attr
  
  
  @testing.parameterize(*testing.product_dict(
      [
          {'shape': (3, 4), 'axis': 0},
          {'shape': (3, 4), 'axis': 1},
          {'shape': (3, 4), 'axis': 2},
          {'shape': (3, 4), 'axis': -1},
          {'shape': (), 'axis': 0},
          {'shape': (), 'axis': -1},
      ],
      [
          {'dtype': numpy.float16},
          {'dtype': numpy.float32},
          {'dtype': numpy.float64},
      ]
  ))
  class TestStack(unittest.TestCase):
  
      def setUp(self):
          self.xs = [numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)]
  
      def check_forward(self, xs_data):
          xs = [chainer.Variable(x) for x in xs_data]
          y = functions.stack(xs, axis=self.axis)
  
          if hasattr(numpy, 'stack'):
              # run test only with numpy>=1.10
              expect = numpy.stack(self.xs, axis=self.axis)
              gradient_check.assert_allclose(y.data, expect)
  
      def test_forward_cpu(self):
          self.check_forward(self.xs)
  
      @attr.gpu
      def test_forward_gpu(self):
          self.check_forward([cuda.to_gpu(x) for x in self.xs])
  
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
  
  
  @testing.parameterize(*testing.product_dict(
      [
          {'shape': (3, 4), 'axis': 0},
          {'shape': (3, 4), 'axis': 1},
          {'shape': (3, 4), 'axis': 2},
          {'shape': (3, 4), 'axis': -1},
          {'shape': (), 'axis': 0},
          {'shape': (), 'axis': -1},
      ],
      [
          {'dtype': numpy.float16},
          {'dtype': numpy.float32},
          {'dtype': numpy.float64},
      ]
  ))
  class TestStack(unittest.TestCase):
  
      def setUp(self):
          self.xs = [numpy.random.uniform(-1, 1, self.shape).astype(self.dtype)]
  
      def check_forward(self, xs_data):
          xs = [chainer.Variable(x) for x in xs_data]
          y = functions.stack(xs, axis=self.axis)
  
          if hasattr(numpy, 'stack'):
              # run test only with numpy>=1.10
              expect = numpy.stack(self.xs, axis=self.axis)
              gradient_check.assert_allclose(y.data, expect)
  
      def test_forward_cpu(self):
          self.check_forward(self.xs)
  
      @attr.gpu
      def test_forward_gpu(self):
          self.check_forward([cuda.to_gpu(x) for x in self.xs])
  
  
  testing.run_module(__name__, __file__)
  
subject: Add run_module