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),
              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)
  
          y_data = cuda.to_cpu(y.data)
          self.assertEqual(y_data.shape[self.axis], 2)
          numpy.testing.assert_array_equal(y_data.take(0, axis=self.axis), self.xs[0])
          numpy.testing.assert_array_equal(y_data.take(1, axis=self.axis), self.xs[1])
  
      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__)
  
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, 'y_shape': (2, 3, 4)},
          {'shape': (3, 4), 'axis': 1, 'y_shape': (3, 2, 4)},
          {'shape': (3, 4), 'axis': 2, 'y_shape': (3, 4, 2)},
          # TODO(unno): concat function doesn't support negative axes now.
          # {'shape': (3, 4), 'axis': -1, 'y_shape': (3, 4, 2)},
          {'shape': (), 'axis': 0, 'y_shape': (2,)},
          # {'shape': (), 'axis': -1, 'y_shape': (2,)},
      ],
      [
          {'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),
              numpy.random.uniform(-1, 1, self.shape).astype(self.dtype),
          ]
          self.g = numpy.random.uniform(-1, 1, self.y_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)
  
          y_data = cuda.to_cpu(y.data)
          self.assertEqual(y_data.shape[self.axis], 2)
          numpy.testing.assert_array_equal(y_data.take(0, axis=self.axis), self.xs[0])
          numpy.testing.assert_array_equal(y_data.take(1, axis=self.axis), self.xs[1])
  
      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])
  
      def check_backward(self, xs_data, g_data):
          def func(*xs):
              return functions.stack(xs, self.axis)
  
          gradient_check.check_backward(
              func, xs_data, g_data, eps=2.0 ** -2, atol=1e-3, rtol=1e-3)
  
      def test_backward_cpu(self):
          self.check_backward(self.xs, self.g)
  
      @attr.gpu
      def test_backward_gpu(self):
          self.check_backward([cuda.to_gpu(x) for x in self.xs], cuda.to_gpu(self.g))
  
  
  testing.run_module(__name__, __file__)
  
subject: Add test for backward