text: 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': (2, 7, 3), 'axis': 1,
           'slices': [[slice(None), slice(None, 2)], [slice(None), slice(2, 5)],
                      [slice(None), slice(5, None)]]},
          {'shape': (7, 3), 'axis': 0,
           'slices': [slice(None, 2), slice(2, 5), slice(5, None)]},
          {'shape': (2,), 'axis': 0, 'slices': [slice(None, 1), slice(1, None)]},
          {'shape': (2,), 'axis': 0, 'slices': [()]},
          {'shape': (2, 7, 3), 'axis': 1,
           'slices': [[slice(None), slice(None, 2)], [slice(None), slice(2, 5)],
                      [slice(None), slice(5, None)]]},
          {'shape': (2, 7, 3), 'axis': 1,
           'slices': [[slice(None), slice(None, 2)], [slice(None), slice(2, 5)],
                      [slice(None), slice(5, None)]]},
      ],
      [
          {'dtype': numpy.float16},
          {'dtype': numpy.float32},
          {'dtype': numpy.float64},
      ],
  ))
  class TestConcat(unittest.TestCase):
  
      def setUp(self):
          self.y = numpy.arange(
              numpy.prod(self.shape), dtype=self.dtype).reshape(self.shape)
          self.xs = [self.y[s] for s in self.slices]
  
      def check_forward(self, xs_data, y_data, axis):
          xs = tuple(chainer.Variable(x_data) for x_data in xs_data)
          y = functions.concat(xs, axis=axis)
          self.assertEqual(y.data.dtype, self.dtype)
          gradient_check.assert_allclose(y_data, y.data, atol=0, rtol=0)
          self.assertIsInstance(y.data.shape, tuple)
  
      def test_forward_cpu(self):
          self.check_forward(self.xs, self.y, axis=self.axis)
  
      @attr.gpu
      def test_forward_gpu(self):
          self.check_forward(
              [cuda.to_gpu(x.copy()) for x in self.xs],
              cuda.to_gpu(self.y), axis=self.axis)
  
      def check_backward(self, xs_data, axis):
          xs = tuple(chainer.Variable(x_data) for x_data in xs_data)
          y = functions.concat(xs, axis=axis)
          y.grad = y.data
          y.backward()
  
          for x in xs:
              gradient_check.assert_allclose(x.data, x.grad, atol=0, rtol=0)
  
      def test_backward_cpu(self):
          self.check_backward(self.xs, axis=self.axis)
  
      @attr.gpu
      def test_backward_gpu(self):
          self.check_backward([cuda.to_gpu(x.copy()) for x in self.xs],
                              axis=self.axis)
  
  
  testing.run_module(__name__, __file__)
  