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)},
          {'shape': ()},
      ],
      [
          {'in_type': numpy.float16},
          {'in_type': numpy.float32},
          {'in_type': numpy.float64},
      ],
      [
          {'out_type': numpy.float16},
          {'out_type': numpy.float32},
          {'out_type': numpy.float64},
      ]
  ))
  class TestCast(unittest.TestCase):
  
      def setUp(self):
          self.x = numpy.random.uniform(-1, 1, self.shape).astype(self.in_type)
          self.g = numpy.random.uniform(-1, 1, self.shape).astype(self.out_type)
  
      def check_forward(self, x_data):
          x = chainer.Variable(x_data)
          y = functions.cast(x, self.out_type)
          self.assertEqual(y.data.shape, x.data.shape)
          self.assertEqual(y.data.dtype, self.out_type)
  
      def test_forward_cpu(self):
          self.check_forward(self.x)
  
      @attr.gpu
      def test_forward_gpu(self):
          self.check_forward(cuda.to_gpu(self.x))
  
      def check_backward(self, x_data, g_data):
          func = functions.Cast(self.out_type)
          gradient_check.check_backward(
              func, x_data, g_data, eps=2.0 ** 5, atol=1e-3, rtol=1e-3)
  
      def test_backward_cpu(self):
          self.check_backward(self.x, 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
  
  
  @testing.parameterize(*testing.product_dict(
      [
          {'shape': (3, 4)},
          {'shape': ()},
      ],
      [
          {'in_type': numpy.float16},
          {'in_type': numpy.float32},
          {'in_type': numpy.float64},
      ],
      [
          {'out_type': numpy.float16},
          {'out_type': numpy.float32},
          {'out_type': numpy.float64},
      ]
  ))
  class TestCast(unittest.TestCase):
  
      def setUp(self):
          self.x = numpy.random.uniform(-1, 1, self.shape).astype(self.in_type)
          self.g = numpy.random.uniform(-1, 1, self.shape).astype(self.out_type)
  
      def check_forward(self, x_data):
          x = chainer.Variable(x_data)
          y = functions.cast(x, self.out_type)
          self.assertEqual(y.data.shape, x.data.shape)
          self.assertEqual(y.data.dtype, self.out_type)
  
      def test_forward_cpu(self):
          self.check_forward(self.x)
  
      @attr.gpu
      def test_forward_gpu(self):
          self.check_forward(cuda.to_gpu(self.x))
  
      def check_backward(self, x_data, g_data):
          func = functions.Cast(self.out_type)
          gradient_check.check_backward(
              func, x_data, g_data, eps=2.0 ** -2, atol=1e-3, rtol=1e-3)
  
      def test_backward_cpu(self):
          self.check_backward(self.x, self.g)
  
subject: Fix test