old_contents: import numpy as np
  import unittest
  
  import chainer
  from chainer import cuda
  from chainer import testing
  from chainer.testing import attr
  
  from chainercv.links.model.ssd import multibox_loss
  
  
  @testing.parameterize(*testing.product({
      'k': [3, 1000],
      'batchsize': [1, 2],
      'n_bbox': [5, 10],
      'n_class': [3, 5],
  }))
  class TestMultiboxLoss(unittest.TestCase):
  
      def setUp(self):
          self.x_loc = np.random.uniform(
              -1, 1, size=(self.batchsize, self.n_bbox, 4)) \
              .astype(np.float32)
          self.x_conf = np.random.uniform(
              -1, 1, size=(self.batchsize, self.n_bbox, self.n_class)) \
              .astype(np.float32)
  
          self.t_loc = np.random.uniform(
              -1, 1, size=(self.batchsize, self.n_bbox, 4)) \
              .astype(np.float32)
          self.t_conf = np.random.randint(
              self.n_class, size=(self.batchsize, self.n_bbox)) \
              .astype(np.int32)
  
      def _check_forward(self, x_loc, x_conf, t_loc, t_conf):
          loss_loc, loss_conf = multibox_loss(
              x_loc, x_conf, t_loc, t_conf, self.k)
  
          self.assertIsInstance(loss_loc, chainer.Variable)
          self.assertIsInstance(loss_loc.data, type(x_loc))
          self.assertEqual(loss_loc.shape, ())
          self.assertEqual(loss_loc.dtype, x_loc.dtype)
  
          self.assertIsInstance(loss_conf, chainer.Variable)
          self.assertIsInstance(loss_conf.data, type(x_conf))
          self.assertEqual(loss_conf.shape, ())
          self.assertEqual(loss_conf.dtype, x_conf.dtype)
  
      def test_forward_cpu(self):
          self._check_forward(
              self.x_loc, self.x_conf, self.t_loc, self.t_conf)
  
      @attr.gpu
      def test_forward_gpu(self):
          self._check_forward(
              cuda.to_gpu(self.x_loc), cuda.to_gpu(self.x_conf),
              cuda.to_gpu(self.t_loc), cuda.to_gpu(self.t_conf))
  
  
  testing.run_module(__name__, __file__)
  
new_contents: import numpy as np
  import unittest
  
  import chainer
  from chainer import testing
  from chainer.testing import attr
  
  from chainercv.links.model.ssd import multibox_loss
  
  
  @testing.parameterize(*testing.product({
      'k': [3, 1000],
      'batchsize': [1, 2],
      'n_bbox': [5, 10],
      'n_class': [3, 5],
  }))
  class TestMultiboxLoss(unittest.TestCase):
  
      def setUp(self):
          self.x_loc = chainer.Variable(np.random.uniform(
              -1, 1, size=(self.batchsize, self.n_bbox, 4))
              .astype(np.float32))
          self.x_conf = chainer.Variable(np.random.uniform(
              -1, 1, size=(self.batchsize, self.n_bbox, self.n_class))
              .astype(np.float32))
  
          self.t_loc = chainer.Variable(np.random.uniform(
              -1, 1, size=(self.batchsize, self.n_bbox, 4))
              .astype(np.float32))
          self.t_conf = chainer.Variable(np.random.randint(
              self.n_class, size=(self.batchsize, self.n_bbox))
              .astype(np.int32))
  
      def _check_forward(self, x_loc, x_conf, t_loc, t_conf):
          loss_loc, loss_conf = multibox_loss(
              x_loc, x_conf, t_loc, t_conf, self.k)
  
          self.assertIsInstance(loss_loc, chainer.Variable)
          self.assertIsInstance(loss_loc.data, type(x_loc.data))
          self.assertEqual(loss_loc.shape, ())
          self.assertEqual(loss_loc.dtype, x_loc.dtype)
  
          self.assertIsInstance(loss_conf, chainer.Variable)
          self.assertIsInstance(loss_conf.data, type(x_conf.data))
          self.assertEqual(loss_conf.shape, ())
          self.assertEqual(loss_conf.dtype, x_conf.dtype)
  
      def test_forward_cpu(self):
          self._check_forward(
              self.x_loc, self.x_conf, self.t_loc, self.t_conf)
  
      @attr.gpu
      def test_forward_gpu(self):
          self.x_loc.to_gpu()
          self.x_conf.to_gpu()
          self.t_loc.to_gpu()
          self.t_conf.to_gpu()
          self._check_forward(
              self.x_loc, self.x_conf, self.t_loc, self.t_conf)
  
  
  testing.run_module(__name__, __file__)
  
subject: update test