old_contents: import unittest
  
  import chainer
  from chainer import testing
  import numpy as np
  
  from chainerrl.links import noisy_linear
  
  
  @testing.parameterize(*testing.product({
      'nobias': [False, True],
  }))
  class TestFactorizedNoisyLinear(unittest.TestCase):
      def test_calls(self):
          l_uninitialized_new = noisy_linear.FactorizedNoisyLinear(
              5, nobias=self.nobias)
          l_uninitialized_old = noisy_linear.FactorizedNoisyLinear(
              None, 5, nobias=self.nobias)
          l_initialized = noisy_linear.FactorizedNoisyLinear(
              6, 5, nobias=self.nobias)
          for l in [l_uninitialized_new, l_uninitialized_old, l_initialized]:
              x_data = np.arange(12).astype(np.float32).reshape((2, 6))
              x = chainer.Variable(x_data)
              l(x)
              l(x_data + 1)
              l(x_data.reshape((2, 3, 2)))
  
new_contents: import unittest
  
  import chainer
  from chainer import testing
  import numpy as np
  
  from chainerrl.links import noisy_linear
  
  
  @testing.parameterize(*testing.product({
      'size_args': [
          (5,),  # uninitialized from Chainer v2
          (None, 5),  # uninitialized
          (6, 5),  # initialized
      ],
      'nobias': [False, True],
  }))
  class TestFactorizedNoisyLinear(unittest.TestCase):
      def test_calls(self):
          l = noisy_linear.FactorizedNoisyLinear(
              *self.size_args, nobias=self.nobias)
          x_data = np.arange(12).astype(np.float32).reshape((2, 6))
          x = chainer.Variable(x_data)
          l(x)
          l(x_data + 1)
          l(x_data.reshape((2, 3, 2)))
  
subject: Use parametrized test