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
  from chainer.testing import condition
  
  
  class TestMeanAbsoluteError(unittest.TestCase):
  
      def setUp(self):
          self.x0 = numpy.random.uniform(-1, 1, (4, 3)).astype(numpy.float32)
          self.x1 = numpy.random.uniform(-1, 1, (4, 3)).astype(numpy.float32)
  
      def check_forward(self, x0_data, x1_data):
          x0 = chainer.Variable(x0_data)
          x1 = chainer.Variable(x1_data)
          loss = functions.mean_absolute_error(x0, x1)
          loss_value = cuda.to_cpu(loss.data)
          self.assertEqual(loss_value.dtype, numpy.float32)
          self.assertEqual(loss_value.shape, ())
  
          # Compute expected value
          loss_expect = 0.
          for i in numpy.ndindex(self.x0.shape):
              loss_expect += abs(self.x0[i] - self.x1[i])
          loss_expect /= self.x0.size
  
          self.assertAlmostEqual(loss_expect, loss_value, places=5)
  
      @condition.retry(3)
      def test_forward_cpu(self):
          self.check_forward(self.x0, self.x1)
  
      @attr.gpu
      @condition.retry(3)
      def test_forward_gpu(self):
          self.check_forward(cuda.to_gpu(self.x0), cuda.to_gpu(self.x1))
  
      def check_backward(self, x0_data, x1_data):
          gradient_check.check_backward(
              functions.MeanAbsoluteError(),
              (x0_data, x1_data), None, eps=1e-2)
  
      @condition.retry(3)
      def test_backward_cpu(self):
          self.check_backward(self.x0, self.x1)
  
      @attr.gpu
      @condition.retry(3)
      def test_backward_gpu(self):
          self.check_backward(cuda.to_gpu(self.x0), cuda.to_gpu(self.x1))
  
  
  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
  from chainer.testing import condition
  
  
  class TestMeanAbsoluteError(unittest.TestCase):
  
      def setUp(self):
          self.x0 = numpy.random.uniform(-1, 1, (4, 3)).astype(numpy.float32)
          # Add sufficient margin to prevent computational error
          diff = numpy.random.uniform(-1, 1, (4, 3)).astype(numpy.float32)
          diff[abs(diff) < 0.01] = 0.5
          self.x1 = self.x0 + diff
  
      def check_forward(self, x0_data, x1_data):
          x0 = chainer.Variable(x0_data)
          x1 = chainer.Variable(x1_data)
          loss = functions.mean_absolute_error(x0, x1)
          loss_value = cuda.to_cpu(loss.data)
          self.assertEqual(loss_value.dtype, numpy.float32)
          self.assertEqual(loss_value.shape, ())
  
          # Compute expected value
          loss_expect = 0.
          for i in numpy.ndindex(self.x0.shape):
              loss_expect += abs(self.x0[i] - self.x1[i])
          loss_expect /= self.x0.size
  
          self.assertAlmostEqual(loss_expect, loss_value, places=5)
  
      @condition.retry(3)
      def test_forward_cpu(self):
          self.check_forward(self.x0, self.x1)
  
      @attr.gpu
      @condition.retry(3)
      def test_forward_gpu(self):
          self.check_forward(cuda.to_gpu(self.x0), cuda.to_gpu(self.x1))
  
      def check_backward(self, x0_data, x1_data):
          gradient_check.check_backward(
              functions.MeanAbsoluteError(),
              (x0_data, x1_data), None, eps=1e-2)
  
      @condition.retry(3)
      def test_backward_cpu(self):
          self.check_backward(self.x0, self.x1)
  
      @attr.gpu
      @condition.retry(3)
      def test_backward_gpu(self):
          self.check_backward(cuda.to_gpu(self.x0), cuda.to_gpu(self.x1))
  
  
  testing.run_module(__name__, __file__)
  
subject: Change random value to prevent test error