old_contents: import numpy
  
  from chainer import functions
  from chainer import testing
  
  
  @testing.parameterize(*(testing.product({
      'ch_out': [1, 5],
      'size': [10, 20],
      'dtype': [numpy.float32, numpy.float16],
      'eps': [1e-5, 1e-1],
  })))
  @testing.backend.inject_backend_tests(
      None,
      # CPU tests
      testing.product({
          'use_cuda': [False],
          'use_ideep': ['never', 'always'],
      })
      # GPU tests
      + testing.product({
          'use_cuda': [True],
          'use_cudnn': ['never', 'always'],
      })
      # ChainerX tests
      + testing.product({
          'use_chainerx': [True],
          'chainerx_device': ['native:0', 'cuda:0'],
      })
  )
  class TestStandardize(testing.FunctionTestCase):
  
      def setUp(self):
          if self.dtype == numpy.float16:
              self.check_forward_options.update({'atol': 5e-3, 'rtol': 1e-2})
              self.check_backward_options.update({'atol': 5e-3, 'rtol': 1e-2})
              self.check_double_backward_options.update(
                  {'atol': 5e-3, 'rtol': 1e-2})
  
      def generate_inputs(self):
          shape = self.ch_out, self.size
          x = numpy.random.uniform(-1, 1, shape).astype(self.dtype)
          return x,
  
      def forward(self, inputs, device):
          x, = inputs
          return functions.standardize(x, self.eps),
  
      def forward_expected(self, inputs):
          x, = inputs
          mean = numpy.mean(x, axis=1, keepdims=True)
          var = numpy.mean(numpy.square(x - mean), axis=1, keepdims=True)
          std = numpy.sqrt(var + self.eps, dtype=self.dtype)
          return (x - mean) / std,
  
  
  testing.run_module(__name__, __file__)
  
new_contents: import numpy
  
  from chainer import functions
  from chainer import testing
  
  
  @testing.parameterize(*(testing.product({
      'ch_out': [1, 5],
      'size': [10, 20],
      'dtype': [numpy.float32, numpy.float16],
      'eps': [1e-5, 1e-1],
  })))
  @testing.backend.inject_backend_tests(
      None,
      # CPU tests
      testing.product({
          'use_cuda': [False],
          'use_ideep': ['never', 'always'],
      })
      # GPU tests
      + testing.product({
          'use_cuda': [True],
          'use_cudnn': ['never', 'always'],
      })
      # ChainerX tests
      + testing.product({
          'use_chainerx': [True],
          'chainerx_device': ['native:0', 'cuda:0'],
      })
  )
  class TestStandardize(testing.FunctionTestCase):
  
      def setUp(self):
          if self.dtype == numpy.float16:
              self.check_forward_options.update({'atol': 5e-3, 'rtol': 1e-2})
              self.check_backward_options.update({'atol': 5e-3, 'rtol': 1e-2})
              self.check_double_backward_options.update(
                  {'atol': 5e-3, 'rtol': 1e-2})
  
      def generate_inputs(self):
          shape = self.ch_out, self.size
          x = numpy.random.uniform(-1, 1, shape).astype(self.dtype)
          return x,
  
      def forward(self, inputs, device):
          x, = inputs
          return functions.standardize(x, self.eps),
  
      def forward_expected(self, inputs):
          x, = inputs
          mean = numpy.mean(x, axis=1, keepdims=True)
          var = numpy.mean(numpy.square(x - mean), axis=1, keepdims=True)
          std = numpy.sqrt(var + self.eps, dtype=self.dtype)
          inv_std = 1. / std
          return (x - mean) * inv_std,
  
  
  testing.run_module(__name__, __file__)
  
subject: fix forward_expected to calc inverse of std