code: def unary_math_function_unittest(
      func,
      func_expected=None,
      label_expected=None,
      make_data=None,
      is_linear=None,
      forward_options=None,
      backward_options=None,
      double_backward_options=None,
  ):
      """Decorator for testing unary mathematical Chainer functions.
  
      This decorator makes test classes test unary mathematical Chainer
      functions. Tested are forward and backward, including double backward,
      computations on CPU and GPU across parameterized ``shape`` and ``dtype``.
  
      Args:
          func(function or ~chainer.Function): Chainer function to be tested by
              the decorated test class. Taking :class:`~chainer.Function` is for
              backward compatibility.
          func_expected: Function used to provide expected values for
              testing forward computation. If not given, a corresponsing numpy
              function for ``func`` is implicitly picked up by its name.
          label_expected(string): String used to test labels of Chainer
              functions. If not given, the name of ``func`` is implicitly used.
          make_data: Function to customize input and gradient data used
              in the tests. It takes ``shape`` and ``dtype`` as its arguments,
              and returns a tuple of input, gradient and double gradient data. By
              default, uniform destribution ranged ``[-1, 1]`` is used for all of
              them.
          is_linear: Tells the decorator that ``func`` is a linear function
              so that it wraps ``func`` as a non-linear function to perform
              double backward test. This argument is left for backward
              compatibility. Linear functions can be tested by default without
              specifying ``is_linear`` in Chainer v5 or later.
          forward_options(dict): Options to be specified as an argument of
              :func:`chainer.testing.assert_allclose` function.
              If not given, preset tolerance values are automatically selected.
          backward_options(dict): Options to be specified as an argument of
              :func:`chainer.gradient_check.check_backward` function.
              If not given, preset tolerance values are automatically selected
              depending on ``dtype``.
          double_backward_options(dict): Options to be specified as an argument
              of :func:`chainer.gradient_check.check_double_backward` function.
              If not given, preset tolerance values are automatically selected
              depending on ``dtype``.
  
      The decorated test class tests forward, backward and double backward
      computations on CPU and GPU across the following
      :func:`~chainer.testing.parameterize` ed parameters:
  
      - shape: rank of zero, and rank of more than zero
      - dtype: ``numpy.float16``, ``numpy.float32`` and ``numpy.float64``
  
      Additionally, it tests the label of the Chainer function.
  
      Chainer functions tested by the test class decorated with the decorator
      should have the following properties:
  
      - Unary, taking one parameter and returning one value
      - ``dtype`` of input and output are the same
      - Elementwise operation for the supplied ndarray
  
      .. admonition:: Example
  
         The following code defines a test class that tests
         :func:`~chainer.functions.sin` Chainer function, which takes a parameter
         with ``dtype`` of float and returns a value with the same ``dtype``.
  
         .. doctest::
  
            >>> import unittest
            >>> from chainer import testing
            >>> from chainer import functions as F
            >>>
            >>> @testing.unary_math_function_unittest(F.sin)
            ... class TestSin(unittest.TestCase):
            ...     pass
  
         Because the test methods are implicitly injected to ``TestSin`` class by
         the decorator, it is enough to place ``pass`` in the class definition.
  
         To customize test data, ``make_data`` optional parameter can be used.
         The following is an example of testing ``sqrt`` Chainer function, which
         is tested in positive value domain here instead of the default input.
  
         .. doctest::
  
            >>> import numpy
            >>>
            >>> def make_data(shape, dtype):
            ...     x = numpy.random.uniform(0.1, 1, shape).astype(dtype)
            ...     gy = numpy.random.uniform(-1, 1, shape).astype(dtype)
            ...     ggx = numpy.random.uniform(-1, 1, shape).astype(dtype)
            ...     return x, gy, ggx
            ...
            >>> @testing.unary_math_function_unittest(F.sqrt,
            ...                                       make_data=make_data)
            ... class TestSqrt(unittest.TestCase):
            ...     pass
  
         ``make_data`` function which returns input, gradient and double gradient
         data generated in proper value domains with given ``shape`` and
         ``dtype`` parameters is defined, then passed to the decorator's
         ``make_data`` parameter.
  
      """
      check_available()
  
      # TODO(takagi) In the future, the Chainer functions that could be tested
      #     with the decorator would be extended as:
      #
      #     - Multiple input parameters
      #     - Multiple output values
      #     - Other types than float: integer
      #     - Other operators other than analytic math: basic math
  
      # Import here to avoid mutual import.
      from chainer import gradient_check
      from chainer import testing
  
      is_new_style = not isinstance(func, function.Function)
  
      func_name = _func_name(func)
      func_class = _func_class(func)
  
      if func_expected is None:
          try:
              func_expected = getattr(numpy, func_name)
          except AttributeError:
              raise ValueError(
                  "NumPy has no functions corresponding "
                  "to Chainer function '{}'.".format(func_name)
              )
  
      if label_expected is None:
          label_expected = func_name
      elif func_class is None:
          raise ValueError(
              "Expected label is given even though Chainer "
              "function does not have its label."
          )
  
      if make_data is None:
          if is_new_style:
              make_data = _make_data_default
          else:
  
              def aux(shape, dtype):
                  return _make_data_default(shape, dtype)[0:2]
  
              make_data = aux
  
      if is_linear is not None:
          warnings.warn(
              "is_linear option is deprecated", DeprecationWarning
          )
  
      def f(klass):
          assert issubclass(klass, unittest.TestCase)
  
          def setUp(self):
              if is_new_style:
                  self.x, self.gy, self.ggx = make_data(
                      self.shape, self.dtype
                  )
              else:
                  self.x, self.gy = make_data(self.shape, self.dtype)
  
              if self.dtype == numpy.float16:
                  self.forward_options = {
                      "atol": numpy.finfo("float16").eps,  # = 0.000977
                      "rtol": numpy.finfo("float16").eps,  # = 0.000977
                  }
                  self.backward_options = {
                      "eps": 2**-4,
                      "atol": 2**-4,
                      "rtol": 2**-4,
                      "dtype": numpy.float64,
                  }
                  self.double_backward_options = {
                      "eps": 2**-4,
                      "atol": 2**-4,
                      "rtol": 2**-4,
                      "dtype": numpy.float64,
                  }
              else:
                  self.forward_options = {"atol": 1e-4, "rtol": 1e-4}
                  self.backward_options = {
                      "dtype": numpy.float64,
                      "atol": 1e-4,
                      "rtol": 1e-4,
                  }
                  self.double_backward_options = {
                      "dtype": numpy.float64,
                      "atol": 1e-4,
                      "rtol": 1e-4,
                  }
              if forward_options is not None:
                  self.forward_options.update(forward_options)
              if backward_options is not None:
                  self.backward_options.update(backward_options)
              if double_backward_options is not None:
                  self.double_backward_options.update(
                      double_backward_options
                  )
  
          setattr(klass, "setUp", setUp)
  
          def check_forward(self, x_data):
              x = variable.Variable(x_data)
              y = func(x)
              self.assertEqual(y.data.dtype, x_data.dtype)
              y_expected = func_expected(
                  cuda.to_cpu(x_data), dtype=x_data.dtype
              )
              testing.assert_allclose(
                  y_expected, y.data, **self.forward_options
              )
  
          setattr(klass, "check_forward", check_forward)
  
          def test_forward_cpu(self):
              self.check_forward(self.x)
  
          setattr(klass, "test_forward_cpu", test_forward_cpu)
  
          @attr.gpu
          def test_forward_gpu(self):
              self.check_forward(cuda.to_gpu(self.x))
  
          setattr(klass, "test_forward_gpu", test_forward_gpu)
  
          def check_backward(self, x_data, y_grad):
              gradient_check.check_backward(
                  func, x_data, y_grad, **self.backward_options
              )
  
          setattr(klass, "check_backward", check_backward)
  
          def test_backward_cpu(self):
              self.check_backward(self.x, self.gy)
  
          setattr(klass, "test_backward_cpu", test_backward_cpu)
  
          @attr.gpu
          def test_backward_gpu(self):
              self.check_backward(
                  cuda.to_gpu(self.x), cuda.to_gpu(self.gy)
              )
  
          setattr(klass, "test_backward_gpu", test_backward_gpu)
  
          if is_new_style:
  
              def check_double_backward(
                  self, x_data, y_grad, x_grad_grad
              ):
                  func1 = _nonlinear(func) if is_linear else func
                  gradient_check.check_double_backward(
                      func1,
                      x_data,
                      y_grad,
                      x_grad_grad,
                      **self.double_backward_options,
                  )
  
              setattr(
                  klass, "check_double_backward", check_double_backward
              )
  
              def test_double_backward_cpu(self):
                  self.check_double_backward(self.x, self.gy, self.ggx)
  
              setattr(
                  klass,
                  "test_double_backward_cpu",
                  test_double_backward_cpu,
              )
  
              @attr.gpu
              def test_double_backward_gpu(self):
                  self.check_double_backward(
                      cuda.to_gpu(self.x),
                      cuda.to_gpu(self.gy),
                      cuda.to_gpu(self.ggx),
                  )
  
              setattr(
                  klass,
                  "test_double_backward_gpu",
                  test_double_backward_gpu,
              )
  
          if func_class is not None:
  
              def test_label(self):
                  self.assertEqual(func_class().label, label_expected)
  
              setattr(klass, "test_label", test_label)
  
          # Return parameterized class.
          return testing.parameterize(
              *testing.product(
                  {
                      "shape": [(3, 2), ()],
                      "dtype": [
                          numpy.float16,
                          numpy.float32,
                          numpy.float64,
                      ],
                  }
              )
          )(klass)
  
      return f
  
file_path: chainer/chainer/testing/unary_math_function_test.py
imports: import unittest
  import warnings
  import numpy
  from chainer.backends import cuda
  from chainer import function
  from chainer import functions
  from chainer import variable
label_desc: Use image node_id 7 for calling a global function with example usage: unary_math_function_unittest(func, func_expected, label_expected, make_data, is_linear, forward_options, backward_options, double_backward_options) and returns: f, testing, _make_data_default