old_contents: import numpy
  
  from chainer.backends import cuda
  from chainer import function_node
  from chainer import utils
  from chainer.utils import type_check
  
  
  _erfcx_cpu = None
  
  
  class Erfcx(function_node.FunctionNode):
  
      @property
      def label(self):
          return 'erfcx'
  
      def check_type_forward(self, in_types):
          type_check.expect(in_types.size() == 1)
          type_check.expect(in_types[0].dtype.kind == 'f')
  
      def forward_cpu(self, x):
          global _erfcx_cpu
          if _erfcx_cpu is None:
              try:
                  from scipy import special
                  _erfcx_cpu = special.erfcx
              except ImportError:
                  raise ImportError("SciPy is not available. Forward computation"
                                    " of erfcx can not be done.")
  
          self.retain_inputs((0,))
          return utils.force_array(_erfcx_cpu(x[0]), dtype=x[0].dtype),
  
      def forward_gpu(self, x):
          self.retain_inputs((0,))
          return cuda.elementwise(
              'T x', 'T y',
              'y = erfcx(x)',
              'elementwise_erfcx',
          )(x[0]),
  
      def backward(self, indexes, gy):
          x = self.get_retained_inputs()[0]
          return 2 * (x * erfcx(x) - numpy.pi ** -0.5) * gy[0],
  
  
  def erfcx(x):
      """Elementwise scaled complementary error function.
  
      .. note::
         Forward computation in CPU can be slow if
         `SciPy <https://www.scipy.org/>`_ is not available.
  
      Args:
          x (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
          :class:`cupy.ndarray`): Input variable.
  
      Returns:
          ~chainer.Variable: Output variable.
      """
      return Erfcx().apply((x,))[0]
  
new_contents: import numpy
  
  from chainer.backends import cuda
  from chainer import function_node
  from chainer import utils
  from chainer.utils import type_check
  
  
  _erfcx_cpu = None
  
  
  class Erfcx(function_node.FunctionNode):
  
      @property
      def label(self):
          return 'erfcx'
  
      def check_type_forward(self, in_types):
          type_check.expect(in_types.size() == 1)
          type_check.expect(in_types[0].dtype.kind == 'f')
  
      def forward_cpu(self, x):
          global _erfcx_cpu
          if _erfcx_cpu is None:
              try:
                  from scipy import special
                  _erfcx_cpu = special.erfcx
              except ImportError:
                  raise ImportError("SciPy is not available. Forward computation"
                                    " of erfcx can not be done.")
  
          self.retain_inputs((0,))
          self.retain_outputs((0,))
          return utils.force_array(_erfcx_cpu(x[0]), dtype=x[0].dtype),
  
      def forward_gpu(self, x):
          self.retain_inputs((0,))
          self.retain_outputs((0,))
          return cuda.elementwise(
              'T x', 'T y',
              'y = erfcx(x)',
              'elementwise_erfcx',
          )(x[0]),
  
      def backward(self, indexes, gy):
          x = self.get_retained_inputs()[0]
          y = self.get_retained_outputs()[0]
          return 2 * (x * y - numpy.pi ** -0.5) * gy[0],
  
  
  def erfcx(x):
      """Elementwise scaled complementary error function.
  
      .. note::
         Forward computation in CPU can be slow if
         `SciPy <https://www.scipy.org/>`_ is not available.
  
      Args:
          x (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
          :class:`cupy.ndarray`): Input variable.
  
      Returns:
          ~chainer.Variable: Output variable.
      """
      return Erfcx().apply((x,))[0]
  
subject: Use retain_outputs in erfcx