old_contents: import math
  import warnings
  
  import numpy
  
  import chainer
  from chainer.backends import cuda
  from chainer import function_node
  from chainer.functions.math import exponential
  from chainer import utils
  from chainer.utils import type_check
  
  
  _ndtr_cpu = None
  
  
  def _slow_ndtr_cpu(x):
      return 0.5 * math.erfc(-x / 2 ** 0.5)
  
  
  class Ndtr(function_node.FunctionNode):
  
      @property
      def label(self):
          return 'ndtr'
  
      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 _ndtr_cpu
          if _ndtr_cpu is None:
              try:
                  from scipy import special
                  _ndtr_cpu = special.ndtr
              except ImportError:
                  warnings.warn(
                      'SciPy is not available. Forward computation of ndtr in'
                      ' CPU can be slow without SciPy.',
                      chainer.warnings.PerformanceWarining)
                  _ndtr_cpu = numpy.vectorize(_slow_ndtr_cpu)
          self.retain_inputs((0,))
          return utils.force_array(_ndtr_cpu(x[0]), dtype=x[0].dtype),
  
      def forward_gpu(self, x):
          self.retain_inputs((0,))
          return cuda.elementwise(
              'T x', 'T y',
              'y = normcdf(x)',
              'elementwise_ndtr',
          )(x[0]),
  
      def backward(self, indexes, gy):
          x = self.get_retained_inputs()[0]
          return (2 * numpy.pi) ** -0.5 * exponential.exp(-0.5 * x ** 2) * gy[0],
  
  
  def ndtr(x):
      """Elementwise cumulative distribution function of normal distribution.
  
      .. note::
         Forward computation in CPU can be slow if
         `SciPy <https://www.scipy.org/>`_ is not available.
  
      Args:
          x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
  
      Returns:
          ~chainer.Variable: Output variable.
      """
      return Ndtr().apply((x,))[0]
  
new_contents: import math
  import warnings
  
  import numpy
  
  import chainer
  from chainer.backends import cuda
  from chainer import function_node
  from chainer.functions.math import exponential
  from chainer import utils
  from chainer.utils import type_check
  
  
  _ndtr_cpu = None
  
  
  def _slow_ndtr_cpu(x):
      return 0.5 * math.erfc(-x / 2 ** 0.5)
  
  
  class Ndtr(function_node.FunctionNode):
  
      @property
      def label(self):
          return 'ndtr'
  
      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 _ndtr_cpu
          if _ndtr_cpu is None:
              try:
                  from scipy import special
                  _ndtr_cpu = special.ndtr
              except ImportError:
                  warnings.warn(
                      'SciPy is not available. Forward computation of ndtr in'
                      ' CPU can be slow without SciPy.',
                      chainer.warnings.PerformanceWarning)
                  _ndtr_cpu = numpy.vectorize(_slow_ndtr_cpu)
          self.retain_inputs((0,))
          return utils.force_array(_ndtr_cpu(x[0]), dtype=x[0].dtype),
  
      def forward_gpu(self, x):
          self.retain_inputs((0,))
          return cuda.elementwise(
              'T x', 'T y',
              'y = normcdf(x)',
              'elementwise_ndtr',
          )(x[0]),
  
      def backward(self, indexes, gy):
          x = self.get_retained_inputs()[0]
          return (2 * numpy.pi) ** -0.5 * exponential.exp(-0.5 * x ** 2) * gy[0],
  
  
  def ndtr(x):
      """Elementwise cumulative distribution function of normal distribution.
  
      .. note::
         Forward computation in CPU can be slow if
         `SciPy <https://www.scipy.org/>`_ is not available.
  
      Args:
          x (:class:`~chainer.Variable` or :ref:`ndarray`): Input variable.
  
      Returns:
          ~chainer.Variable: Output variable.
      """
      return Ndtr().apply((x,))[0]
  
subject: Fix typo