old_contents: import numpy
  
  from chainer import cuda
  from chainer import function
  from chainer import utils
  from chainer.utils import type_check
  
  
  
  class Expm1(function.Function):
  
      @property
      def label(self):
          return 'expm1'
  
      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):
          self.y = utils.force_array(numpy.expm1(x[0]))
          return self.y,
  
      def forward_gpu(self, x):
          self.y = cuda.cupy.exp(x[0])-1.0
          return self.y,
  
      def backward(self, x, gy):
          return utils.force_array((self.y+1.0).astype('float32') * gy[0]),
  
  
  def expm1(x):
      """Elementwise exponential function."""
      return Expm1()(x)
  
  
  class Log1p(function.Function):
  
      @property
      def label(self):
          return 'log1p'
  
      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):
          return utils.force_array(numpy.log1p(x[0])),
  
      def forward_gpu(self, x):
          return cuda.cupy.log(x[0]+1.0),
  
      def backward(self, x, gy):
          return utils.force_array((gy[0] / (x[0]+1.0)).astype('float32')),
  
  
  def log1p(x):
      """Elementwise natural logarithm function."""
      return Log1p()(x)
  
new_contents: import numpy
  
  from chainer import cuda
  from chainer import function
  from chainer import utils
  from chainer.utils import type_check
  
  
  
  class Expm1(function.Function):
  
      @property
      def label(self):
          return 'expm1'
  
      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):
          self.y = utils.force_array(numpy.expm1(x[0]))
          return self.y,
  
      def forward_gpu(self, x):
          self.y = cuda.cupy.expm1(x[0])
          return self.y,
  
      def backward(self, x, gy):
          return utils.force_array((self.y+1.0).astype('float32') * gy[0]),
  
  
  def expm1(x):
      """Elementwise exponential function."""
      return Expm1()(x)
  
  
  class Log1p(function.Function):
  
      @property
      def label(self):
          return 'log1p'
  
      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):
          return utils.force_array(numpy.log1p(x[0])),
  
      def forward_gpu(self, x):
          return cuda.cupy.log1p(x[0]),
  
      def backward(self, x, gy):
          return utils.force_array((gy[0] / (x[0]+1.0)).astype('float32')),
  
  
  def log1p(x):
      """Elementwise natural logarithm function."""
      return Log1p()(x)
  
subject: modify 1160