old_contents: import numpy
  
  import chainer
  from chainer.backends import cuda
  from chainer import function_node
  from chainer.utils import type_check
  
  
  class MeanAbsoluteError(function_node.FunctionNode):
  
      """Mean absolute error function."""
  
      def check_type_forward(self, in_types):
          type_check.expect(in_types.size() == 2)
          type_check.expect(
              in_types[0].dtype == numpy.float32,
              in_types[1].dtype == numpy.float32,
              in_types[0].shape == in_types[1].shape
          )
  
      def forward_cpu(self, inputs):
          self.retain_inputs((0, 1))
          diff = (inputs[0] - inputs[1]).ravel()
          return numpy.array(abs(diff).sum() / diff.size, dtype=diff.dtype),
  
      def forward_gpu(self, inputs):
          self.retain_inputs((0, 1))
          diff = (inputs[0] - inputs[1]).ravel()
          return abs(diff).sum() / diff.dtype.type(diff.size),
  
      def backward(self, indexes, grad_outputs):
          x0, x1 = self.get_retained_inputs()
          diff = x0 - x1
          gy, = grad_outputs
          coeff = gy * gy.data.dtype.type(1. / diff.size)
          coeff = chainer.functions.broadcast_to(coeff, diff.shape)
          gx0 = coeff * cuda.get_array_module(gy.array).sign(diff.array)
          return gx0, -gx0
  
  
  def mean_absolute_error(x0, x1):
      """Mean absolute error function.
  
      This function computes mean absolute error between two variables. The mean
      is taken over the minibatch.
  
      """
      return MeanAbsoluteError().apply((x0, x1))[0]
  
new_contents: import numpy
  
  import chainer
  from chainer.backends import cuda
  from chainer import function_node
  from chainer.utils import type_check
  
  
  class MeanAbsoluteError(function_node.FunctionNode):
  
      """Mean absolute error function."""
  
      def check_type_forward(self, in_types):
          type_check.expect(in_types.size() == 2)
          type_check.expect(
              in_types[0].dtype == numpy.float32,
              in_types[1].dtype == numpy.float32,
              in_types[0].shape == in_types[1].shape
          )
  
      def forward_cpu(self, inputs):
          x0, x1 = inputs
          self.diff = x0 - x1
          diff = self.diff.ravel()
          return numpy.array(abs(diff).sum() / diff.size, dtype=diff.dtype),
  
      def forward_gpu(self, inputs):
          x0, x1 = inputs
          self.diff = x0 - x1
          diff = self.diff.ravel()
          return abs(diff).sum() / diff.dtype.type(diff.size),
  
      def backward(self, indexes, grad_outputs):
          gy, = grad_outputs
          coeff = gy * gy.data.dtype.type(1. / self.diff.size)
          coeff = chainer.functions.broadcast_to(coeff, self.diff.shape)
          gx0 = coeff * cuda.get_array_module(gy.data).sign(self.diff)
          return gx0, -gx0
  
  
  def mean_absolute_error(x0, x1):
      """Mean absolute error function.
  
      This function computes mean absolute error between two variables. The mean
      is taken over the minibatch.
  
      """
      return MeanAbsoluteError().apply((x0, x1))[0]
  
subject: Revert "use retain_inputs"