old_contents: import numpy
  
  from chainer.backends import cuda
  from chainer import function_node
  from chainer.utils import type_check
  
  
  class Pad(function_node.FunctionNode):
  
      """Padding of an array."""
  
      def __init__(self, pad_width, mode, **keywords):
          self.mode = mode
          self.keywords = keywords
          self.pad_width = pad_width
          self.pad_bw = numpy.asarray(pad_width)
          if self.pad_bw.size == 1:
              self.pad_bw = numpy.repeat(self.pad_bw, 2)
  
      def check_type_forward(self, in_types):
          # Depending on the arguments, pad_width and keywords, the input value
          # may be inappropriate. In that case, numpy.pad or cupy.pad will raise
          # errors, so that only check the size and the dtype in this function.
          type_check.expect(in_types.size() == 1)
          x_type = in_types[0]
          type_check.expect(x_type.dtype.kind == 'f')
  
      def forward(self, inputs):
          xp = cuda.get_array_module(*inputs)
          return xp.pad(inputs[0], self.pad_width, mode=self.mode,
                        **self.keywords),
  
      def backward(self, inputs, grad_outputs):
          gy, = grad_outputs
          in_shape = self.inputs[0].shape
          if self.pad_bw.ndim == 1:
              self.pad_bw = numpy.tile(self.pad_bw, (len(in_shape), 1))
          input_idxs = tuple(
              slice(p[0], p[0] + dim) for dim, p in zip(in_shape, self.pad_bw))
          return gy[input_idxs],
  
  
  def pad(x, pad_width, mode, **keywords):
      """Pad an input variable.
  
      Args:
          x (chainer.Variable or :class:``numpy.ndarray`` or cupy.ndarray):
              Input data.
          pad_width (int or array-like):
              Number of values padded to the edges of each axis.
          mode (str):
              Specifies how the function fills the periphery of the array.
              The mode is passed to :func:`numpy.pad` or :func:`cupy.pad`.
              If it is ``'constant'``, the input is padded by a constant value
              specified by ``constant_values``.
          constant_values (int or array-like):
              Constant values to fill the periphery in the ``'constant'`` mode.
  
      Returns:
          ~chainer.Variable: Output variable.
  
      """
      return Pad(pad_width, mode, **keywords).apply((x,))[0]
  
new_contents: import numpy
  
  from chainer.backends import cuda
  from chainer import function_node
  from chainer.utils import type_check
  
  
  class Pad(function_node.FunctionNode):
  
      """Padding of an array."""
  
      def __init__(self, pad_width, mode, **keywords):
          self.mode = mode
          self.keywords = keywords
          self.pad_width = pad_width
          self.pad_bw = numpy.asarray(pad_width)
          if self.pad_bw.size == 1:
              self.pad_bw = numpy.repeat(self.pad_bw, 2)
  
      def check_type_forward(self, in_types):
          # Depending on the arguments, pad_width and keywords, the input value
          # may be inappropriate. In that case, numpy.pad or cupy.pad will raise
          # errors, so that only check the size and the dtype in this function.
          type_check.expect(in_types.size() == 1)
          x_type = in_types[0]
          type_check.expect(x_type.dtype.kind == 'f')
  
      def forward(self, inputs):
          xp = cuda.get_array_module(*inputs)
          return xp.pad(inputs[0], self.pad_width, mode=self.mode,
                        **self.keywords),
  
      def backward(self, inputs, grad_outputs):
          gy, = grad_outputs
          in_shape = self.inputs[0].shape
          if self.pad_bw.ndim == 1:
              self.pad_bw = numpy.tile(self.pad_bw, (len(in_shape), 1))
          input_idxs = tuple(
              slice(p[0], p[0] + dim) for dim, p in zip(in_shape, self.pad_bw))
          return gy[input_idxs],
  
  
  def pad(x, pad_width, mode, **keywords):
      """Pad an input variable.
  
      Args:
          x (chainer.Variable or :class:`numpy.ndarray` or cupy.ndarray):
              Input data.
          pad_width (int or array-like):
              Number of values padded to the edges of each axis.
          mode (str):
              Specifies how the function fills the periphery of the array.
              The mode is passed to :func:`numpy.pad` or :func:`cupy.pad`.
              If it is ``'constant'``, the input is padded by a constant value
              specified by ``constant_values``.
          constant_values (int or array-like):
              Constant values to fill the periphery in the ``'constant'`` mode.
  
      Returns:
          ~chainer.Variable: Output variable.
  
      """
      return Pad(pad_width, mode, **keywords).apply((x,))[0]
  
subject: fix broken chainer.function.pad document