new_contents: import numpy
  import six
  
  from chainer import cuda
  from chainer import function
  from chainer.utils import type_check
  
  
  class Pad(function.Function):
  
      """Padding of an array"""
  
      def __init__(self, pad_width, mode, keywords):
          self.pad_width = pad_width
          self.mode = mode
          self.keywords = keywords
  
      def check_type_forward(self, in_types):
          type_check.expect(in_types.size() == 1)
          x_types = in_types[0]
  
      def forward(self, inputs):
          xp = cuda.get_array_module(*inputs)
          return xp.tile(inputs[0], self.size, self.mode, self.keywords),
  
      def backward(self, inputs, grads):
          pass
  
  
  def pad(x, pad_width, mode, **keywords):
      """Pad an input variable.
  
      Args:
          x (~chainer.Variable): Input variable to be padded.
          pad_width (int or array-like):
              Number of values padded to the edges of each axis.
          mode (str):
              'constant'
                  Pads with a constant values.
          constant_values (int or array-like):
              The values are padded for each axis.
  
      Returns:
          ~chainer.Variable: Output variable.
  
      """
      return Pad(pad_width, mode, keywords)(x)
  
subject: implement forward