code: def forward(self, x):
      ys = [x]
      H, W = x.shape[2:]
      for f, ksize in zip(self, self.ksizes):
          y = F.average_pooling_2d(x, ksize, ksize)
          y = f(y)
          y = F.resize_images(y, (H, W))
          ys.append(y)
      return F.concat(ys, axis=1)
  
imports: from __future__ import division
  from math import ceil
  import numpy
  import chainer
  import chainer.functions
  from chainer import initializers
  import chainer.links
  from chainercv.experimental.links.model.pspnet.transforms import convolution_crop
  from chainercv.links import Conv2DBNActiv
  from chainercv.links.model.resnet import ResBlock
  from chainercv.links import PickableSequentialChain
  from chainercv import transforms
  from chainercv import utils
label_desc: Use image node_id 2 for calling the PyramidPoolingModule obj's underlying member method code with example usage: obj.forward(x) and returns: F