class_docstr: An extractor class of Feature Pyramid Networks.
  
  This class wraps a feature extractor and provides
  multi-scale features.
  
  Args:
      base (Link): A base feature extractor.
          It should have :meth:`forward` and :obj:`mean`.
          :meth:`forward` should take a batch of images and return
          feature maps of them. The size of the :math:`k+1`-th feature map
          should be the half as that of the :math:`k`-th feature map.
      n_base_output (int): The number of feature maps
          that :obj:`base` returns.
      scales (tuple of floats): The scales of feature maps.
code: def forward(self, x):
      hs = list(self.base(x))
  
      for i in reversed(range(len(hs))):
          hs[i] = self.inner[i](hs[i])
          if i + 1 < len(hs):
              hs[i] += F.unpooling_2d(hs[i + 1], 2, cover_all=False)
  
      for i in range(len(hs)):
          hs[i] = self.outer[i](hs[i])
  
      while len(hs) < len(self.scales):
          hs.append(
              F.max_pooling_2d(hs[-1], 1, stride=2, cover_all=False)
          )
  
      return hs
  
file_path: chainercv/chainercv/links/model/fpn/fpn.py
imports: import chainer
  import chainer.functions
  from chainer import initializers
  import chainer.links
label_desc: Use image node_id 3 for calling the FPN obj's underlying member method code with example usage: obj.forward(x) and returns: hs