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 __init__(self, base, n_base_output, scales):
      super(FPN, self).__init__()
      with self.init_scope():
          self.base = base
          self.inner = chainer.ChainList()
          self.outer = chainer.ChainList()
  
      init = {"initialW": initializers.GlorotNormal()}
      for _ in range(n_base_output):
          self.inner.append(L.Convolution2D(256, 1, **init))
          self.outer.append(L.Convolution2D(256, 3, pad=1, **init))
  
      self.scales = scales
  
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 1 to create a new FPN object from inherited base classes: chainer with example: obj = FPN(base, n_base_output, scales)