class_docstr: A squeeze-and-excitation block.
  
  This block is part of squeeze-and-excitation networks. Channel-wise
  multiplication weights are inferred from and applied to input feature map.
  Please refer to `the original paper
  <https://arxiv.org/pdf/1709.01507.pdf>`_ for more details.
  
  .. seealso::
      :class:`chainercv.links.model.senet.SEResNet`
  
  Args:
      n_channel (int): The number of channels of the input and output array.
      ratio (int): Reduction ratio of :obj:`n_channel` to the number of
          hidden layer units.
code: def __init__(self, n_channel, ratio=16):
      super(SEBlock, self).__init__()
      reduction_size = n_channel // ratio
  
      with self.init_scope():
          self.down = L.Linear(n_channel, reduction_size)
          self.up = L.Linear(reduction_size, n_channel)
  
imports: import chainer
  import chainer.functions
  import chainer.links
label_desc: Use image node_id 1 to create a new SEBlock object from inherited base classes: chainer with example: obj = SEBlock(n_channel, ratio)