class_docstr: Learnable L2 normalization [#]_.
  
  This link normalizes input along the channel axis and scales it.
  The scale factors are trained channel-wise.
  
  .. [#] Wei Liu, Andrew Rabinovich, Alexander C. Berg.
     ParseNet: Looking Wider to See Better. ICLR 2016.
  
  Args:
      n_channel (int): The number of channels.
      initial: A value to initialize the scale factors. It is pased to
          :meth:`chainer.initializers._get_initializer`. The default value
          is 0.
      eps (float): A small value to avoid zero-division. The default value
          is :math:`1e-5`.
code: def __init__(self, n_channel, initial=0, eps=1e-5):
      super(Normalize, self).__init__()
      self.eps = eps
      with self.init_scope():
          initializer = initializers._get_initializer(initial)
          self.scale = variable.Parameter(initializer)
          self.scale.initialize(
              (n_channel),
          )
  
file_path: chainercv/chainercv/links/model/ssd/normalize.py
imports: import numpy
  import chainer
  import chainer.functions
  from chainer import initializers
  from chainer import variable
label_desc: Use image node_id 1 to create a new Normalize object from inherited base classes: chainer with example: obj = Normalize(n_channel, initial, eps)