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`.
docstr: Normalize input and scale it.
  
  Args:
      x (chainer.Variable): A variable holding 4-dimensional array.
          Its :obj:`dtype` is :obj:`numpy.float32`.
  
  Returns:
      chainer.Variable:
      The shape and :obj:`dtype` are same as those of input.
docstr_tok: ["Normalize","input","and","scale","it",".","Args",":","x","(","chainer.Variable",")",":","A","variable","holding","4-dimensional","array",".","Its",":","obj",":","`","dtype","`","is",":","obj",":","`","numpy.float32","`",".","Returns",":","chainer.Variable",":","The","shape","and",":","obj",":","`","dtype","`","are","same","as","those","of","input","."]
code: def forward(self, x):
      """Normalize input and scale it.
  
      Args:
          x (chainer.Variable): A variable holding 4-dimensional array.
              Its :obj:`dtype` is :obj:`numpy.float32`.
  
      Returns:
          chainer.Variable:
          The shape and :obj:`dtype` are same as those of input.
      """
  
      x = F.normalize(x, eps=self.eps, axis=1)
      scale = F.broadcast_to(
          self.scale[:, np.newaxis, np.newaxis], x.shape
      )
      return x * scale
  
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 2 for calling the Normalize obj's underlying member method code with example usage: obj.forward(x) and returns: unknown