class_docstr: Layer normalization layer on outputs of linear functions.
  
  .. warning::
  
      This feature is experimental. The interface can change in the future.
  
  This link implements a "layer normalization" layer
  which normalizes the input units by statistics
  that are computed along the second axis,
  scales and shifts them.
  Parameter initialization will be deferred until
  the first forward data pass at which time the size will be determined.
  
  
  Args:
      size (int): Size of input units. If ``None``, parameter initialization
          will be deferred until the first forward data pass at which time
          the size will be determined.
      eps (float): Epsilon value for numerical stability of normalization.
      initial_gamma (~chainer.Initializer): Initializer for scaling vector.
          If ``None``, then the vector is filled by 1.
          If a scalar, the vector is filled by it.
          If ``numpy.ndarray``, the vector is set by it.
      initial_beta (~chainer.Initializer): Initializer for shifting vector.
          If ``None``, then the vector is filled by 0.
          If a scalar, the vector is filled by it.
          If ``numpy.ndarray``, the vector is set by it.
  
  Attributes:
      gamma (~chainer.Parameter): Scaling parameter.
      beta (~chainer.Parameter): Shifting parameter.
      eps (float): Epsilon value for numerical stability.
  
  See: `Layer Normalization <https://arxiv.org/abs/1607.06450>`_
code: def _initialize_params(self, size):
      self.gamma.initialize(size)
      self.beta.initialize(size)
  
file_path: chainer/chainer/links/normalization/layer_normalization.py
imports: from chainer.functions.normalization import layer_normalization
  from chainer import link
  from chainer import utils
  from chainer import variable
label_desc: Use image node_id 2 for calling the LayerNormalization obj's underlying member method code with example usage: obj._initialize_params(size) without return types