code: def layer_normalization(x, gamma, beta, eps=1e-5):
      """Layer normalization.
  
      This function implements a "layer normalization"
      which normalizes the input units by statistics
      that are computed along the second axis,
      scales and shifts them.
  
      Args:
          x (:class:`~chainer.Variable` or :ref:`ndarray`): Batch vectors.
              Shape of this value must be `(batch_size, unit_size)`,
              e.g., the output of :func:`~chainer.functions.linear`.
          gamma (:class:`~chainer.Variable` or :ref:`ndarray`): Scaling vectors.
          beta (:class:`~chainer.Variable` or :ref:`ndarray`): Shifting vectors.
  
      Returns:
          ~chainer.Variable: The output variable which has the same shape
          as :math:`x`.
  
      See: `Layer Normalization <https://arxiv.org/abs/1607.06450>`_
  
      .. seealso::
  
          :class:`~chainer.links.LayerNormalization` to manage the model
          parameters ``gamma`` and ``beta``.
  
      """
      return LayerNormalization(eps).apply((x, gamma, beta))[0]
  
file_path: chainer/chainer/functions/normalization/layer_normalization.py
imports: from chainer import backend
  from chainer import function_node
  import chainer.functions
  from chainer.utils import type_check
label_desc: Use image node_id 1 for calling a global function with example usage: layer_normalization(x, gamma, beta, eps) and returns: LayerNormalization