class_docstr: An implementation of `Layer Normalization
  <https://www.semanticscholar.org/paper/Layer-Normalization-Ba-Kiros/97fb4e3d45bb098e27e0071448b6152217bd35a5>`_ .
  
  Layer Normalization stabilises the training of deep neural networks by
  normalising the outputs of neurons from a particular layer. It computes:
  
  output = (gamma * (tensor - mean) / (std + eps)) + beta
  
  Parameters
  ----------
  dimension : ``int``, required.
      The dimension of the layer output to normalize.
  eps : ``float``, optional, (default = 1e-6)
      An epsilon to prevent dividing by zero in the case
      the layer has zero variance.
  
  Returns
  -------
  The normalized layer output.
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code: def forward(self, tensor):  # pylint: disable=arguments-differ
      mean = tensor.mean(-1, keepdim=True)
      std = tensor.std(-1, unbiased=False, keepdim=True)
      return self.gamma * (tensor - mean) / (std + self.eps) + self.beta
  
imports: from __future__ import division
  from __future__ import absolute_import
  import torch
label_desc: Use image node_id 2 for calling the LayerNorm obj's underlying member method code with example usage: obj.forward(tensor) and returns: unknown