class_docstr: Implements layer normalization as proposed in [BKH16]_.
class_docstr_tok: ["Implements","layer","normalization","as","proposed","in","[","BKH16","]","_","."]
docstr: Normalizes hidden units of data as follows:
  
  data = scale * (data - mean) / sqrt(var + eps) + shift
  
  Normalization is performed over the last dimension of the input data.
  
  Parameters
  ----------
  data
      Data to normalize of shape (d0, ..., dn, num_hidden)
  
  Returns
  -------
  Normalized inputs of shape: (d0, ..., dn, num_hidden)
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code: def hybrid_forward(self, F, data: Tensor) -> Tensor:
      r"""
      Normalizes hidden units of data as follows:
  
      data = scale * (data - mean) / sqrt(var + eps) + shift
  
      Normalization is performed over the last dimension of the input data.
  
      Parameters
      ----------
      data
          Data to normalize of shape (d0, ..., dn, num_hidden)
  
      Returns
      -------
      Normalized inputs of shape: (d0, ..., dn, num_hidden)
      """
  
      return self.lnorm(data)
  
imports: from typing import Dict, Optional, Tuple
  import mxnet
  from mxnet.gluon import HybridBlock
  from gluonts.mx import Tensor
label_desc: Use image node_id 2 for calling the LayerNormalization obj's underlying member method code with example usage: obj.hybrid_forward(F, data) and returns: self