class_docstr: HyperLSTM with Ortho Init, Layer Norm, Recurrent Dropout, no Memory Loss.
  
  https://arxiv.org/abs/1609.09106
  http://blog.otoro.net/2016/09/28/hyper-networks/
class_docstr_tok: ["HyperLSTM","with","Ortho","Init",",","Layer","Norm",",","Recurrent","Dropout",",","no","Memory","Loss",".","https",":","\/\/arxiv.org\/abs\/1609.09106","http",":","\/\/blog.otoro.net\/2016\/09\/28\/hyper-networks\/"]
code: def hyper_norm(self, layer, scope="hyper", use_bias=True):
      num_units = self.num_units
      embedding_size = self.hyper_embedding_size
      # recurrent batch norm init trick (https://arxiv.org/abs/1603.09025).
      init_gamma = 0.10  # cooijmans' da man.
      with tf.variable_scope(scope):
          zw = super_linear(
              self.hyper_output,
              embedding_size,
              init_w="constant",
              weight_start=0.00,
              use_bias=True,
              bias_start=1.0,
              scope="zw",
          )
          alpha = super_linear(
              zw,
              num_units,
              init_w="constant",
              weight_start=init_gamma / embedding_size,
              use_bias=False,
              scope="alpha",
          )
          result = tf.multiply(alpha, layer)
          if use_bias:
              zb = super_linear(
                  self.hyper_output,
                  embedding_size,
                  init_w="gaussian",
                  weight_start=0.01,
                  use_bias=False,
                  bias_start=0.0,
                  scope="zb",
              )
              beta = super_linear(
                  zb,
                  num_units,
                  init_w="constant",
                  weight_start=0.00,
                  use_bias=False,
                  scope="beta",
              )
              result += beta
      return result
  
imports: import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 6 for calling the HyperLSTMCell obj's underlying member method code with example usage: obj.hyper_norm(layer, scope, use_bias) and returns: result