class_docstr: LSTM cell with layer normalization and recurrent dropout.
  
  This class adds layer normalization and recurrent dropout to a LSTM unit.
  Layer normalization implementation is based on:
  
    https://arxiv.org/abs/1607.06450.
  
  "Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
  
  and is applied before the internal nonlinearities.
  Recurrent dropout is based on:
  
    https://arxiv.org/abs/1603.05118
  
  "Recurrent Dropout without Memory Loss"
  Stanislau Semeniuta, Aliaksei Severyn, Erhardt Barth.
  
  Example:
  
  >>> inputs = np.random.random([30,23,9]).astype(np.float32)
  >>> lnLSTMCell = tfa.rnn.LayerNormLSTMCell(4)
  >>> rnn = tf.keras.layers.RNN(lnLSTMCell, return_sequences=True, return_state=True)
  >>> outputs, memory_state, carry_state = rnn(inputs)
  >>> outputs.shape
  TensorShape([30, 23, 4])
  >>> memory_state.shape
  TensorShape([30, 4])
  >>> carry_state.shape
  TensorShape([30, 4])
class_docstr_tok: ["LSTM","cell","with","layer","normalization","and","recurrent","dropout",".","This","class","adds","layer","normalization","and","recurrent","dropout","to","a","LSTM","unit",".","Layer","normalization","implementation","is","based","on",":","https",":","\/\/arxiv.org\/abs\/1607.06450",".","``","Layer","Normalization","''","Jimmy","Lei","Ba",",","Jamie","Ryan","Kiros",",","Geoffrey","E.","Hinton","and","is","applied","before","the","internal","nonlinearities",".","Recurrent","dropout","is","based","on",":","https",":","\/\/arxiv.org\/abs\/1603.05118","''","Recurrent","Dropout","without","Memory","Loss","''","Stanislau","Semeniuta",",","Aliaksei","Severyn",",","Erhardt","Barth",".","Example",":",">",">",">","inputs","=","np.random.random","(","[","30,23,9","]",")",".astype","(","np.float32",")",">",">",">","lnLSTMCell","=","tfa.rnn.LayerNormLSTMCell","(","4",")",">",">",">","rnn","=","tf.keras.layers.RNN","(","lnLSTMCell",",","return_sequences=True",",","return_state=True",")",">",">",">","outputs",",","memory_state",",","carry_state","=","rnn","(","inputs",")",">",">",">","outputs.shape","TensorShape","(","[","30",",","23",",","4","]",")",">",">",">","memory_state.shape","TensorShape","(","[","30",",","4","]",")",">",">",">","carry_state.shape","TensorShape","(","[","30",",","4","]",")"]
code: def _create_norm_layer(self, name):
      return keras.layers.LayerNormalization(
          beta_initializer=self.norm_beta_initializer,
          gamma_initializer=self.norm_gamma_initializer,
          epsilon=self.norm_epsilon,
          name=name,
      )
  
file_path: addons/tensorflow_addons/rnn/layer_norm_lstm_cell.py
imports: import tensorflow
  import tensorflow.keras
  from typeguard import typechecked
  from tensorflow_addons.utils.types import Activation, FloatTensorLike, TensorLike, Initializer, Constraint, Regularizer
label_desc: Use image node_id 5 for calling the LayerNormLSTMCell obj's underlying member method code with example usage: obj._create_norm_layer(name) and returns: keras