class_docstr: Cell class for LayerNormSimpleRNN.
  
  References:
  [1] Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton.
      "Layer Normalization." ArXiv:1607.06450 [Cs, Stat],
      July 21, 2016. http://arxiv.org/abs/1607.06450
  
  Example:
  
  >>> inputs = np.random.random([30,23,9]).astype(np.float32)
  >>> lnsRNNCell = tfa.rnn.LayerNormSimpleRNNCell(4)
  >>> rnn = tf.keras.layers.RNN(lnsRNNCell, return_sequences=True, return_state=True)
  >>> outputs, memory_state = rnn(inputs)
  >>> outputs.shape
  TensorShape([30, 23, 4])
  >>> memory_state.shape
  TensorShape([30, 4])
  
  Args:
    units: Positive integer, dimensionality of the output space.
    activation: Activation function to use.
      Default: hyperbolic tangent (`tanh`).
      If you pass `None`, no activation is applied
      (ie. "linear" activation: `a(x) = x`).
    use_bias: Boolean, (default `True`), whether the layer uses a bias
      vector.
    layernorm_epsilon: Float, (default `1e-5`), Small float added to variance
      to avoid dividing by zero.
    kernel_initializer: Initializer for the `kernel` weights matrix,
      used for the linear transformation of the inputs. Default:
      `glorot_uniform`.
    recurrent_initializer: Initializer for the `recurrent_kernel`
      weights matrix, used for the linear transformation of the recurrent
      state. Default: `orthogonal`.
    bias_initializer: Initializer for the bias vector (`use_bias=True`).
       Default: `zeros`.
    gamma_initializer: Initializer for the gamma vector of the layer
       normalization layer. Default: `ones`.
    kernel_regularizer: Regularizer function applied to the `kernel` weights
      matrix. Default: `None`.
    recurrent_regularizer: Regularizer function applied to the
      `recurrent_kernel` weights matrix. Default: `None`.
    bias_regularizer: Regularizer function applied to the bias vector
       (`use_bias=True`). Default: `None`.
    gamma_regularizer: Regularizer function applied to the gamma vector
       of the layer normalization layer. Default: `None`.
    kernel_constraint: Constraint function applied to the `kernel` weights
      matrix. Default: `None`.
    recurrent_constraint: Constraint function applied to the
      `recurrent_kernel` weights matrix. Default: `None`.
    bias_constraint: Constraint function applied to the bias vector
       (`use_bias=True`). Default: `None`.
    gamma_constraint: Constraint function applied to the gamma vector
       of the layer normalization layer. Default: `None`.
    dropout: Float between 0 and 1. Fraction of the units to drop for the
      linear transformation of the inputs. Default: 0.
    recurrent_dropout: Float between 0 and 1. Fraction of the units to drop
      for the linear transformation of the recurrent state. Default: 0.
  
  Call arguments:
    inputs: A 2D tensor, with shape of `[batch, feature]`.
    states: A 2D tensor with shape of `[batch, units]`, which is the state
      from the previous time step. For timestep 0, the initial state provided
      by the user will be feed to cell.
    training: Python boolean indicating whether the layer should behave in
      training mode or in inference mode. Only relevant when `dropout` or
      `recurrent_dropout` is used.
  
  Examples:
  
  >>> inputs = np.random.random([32, 10, 8]).astype(np.float32)
  >>> rnn = tf.keras.layers.RNN(tfa.rnn.LayerNormSimpleRNNCell(4))
  >>> output = rnn(inputs)  # The output has shape `[32, 4]`.
  >>> rnn = tf.keras.layers.RNN(
  ... tfa.rnn.LayerNormSimpleRNNCell(4),
  ... return_sequences=True,
  ... return_state=True)
  >>> whole_sequence_output, final_state = rnn(inputs)
  >>> whole_sequence_output
  <tf.Tensor: shape=(32, 10, 4), dtype=float32, numpy=...>
  >>> final_state
  <tf.Tensor: shape=(32, 4), dtype=float32, numpy=...>
code: def build(self, input_shape):
      super(LayerNormSimpleRNNCell, self).build(input_shape)
      self.layernorm.build((None, self.units))
  
file_path: addons/tensorflow_addons/rnn/layer_norm_simple_rnn_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 2 for calling the LayerNormSimpleRNNCell obj's underlying member method code with example usage: obj.build(input_shape) without return types