class_docstr: Neural Architecture Search (NAS) recurrent network cell.
  
  This implements the recurrent cell from the paper:
  
    https://arxiv.org/abs/1611.01578
  
  Barret Zoph and Quoc V. Le.
  "Neural Architecture Search with Reinforcement Learning" Proc. ICLR 2017.
  
  The class uses an optional projection layer.
  
  Example:
  
  >>> inputs = np.random.random([30,23,9]).astype(np.float32)
  >>> NASCell = tfa.rnn.NASCell(4)
  >>> rnn = tf.keras.layers.RNN(NASCell, 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])
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code: def get_config(self):
      config = {
          "units": self.units,
          "projection": self.projection,
          "use_bias": self.use_bias,
          "kernel_initializer": self.kernel_initializer,
          "recurrent_initializer": self.recurrent_initializer,
          "bias_initializer": self.bias_initializer,
          "projection_initializer": self.projection_initializer,
      }
      base_config = super().get_config()
      return {**base_config, **config}
  
file_path: addons/tensorflow_addons/rnn/nas_cell.py
imports: import tensorflow
  from typeguard import typechecked
  from tensorflow_addons.rnn.abstract_rnn_cell import AbstractRNNCell
  from tensorflow_addons.utils.types import FloatTensorLike, TensorLike, Initializer
  from typing import Optional
label_desc: Use image node_id 6 for calling the NASCell obj's underlying member method code with example usage: obj.get_config() and returns: dict