Keras LSTM-RNN layer

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System information

• TensorFlow version (you are using): 2.3.1
• Are you willing to contribute it (Yes/No): Yes, when able and available

Describe the feature and the current behavior/state.
There is a high-level API on Keras to LSTM layers on top of RNN that allows getting LSTM output as simple as this:

lstm_module = LSTMModule(5)   
lstm_input = tf.constant([[0.1, 0.2], [0.3, 0.4]], shape=[1, 2, 2])
lstm_output = lstm_module(lstm_input)

A definition of the LSTM Layer with Model Subclassing API from Tensorflow:

class LSTMModule(tf.keras.layers.Layer):

    def __init__(self, lstm_dims):
        super().__init__()
        self.lstm_dims = lstm_dims
        self.lstm = LSTM(lstm_dims, return_sequences=True, return_state=True)

    def call(self, inputs):
        # Forward pass
        ini_hidden_state = tf.zeros(shape=[1, self.lstm_dims]), tf.zeros(shape=[1, self.lstm_dims])
        return self.get_lstm_output(self.lstm, inputs, ini_hidden_state)

    @staticmethod
    def get_lstm_output(lstm_model, input_sequence, initial_state):
        output = lstm_model(input_sequence, initial_state=initial_state)
        hidden_states, hidden_state, cell_state = output[0], output[1], output[2]
        return hidden_states, hidden_state, cell_state

Will this change the current api? How?
This will add a new feature to tensorflow-framework module.

Who will benefit with this feature?
Anyone that requires deep learning to solve sequence classification and prediction problems and everyone who is already familiar with Keras.

Any Other info.
This feature comes from #270

Non vengono indicati file o test. Inizia esaminando l’issue #270 e le API esistenti di tensorflow-framework relative alla richiesta di un layer LSTM/RNN in stile Keras. Il lavoro è completato quando sarà stabilito se l’API LSTM di alto livello richiesta appartiene a questo repository e sarà definito l’ambito della sua implementazione.