Keras LSTM-RNN layer

No files or tests are named. Start by reviewing issue #270 and the existing tensorflow-framework APIs around the Keras-style LSTM/RNN layer request. Done means establishing whether the requested high-level LSTM API belongs in this repository and defining its implementation scope.

Please make sure that this is a feature request. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:feature_template

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