Execute RecurrentAttention.

        :param inputs: tensor with inputs
        :param hidden: hidden state for LSTM layer
        :param context: context tensor from encoder
        :param context_len: vector of encoder sequence lengths

        :returns (rnn_outputs, hidden, attn_output, attn_scores)

def forward(self, inputs, hidden, context, context_len):
        """
        Execute RecurrentAttention.

        :param inputs: tensor with inputs
        :param hidden: hidden state for LSTM layer
        :param context: context tensor from encoder
        :param context_len: vector of encoder sequence lengths

        :returns (rnn_outputs, hidden, attn_output, attn_scores)
        """
        # set attention mask, sequences have different lengths, this mask
        # allows to include only valid elements of context in attention's
        # softmax
        self.attn.set_mask(context_len, context)

        inputs = self.dropout(inputs)
        rnn_outputs, hidden = self.rnn(inputs, hidden)
        attn_outputs, scores = self.attn(rnn_outputs, context)

        return rnn_outputs, hidden, attn_outputs, scores