Returns the argmax decoding given the probability matrix. Removes
        repeated elements in the sequence, as well as blanks.

        Arguments:
            probs: Tensor of character probabilities from the network. Expected shape of seq_length x batch x output_dim
            sizes(optional): Size of each sequence in the mini-batch
        Returns:
            strings: sequences of the model's best guess for the transcription on inputs

def decode(self, probs, sizes=None):
        """
        Returns the argmax decoding given the probability matrix. Removes
        repeated elements in the sequence, as well as blanks.

        Arguments:
            probs: Tensor of character probabilities from the network. Expected shape of seq_length x batch x output_dim
            sizes(optional): Size of each sequence in the mini-batch
        Returns:
            strings: sequences of the model's best guess for the transcription on inputs
        """
        _, max_probs = torch.max(probs.transpose(0, 1), 2)
        strings = self.convert_to_strings(max_probs.view(max_probs.size(0), max_probs.size(1)), sizes)
        return self.process_strings(strings, remove_repetitions=True)