code: def decode(self, probs, sizes=None):
          
          _, 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)
docs: 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