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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)