def METHOD_NAME(self, tokens, lprobs, bsz: int, beam_size: int, step: int):
    """For each hypothesis generate a list of previous ngrams and set associated lprobs to -inf"""
    banned_tokens = [
        torch.jit.annotate(List[int], []) for bbsz_idx in range(bsz * beam_size)
    ]
    if step + 2 - self.no_repeat_ngram_size >= 0:
        cpu_tokens: List[List[int]] = tokens.cpu().tolist()
        check_start_pos = step + 2 - self.no_repeat_ngram_size
        for bbsz_idx in range(bsz * beam_size):
            ngram_to_check = cpu_tokens[bbsz_idx][
                -(self.no_repeat_ngram_size - 1) :
            ]
            for i in range(check_start_pos):
                if (
                    ngram_to_check
                    == cpu_tokens[bbsz_idx][i : i + self.no_repeat_ngram_size - 1]
                ):
                    banned_tokens[bbsz_idx].append(
                        cpu_tokens[bbsz_idx][i + self.no_repeat_ngram_size - 1]
                    )
    for bbsz_idx in range(bsz * beam_size):
        lprobs[bbsz_idx][
            torch.tensor(banned_tokens[bbsz_idx], dtype=torch.int64)
        ] = torch.tensor(-math.inf).to(lprobs)
    return lprobs