    def step(
        self,
        step: int,
        lprobs: Tensor,
        scores: Optional[Tensor],
        prev_output_tokens: Optional[Tensor] = None,
        original_batch_idxs: Optional[Tensor] = None,
    ):
        """
        A constrained step builds a large candidates list from the following:
        - the top 2 * {beam_size} items over the whole beam
        - for each item in the beam
          - the top {each_k} (default 1)
          - all next constraints
        We then compute the constrained state of each beam item, and assign
        stripe codes: 0 to the best in each bank, 1 to the 2nd-best, and so
        on. We then sort by (stripe, score), and truncate the list at
        2 * beam size.

        Args:
            step: the decoder step
            lprobs: (batch size, beam size, target vocab)
                the target-vocab distributions for each item in the beam.
        Retrun: A tuple of (scores, indices, beams, constraints) where:
            scores: (batch, output beam size)
                the scores of the chosen elements
            indices: (batch, output beam size)
                the target vocab indices of the chosen elements
            beams: (batch, output beam size)
                the 0-indexed hypothesis ids of the chosen elements
            constraints: (batch, output beam size)
                the new constraint states
        """
        each_k = 1
        device = lprobs.device

        batch_size, beam_size, vocab_size = lprobs.size()

        self.num_cands = min(
            # Just take the k-best. We'll get another k from the 1-best from each
            # row, plus more from the constraints
            beam_size * 2,
            lprobs.view(batch_size, -1).size(1) - 1,  # -1 so we never select pad
        )

        # STEP 0: Preliminary. Prevent EOS for unfinished hyps across all batch items
        constraint_states = self.constraint_states
        if constraint_states and step > 0:
            not_finished_indices = []
            for sentno, sent_constraints in enumerate(constraint_states):
                for beamno, state in enumerate(sent_constraints):
                    index = sentno * beam_size + beamno
                    if not state.finished:
                        not_finished_indices.append(index)
            not_finished_indices = torch.tensor(not_finished_indices)
            if not_finished_indices.numel() > 0:
                lprobs.view(batch_size * beam_size, -1)[
                    not_finished_indices, self.eos
                ] = -math.inf

        if step == 0:
            # at the first step all hypotheses are equally likely, so use
            # only the first beam entry for each batch item
            lprobs = lprobs[:, ::beam_size, :].contiguous()
        else:
            # make probs contain cumulative scores for each hypothesis
            assert scores is not None
            lprobs = lprobs + scores[:, :, step - 1].unsqueeze(-1)

        top_prediction = torch.topk(
            lprobs.view(batch_size, -1),
            self.num_cands,
        )
        scores_buf, indices_buf = top_prediction
        # Project back into relative indices and beams
        beams_buf = indices_buf // vocab_size
        indices_buf = indices_buf.fmod(vocab_size)

        # Short circuit if there are no constraints in this batch
        if not constraint_states:
            return scores_buf, indices_buf, beams_buf

        # STEP 1: get top-1 from each hypothesis across all sentences in the batch
        if step > 0:
            top_scores, top_indices = torch.topk(
                lprobs.view(batch_size * beam_size, -1),
                k=each_k,
                dim=1,
            )
            top_scores = top_scores.view(batch_size, -1)
            top_indices = top_indices.view(batch_size, -1)
            scores_buf = torch.cat((scores_buf, top_scores), dim=1)
            indices_buf = torch.cat((indices_buf, top_indices), dim=1)
            new_beams = torch.arange(0, beam_size, device=device).repeat(batch_size, 1)
            beams_buf = torch.cat((beams_buf, new_beams), dim=1)

        # Now, process sentences in the batch one by one.
        new_scores_buf = torch.zeros((batch_size, 2 * beam_size), device=device)
        new_indices_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long()
        new_beams_buf = torch.zeros((batch_size, 2 * beam_size), device=device).long()
        for sentno, states in enumerate(constraint_states):
            scores, indices, beams, new_states = self.step_sentence(
                step,
                sentno,
                lprobs[sentno],
                constraint_states[sentno],
                beams_buf[sentno].clone(),
                indices_buf[sentno].clone(),
                scores_buf[sentno].clone(),
            )
            new_scores_buf[sentno] = scores
            new_indices_buf[sentno] = indices
            new_beams_buf[sentno] = beams
            self.constraint_states[sentno] = new_states

        return new_scores_buf, new_indices_buf, new_beams_buf
A constrained step builds a large candidates list from the following:
- the top 2 * {beam_size} items over the whole beam
- for each item in the beam
- the top {each_k} (default 1)
- all next constraints
We then compute the constrained state of each beam item, and assign
stripe codes: 0 to the best in each bank, 1 to the 2nd-best, and so
on. We then sort by (stripe, score), and truncate the list at
2 * beam size.

Args:
step: the decoder step
lprobs: (batch size, beam size, target vocab)
the target-vocab distributions for each item in the beam.
Retrun: A tuple of (scores, indices, beams, constraints) where:
scores: (batch, output beam size)
the scores of the chosen elements
indices: (batch, output beam size)
the target vocab indices of the chosen elements
beams: (batch, output beam size)
the 0-indexed hypothesis ids of the chosen elements
constraints: (batch, output beam size)
the new constraint states
fairseq/search.py