def pack_constraints(batch_constraints: List[List[torch.Tensor]]) -> torch.Tensor:
    """Takes a list of list of constraints in tensor form (a list of
    tensor constraints for each sentence) and transforms it into a
    packed Tensor. For example, here is a batch of size 3 with 3, 0,
    and 1 constraints:

        [ [ [3 1 2], [3], [4 5 6 7], ]
          [],
          [ [1 8 9 10 1 4 11 12], ]
        ]

    Its corresponding packed structure is:

        [ [ 3  3  1  2  0  3  0  4  5  6  7  0],
          [ 0  0  0  0  0  0  0  0  0  0  0  0],
          [ 1  1  8  9 10  1  4 11 12  0  0  0] ]

    The packed tensor has shape (batch size, maxlen), where
    maxlen is defined below. Each row contains concatenated
    constraint tokens for that sentence, with 0 appended after
    each constraint. The first item in each row is the number
    of constraints for that sentence. So maxlen is the maximum
    of

    (number of constraints) + (sum length of constraints) + 1.

    across all sentences in the batch.
    """
    # The maximum word length of concatenated constraints for any sentence
    max_constraints_len = 1
    for sentence_constraints in batch_constraints:
        if len(sentence_constraints):
            # number of constraints, plus sum of constrain lens, plus a zero after each
            constraints_len = (
                1
                + sum([c.size(0) for c in sentence_constraints])
                + len(sentence_constraints)
            )
            max_constraints_len = max(max_constraints_len, constraints_len)

    batch_size = len(batch_constraints)
    constraints_tensor = torch.zeros((batch_size, max_constraints_len)).long()
    for i, sentence_constraints in enumerate(batch_constraints):
        constraints_tensor[i, 0] = len(sentence_constraints)
        offset = 1
        for j, constraint in enumerate(sentence_constraints):
            this_len = constraint.size(0)
            constraints_tensor[i, offset : offset + this_len] = constraint
            offset += this_len + 1

    return constraints_tensor.long()
Takes a list of list of constraints in tensor form (a list of
tensor constraints for each sentence) and transforms it into a
packed Tensor. For example, here is a batch of size 3 with 3, 0,
and 1 constraints:

[ [ [3 1 2], [3], [4 5 6 7], ]
[],
[ [1 8 9 10 1 4 11 12], ]
]

Its corresponding packed structure is:

[ [ 3  3  1  2  0  3  0  4  5  6  7  0],
[ 0  0  0  0  0  0  0  0  0  0  0  0],
[ 1  1  8  9 10  1  4 11 12  0  0  0] ]

The packed tensor has shape (batch size, maxlen), where
maxlen is defined below. Each row contains concatenated
constraint tokens for that sentence, with 0 appended after
each constraint. The first item in each row is the number
of constraints for that sentence. So maxlen is the maximum
of

(number of constraints) + (sum length of constraints) + 1.

across all sentences in the batch.
fairseq/token_generation_constraints.py