code: 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()
  
imports: from collections import Counter
  from typing import List, Optional, Set, Tuple
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: pack_constraints(batch_constraints) and returns: constraints_tensor