code: def enumerate_one_two_matchings(num_destins):
      if num_destins == 1:
          return torch.tensor([[0, 0]])
  
      num_sources = num_destins * 2
      subproblem = enumerate_one_two_matchings(num_destins - 1)
      subsize = subproblem.size(0)
      result = torch.empty(
          subsize * num_sources * (num_sources - 1) // 2,
          num_sources,
          dtype=torch.long,
      )
  
      # Iterate over pairs of sources s0<s1 matching the last destination d.
      d = num_destins - 1
      pos = 0
      for s1 in range(num_sources):
          for s0 in range(s1):
              block = result[pos : pos + subsize]
              block[:, :s0] = subproblem[:, :s0]
              block[:, s0] = d
              block[:, s0 + 1 : s1] = subproblem[:, s0 : s1 - 1]
              block[:, s1] = d
              block[:, s1 + 1 :] = subproblem[:, s1 - 1 :]
              pos += subsize
      return result
  
imports: import logging
  import math
  import warnings
  import torch
  from torch.distributions import constraints
  from torch.distributions.utils import lazy_property
  from .torch import Categorical
  from .torch_distribution import TorchDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: enumerate_one_two_matchings(num_destins) and returns: result, torch