code: def test_convert_to_triplets(self):
      a1 = torch.LongTensor([0, 1, 2, 3])
      p = torch.LongTensor([4, 4, 4, 4])
      a2 = torch.LongTensor([4, 5, 6, 7])
      n = torch.LongTensor([5, 5, 6, 6])
      triplets = lmu.convert_to_triplets(
          (a1, p, a2, n), labels=torch.arange(7)
      )
      self.assertTrue(all(len(x) == 0 for x in triplets))
  
      a2 = torch.LongTensor([0, 4, 5, 6])
      triplets = lmu.convert_to_triplets(
          (a1, p, a2, n), labels=torch.arange(7)
      )
      self.assertTrue(
          triplets
          == (torch.tensor([0]), torch.tensor([4]), torch.tensor([5]))
      )
  
      a1 = torch.LongTensor([0, 1, 0, 2])
      p = torch.LongTensor([5, 6, 7, 8])
      a2 = torch.LongTensor([0, 1, 2, 0])
      n = torch.LongTensor([9, 10, 11, 12])
      triplets = lmu.convert_to_triplets(
          (a1, p, a2, n), labels=torch.arange(13)
      )
      triplets = torch.stack(triplets, dim=1)
      found_set = set()
      for t in triplets:
          found_set.add(tuple(t.cpu().numpy()))
      correct_triplets = {
          (0, 5, 9),
          (0, 5, 12),
          (0, 7, 9),
          (0, 7, 12),
          (1, 6, 10),
          (2, 8, 11),
      }
  
      self.assertTrue(found_set == correct_triplets)
  
file_path: pytorch-metric-learning/tests/utils/test_loss_and_miner_utils.py
imports: import unittest
  import torch
  from pytorch_metric_learning.utils import loss_and_miner_utils
  from ..None import TEST_DEVICE, TEST_DTYPES
label_desc: Use image node_id 3 for calling the TestLossAndMinerUtils obj's underlying member method code with example usage: obj.test_convert_to_triplets() without return types