code: def test_lifted_structure_loss(self):
      neg_margin = 0.5
      loss_func = LiftedStructureLoss(neg_margin=neg_margin)
  
      for dtype in TEST_DTYPES:
          embedding_angles = [0, 20, 40, 60, 80]
          embeddings = torch.tensor(
              [angle_to_coord(a) for a in embedding_angles],
              requires_grad=True,
              dtype=dtype,
          ).to(
              TEST_DEVICE
          )  # 2D embeddings
          labels = torch.LongTensor([0, 0, 1, 1, 2])
  
          loss = loss_func(embeddings, labels)
          loss.backward()
  
          pos_pairs = [(0, 1), (1, 0), (2, 3), (3, 2)]
          neg_pairs = [
              (0, 2),
              (0, 3),
              (0, 4),
              (1, 2),
              (1, 3),
              (1, 4),
              (2, 0),
              (2, 1),
              (2, 4),
              (3, 0),
              (3, 1),
              (3, 4),
              (4, 0),
              (4, 1),
              (4, 2),
              (4, 3),
          ]
  
          total_loss = 0
          embeddings = torch.nn.functional.normalize(embeddings)
          for a1, p in pos_pairs:
              anchor, positive = embeddings[a1], embeddings[p]
              pos_pair_component = torch.sqrt(
                  torch.sum((anchor - positive) ** 2)
              )
              neg_pair_component = 0
              for a2, n in neg_pairs:
                  negative = embeddings[n]
                  if a2 == a1:
                      neg_pair_component += torch.exp(
                          neg_margin
                          - torch.sqrt(
                              torch.sum((anchor - negative) ** 2)
                          )
                      )
                  elif a2 == p:
                      neg_pair_component += torch.exp(
                          neg_margin
                          - torch.sqrt(
                              torch.sum((positive - negative) ** 2)
                          )
                      )
                  else:
                      continue
              total_loss += (
                  torch.relu(
                      torch.log(neg_pair_component) + pos_pair_component
                  )
                  ** 2
              )
  
          total_loss /= 2 * len(pos_pairs)
  
          rtol = 1e-2 if dtype == torch.float16 else 1e-5
          self.assertTrue(torch.isclose(loss, total_loss, rtol=rtol))
  
file_path: pytorch-metric-learning/tests/losses/test_lifted_structure_loss.py
imports: import unittest
  import torch
  from pytorch_metric_learning.losses import GeneralizedLiftedStructureLoss, LiftedStructureLoss
  from ..None import TEST_DEVICE, TEST_DTYPES
  from ..zzz_testing_utils.testing_utils import angle_to_coord
label_desc: Use image node_id 1 for calling the TestLiftedStructure obj's underlying member method code with example usage: obj.test_lifted_structure_loss() without return types