code: def test_generalized_lifted_structure_loss(self):
      neg_margin = 0.5
      loss_func = GeneralizedLiftedStructureLoss(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),
          ]
  
          embeddings = torch.nn.functional.normalize(embeddings)
          correct_total = 0
          for i in range(len(embeddings)):
              correct_pos_loss = 0
              correct_neg_loss = 0
              for a, p in pos_pairs:
                  if a == i:
                      anchor, positive = embeddings[a], embeddings[p]
                      correct_pos_loss += torch.exp(
                          torch.sqrt(
                              torch.sum((anchor - positive) ** 2)
                          )
                      )
              if correct_pos_loss > 0:
                  correct_pos_loss = torch.log(correct_pos_loss)
  
              for a, n in neg_pairs:
                  if a == i:
                      anchor, negative = embeddings[a], embeddings[n]
                      correct_neg_loss += torch.exp(
                          neg_margin
                          - torch.sqrt(
                              torch.sum((anchor - negative) ** 2)
                          )
                      )
              if correct_neg_loss > 0:
                  correct_neg_loss = torch.log(correct_neg_loss)
  
              correct_total += torch.relu(
                  correct_pos_loss + correct_neg_loss
              )
  
          correct_total /= embeddings.size(0)
  
          rtol = 1e-2 if dtype == torch.float16 else 1e-5
          self.assertTrue(torch.isclose(loss, correct_total, 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 TestGeneralizedLiftedStructureLoss obj's underlying member method code with example usage: obj.test_generalized_lifted_structure_loss() without return types