code: def test_with_distance_weighted_miner(self):
      for dtype in TEST_DTYPES:
          memory_size = 256
          inner_loss = NTXentLoss(temperature=0.1)
          inner_miner = DistanceWeightedMiner(
              cutoff=0.5, nonzero_loss_cutoff=1.4
          )
          loss_with_miner = CrossBatchMemory(
              loss=inner_loss,
              embedding_size=2,
              memory_size=memory_size,
              miner=inner_miner,
          )
          for i in range(20):
              embedding_angles = torch.arange(0, 32)
              embeddings = torch.tensor(
                  [angle_to_coord(a) for a in embedding_angles],
                  requires_grad=True,
                  dtype=dtype,
              ).to(
                  TEST_DEVICE
              )  # 2D embeddings
              labels = torch.randint(low=0, high=10, size=(32,)).to(
                  TEST_DEVICE
              )
              loss_val = loss_with_miner(embeddings, labels)
              loss_val.backward()
              self.assertTrue(True)
  
imports: import unittest
  import torch
  import pytorch_metric_learning.losses
  from pytorch_metric_learning.losses import ContrastiveLoss, CrossBatchMemory, MultiSimilarityLoss, NTXentLoss
  from pytorch_metric_learning.miners import DistanceWeightedMiner, MultiSimilarityMiner, PairMarginMiner, TripletMarginMiner
  from pytorch_metric_learning.utils import loss_and_miner_utils
  from ..None import TEST_DEVICE, TEST_DTYPES
  from ..zzz_testing_utils.testing_utils import angle_to_coord
label_desc: Use image node_id 5 for calling the TestCrossBatchMemory obj's underlying member method code with example usage: obj.test_with_distance_weighted_miner() without return types