code: def test_normalized_dist_squared_mining(self):
      for dtype in TEST_DTYPES:
          angles = [0, 20, 40, 60, 80, 100]
          embeddings = torch.tensor(
              [angle_to_coord(a, normalized=True) for a in angles],
              dtype=dtype,
          ).to(TEST_DEVICE)
          a1, p, a2, n = self.normalized_dist_miner_squared(
              embeddings, self.labels
          )
          pos_pairs = torch.stack([a1, p], dim=1)
          neg_pairs = torch.stack([a2, n], dim=1)
          self.helper(pos_pairs, neg_pairs)
  
file_path: pytorch-metric-learning/tests/miners/test_hdc_miner.py
imports: import unittest
  import torch
  from pytorch_metric_learning.distances import CosineSimilarity, LpDistance
  from pytorch_metric_learning.miners import HDCMiner
  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 TestHDCMiner obj's underlying member method code with example usage: obj.test_normalized_dist_squared_mining() without return types