code: def setUpClass(self):
      self.dist_miner = HDCMiner(
          filter_percentage=0.3,
          distance=LpDistance(normalize_embeddings=False),
      )
      self.normalized_dist_miner = HDCMiner(
          filter_percentage=0.3,
          distance=LpDistance(normalize_embeddings=True),
      )
      self.normalized_dist_miner_squared = HDCMiner(
          filter_percentage=0.3,
          distance=LpDistance(normalize_embeddings=True, power=2),
      )
      self.sim_miner = HDCMiner(
          filter_percentage=0.3, distance=CosineSimilarity()
      )
      self.labels = torch.LongTensor([0, 0, 1, 1, 1, 0])
      correct_a1 = torch.LongTensor([0, 5, 1, 5])
      correct_p = torch.LongTensor([5, 0, 5, 1])
      self.correct_pos_pairs = torch.stack(
          [correct_a1, correct_p], dim=1
      ).to(TEST_DEVICE)
      correct_a2 = torch.LongTensor([1, 2, 4, 5, 0, 2])
      correct_n = torch.LongTensor([2, 1, 5, 4, 2, 0])
      self.correct_neg_pairs = torch.stack(
          [correct_a2, correct_n], dim=1
      ).to(TEST_DEVICE)
  
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 1 for calling the TestHDCMiner obj's underlying member method code with example usage: obj.setUpClass() without return types