def METHOD_NAME(self):
    for dtype in TEST_DTYPES:
        angles = [0, 20, 40, 60, 80, 100, 120, 140, 160]
        embeddings = torch.tensor(
            [angle_to_coord(a, normalized=True) for a in angles], dtype=dtype
        ).to(TEST_DEVICE)
        a, p, n = self.normalized_dist_miner_squared(embeddings, self.labels)
        self.helper(a, p, n)
        correct_hardest_pos_pair = torch.sum(
            (embeddings[2] - embeddings[8]) ** 2
        ).item()
        correct_hardest_neg_pair = torch.sum(
            (embeddings[1] - embeddings[2]) ** 2
        ).item()
        places = 2 if dtype == torch.float16 else 5
        if WITH_COLLECT_STATS:
            self.assertAlmostEqual(
                self.normalized_dist_miner_squared.hardest_pos_pair,
                correct_hardest_pos_pair,
                places=places,
            )
            self.assertAlmostEqual(
                self.normalized_dist_miner_squared.hardest_neg_pair,
                correct_hardest_neg_pair,
                places=places,
            )