code: def test_paired_pca_without_grad(self, device: str):
      seed_embeddings1 = torch.tensor(
          [[1.0, 0.5], [1.5, 1.0]], device=device
      )
      seed_embeddings2 = torch.tensor(
          [[0.5, 1.0], [1.0, 1.5]], device=device
      )
      paired_pca = PairedPCABiasDirection()
  
      const = math.sqrt(2) / 2
      expected_bias_direction = torch.tensor(
          [-const, const], device=device
      )
      test_bias_direction = paired_pca(
          seed_embeddings1, seed_embeddings2
      )
      k = expected_bias_direction / test_bias_direction
      assert k[0].item() == pytest.approx(k[1].item())
      assert seed_embeddings1.grad is None
      assert seed_embeddings2.grad is None
  
imports: import torch
  from torch import allclose
  import pytest
  import math
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, multi_device
  from allennlp.fairness.bias_direction import PCABiasDirection, PairedPCABiasDirection, TwoMeansBiasDirection, ClassificationNormalBiasDirection
label_desc: Use image node_id 2 for calling the PairedPCABiasDirectionTest obj's underlying member method code with example usage: obj.test_paired_pca_without_grad(device) without return types