code: def test_two_means_without_grad(self, device: str):
      seed_embeddings1 = torch.eye(2, device=device)
      seed_embeddings2 = 1 - torch.eye(2, device=device)
      two_means = TwoMeansBiasDirection()
  
      expected_bias_direction = torch.tensor(
          [float("nan"), float("nan")], device=device
      )
      test_bias_direction = two_means(
          seed_embeddings1, seed_embeddings2
      )
      assert allclose(
          expected_bias_direction, test_bias_direction, equal_nan=True
      )
      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 TwoMeansBiasDirectionTest obj's underlying member method code with example usage: obj.test_two_means_without_grad(device) without return types