code: def test_classification_normal_without_grad(self, device: str):
      seed_embeddings1 = torch.eye(2, device=device)
      seed_embeddings2 = torch.tensor(
          [[1.0, 1.0], [1.0, 1.0]], device=device
      )
      classification_normal = ClassificationNormalBiasDirection()
      test_bias_direction = classification_normal(
          seed_embeddings1, seed_embeddings2
      )
      const = 1 / math.sqrt(2)
      assert (
          allclose(
              test_bias_direction,
              torch.Tensor([const, const]).to(device),
          )
          or allclose(
              test_bias_direction,
              torch.Tensor([-const, -const]).to(device),
          )
          or allclose(
              test_bias_direction,
              torch.Tensor([const, -const]).to(device),
          )
          or allclose(
              test_bias_direction,
              torch.Tensor([-const, const]).to(device),
          )
      )
      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 ClassificationNormalBiasDirectionTest obj's underlying member method code with example usage: obj.test_classification_normal_without_grad(device) without return types