code: def setup_method(self):
      super().setup_method()
  
      # embedding data from VERB demo
      emb_filename = str(
          self.FIXTURES_ROOT / "fairness" / "bias_embeddings.json"
      )
      with open(emb_filename) as emb_file:
          emb_data = json.load(emb_file)
  
      seed_embeddings1 = torch.cat(
          [
              torch.Tensor(emb_data["he"]).reshape(1, -1),
              torch.Tensor(emb_data["him"]).reshape(1, -1),
          ]
      )
      seed_embeddings2 = torch.cat(
          [
              torch.Tensor(emb_data["she"]).reshape(1, -1),
              torch.Tensor(emb_data["her"]).reshape(1, -1),
          ]
      )
      tm = TwoMeansBiasDirection()
      self.bias_direction = tm(seed_embeddings1, seed_embeddings2)
  
      evaluation_embeddings = []
      expected_bias_mitigated_embeddings = []
      for word in ["engineer", "banker", "nurse", "receptionist"]:
          evaluation_embeddings.append(
              torch.Tensor(emb_data[word]).reshape(1, -1)
          )
          expected_bias_mitigated_embeddings.append(
              torch.Tensor(
                  emb_data["linear_two_means_" + word]
              ).reshape(1, -1)
          )
      self.evaluation_embeddings = torch.cat(
          evaluation_embeddings
      ).reshape(2, 2, -1)
      self.expected_bias_mitigated_embeddings = torch.cat(
          expected_bias_mitigated_embeddings
      ).reshape(2, 2, -1)
  
imports: import torch
  from torch import allclose
  import pytest
  import json
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase, multi_device
  from allennlp.fairness.bias_mitigators import LinearBiasMitigator, HardBiasMitigator, INLPBiasMitigator, OSCaRBiasMitigator
  from allennlp.fairness.bias_direction import TwoMeansBiasDirection
label_desc: Use image node_id 1 for calling the LinearBiasMitigatorTest obj's underlying member method code with example usage: obj.setup_method() without return types