code: def test_preprocess_tabu_edges(
      self, dist_type, X, tabu_edges, tabu_edges_updated
  ):
      np.random.seed(42)
      dist_types = [dist_type(idx=idx) for idx in np.arange(X.shape[1])]
      for dt in dist_types:
          X = dt.preprocess_X(X)
          tabu_edges = dt.preprocess_tabu_edges(tabu_edges)
  
      # check that all expected edges are generated
      for tabu_edge in tabu_edges:
          assert tabu_edge in tabu_edges_updated
      # assert lengths are the same
      assert len(tabu_edges_updated) == len(tabu_edges)
      # assert uniqueness
      assert len(set(tabu_edges)) == len(tabu_edges)
  
imports: import copy
  import numpy
  import pandas
  import pytest
  import torch
  from causalnex.structure.pytorch.dist_type import DistTypeBinary, DistTypeCategorical, DistTypeContinuous, DistTypeOrdinal, DistTypePoisson
  from causalnex.structure.pytorch.notears import from_numpy, from_pandas
label_desc: Use image node_id 8 for calling the TestDistTypeClasses obj's underlying member method code with example usage: obj.test_preprocess_tabu_edges(dist_type, X, tabu_edges, tabu_edges_updated) without return types