text: import pytest
  import numpy as np
  
  from sklearn.neighbors import NearestNeighbors
  from sklearn.svm import SVC
  
  from sklearn.utils._testing import assert_allclose
  from sklearn.utils._testing import assert_array_equal
  
  from imblearn.over_sampling import SVMSMOTE
  
  
  @pytest.fixture
  def data():
      X = np.array(
          [
              [0.11622591, -0.0317206],
              [0.77481731, 0.60935141],
              [1.25192108, -0.22367336],
              [0.53366841, -0.30312976],
              [1.52091956, -0.49283504],
              [-0.28162401, -2.10400981],
              [0.83680821, 1.72827342],
              [0.3084254, 0.33299982],
              [0.70472253, -0.73309052],
              [0.28893132, -0.38761769],
              [1.15514042, 0.0129463],
              [0.88407872, 0.35454207],
              [1.31301027, -0.92648734],
              [-1.11515198, -0.93689695],
              [-0.18410027, -0.45194484],
              [0.9281014, 0.53085498],
              [-0.14374509, 0.27370049],
              [-0.41635887, -0.38299653],
              [0.08711622, 0.93259929],
              [1.70580611, -0.11219234],
          ]
      )
      y = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0])
      return X, y
  
  
  def test_svm_smote(data):
      svm_smote = SVMSMOTE(random_state=42)
      svm_smote_nn = SVMSMOTE(
          random_state=42,
          k_neighbors=NearestNeighbors(n_neighbors=6),
          m_neighbors=NearestNeighbors(n_neighbors=11),
          svm_estimator=SVC(gamma="scale", random_state=42),
      )
  
      X_res_1, y_res_1 = svm_smote.fit_resample(*data)
      X_res_2, y_res_2 = svm_smote_nn.fit_resample(*data)
  
      assert_allclose(X_res_1, X_res_2)
      assert_array_equal(y_res_1, y_res_2)
  