code: def test_generate_knntriplets(k_genuine, k_impostor):
      """Checks edge and over the edge cases of knn triplet construction with not
      enough neighbors"""
  
      T_test = [
          [0, 1, 3],
          [0, 1, 4],
          [0, 1, 5],
          [0, 2, 3],
          [0, 2, 4],
          [0, 2, 5],
          [1, 0, 3],
          [1, 0, 4],
          [1, 0, 5],
          [1, 2, 3],
          [1, 2, 4],
          [1, 2, 5],
          [2, 0, 3],
          [2, 0, 4],
          [2, 0, 5],
          [2, 1, 3],
          [2, 1, 4],
          [2, 1, 5],
          [3, 4, 0],
          [3, 4, 1],
          [3, 4, 2],
          [3, 5, 0],
          [3, 5, 1],
          [3, 5, 2],
          [4, 3, 0],
          [4, 3, 1],
          [4, 3, 2],
          [4, 5, 0],
          [4, 5, 1],
          [4, 5, 2],
          [5, 3, 0],
          [5, 3, 1],
          [5, 3, 2],
          [5, 4, 0],
          [5, 4, 1],
          [5, 4, 2],
      ]
  
      X = np.array(
          [[0, 0], [2, 2], [4, 4], [8, 8], [16, 16], [32, 32], [33, 33]]
      )
      y = np.array([1, 1, 1, 2, 2, 2, -1])
  
      msg1 = (
          "The class 1 has 3 elements, which is not sufficient to "
          f"generate {k_genuine+1} genuine neighbors "
          "as specified by k_genuine"
      )
      msg2 = (
          "The class 2 has 3 elements, which is not sufficient to "
          f"generate {k_genuine+1} genuine neighbors "
          "as specified by k_genuine"
      )
      msg3 = (
          "The class 1 has 3 elements of other classes, which is "
          f"not sufficient to generate {k_impostor} impostor "
          "neighbors as specified by k_impostor"
      )
      msg4 = (
          "The class 2 has 3 elements of other classes, which is "
          f"not sufficient to generate {k_impostor} impostor "
          "neighbors as specified by k_impostor"
      )
      msgs = [msg1, msg2, msg3, msg4]
      with pytest.warns(UserWarning) as user_warning:
          T = Constraints(y).generate_knntriplets(
              X, k_genuine, k_impostor
          )
      assert any(
          [
              [msg in str(warn.message) for msg in msgs]
              for warn in user_warning
          ]
      )
      assert np.array_equal(sorted(T.tolist()), T_test)
  
file_path: metric-learn/test/test_constraints.py
imports: import pytest
  import numpy
  from sklearn.utils import shuffle
  from metric_learn.constraints import Constraints
  from sklearn.datasets import make_blobs
label_desc: Use image node_id 6 for calling a global function with example usage: test_generate_knntriplets(k_genuine, k_impostor) without return types