code: def test_generate_knntriplets_k_impostor():
      """Checks the correct error raised when k_impostor is too big"""
      X, y = shuffle(*make_blobs(random_state=SEED), random_state=SEED)
  
      length = len(y)
      label, labels_count = np.unique(y, return_counts=True)
      labels_count_max = np.max(labels_count)
      (idx_biggest_label,) = np.where(labels_count == labels_count_max)
      k_impostor = length - labels_count_max + 1
  
      warn_msgs = []
      for idx in idx_biggest_label:
          warn_msgs.append(
              "The class {} has {} elements of other classes, which is"
              " not sufficient to generate {} impostor neighbors as "
              "specified by k_impostor. Will generate {} impostor "
              "neighbors instead.\n".format(
                  label[idx], k_impostor - 1, k_impostor, k_impostor - 1
              )
          )
  
      with pytest.warns(UserWarning) as raised_warning:
          Constraints(y).generate_knntriplets(X, 1, k_impostor)
      for warn in raised_warning:
          assert str(warn.message) in warn_msgs
  
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 8 for calling a global function with example usage: test_generate_knntriplets_k_impostor() without return types