code: def test_convergence_fail():
      # Make sure ConvergenceWarning is raised if max_iter is too small
      d = load_linnerud()
      X = d.data
      Y = d.target
      pls_nipals = PLSCanonical(n_components=X.shape[1], max_iter=2)
      with pytest.warns(ConvergenceWarning):
          pls_nipals.fit(X, Y)
  
imports: import warnings
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.cross_decomposition import CCA, PLSSVD, PLSCanonical, PLSRegression
  from sklearn.cross_decomposition._pls import _center_scale_xy, _get_first_singular_vectors_power_method, _get_first_singular_vectors_svd, _svd_flip_1d
  from sklearn.datasets import load_linnerud, make_regression
  from sklearn.ensemble import VotingRegressor
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model import LinearRegression
  from sklearn.utils import check_random_state
  from sklearn.utils.extmath import svd_flip
label_desc: Use image node_id 7 for calling a global function with example usage: test_convergence_fail() without return types