code: def test_pls_regression_fit_1d_y():
      """Check that when fitting with 1d `y`, prediction should also be 1d.
  
      Non-regression test for Issue #26549.
      """
      X = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36]])
      y = np.array([2, 6, 12, 20, 30, 42])
      expected = y.copy()
  
      plsr = PLSRegression().fit(X, y)
      y_pred = plsr.predict(X)
      assert y_pred.shape == expected.shape
  
      # Check that it works in VotingRegressor
      lr = LinearRegression().fit(X, y)
      vr = VotingRegressor([("lr", lr), ("plsr", plsr)])
      y_pred = vr.fit(X, y).predict(X)
      assert y_pred.shape == expected.shape
      assert_allclose(y_pred, expected)
  
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 23 for calling a global function with example usage: test_pls_regression_fit_1d_y() without return types