code: def test_confounds_connectome_measure():
      n_subjects = 10
  
      signals, confounds = _signals(n_subjects)
  
      correlation_measure = ConnectivityMeasure(
          kind="correlation", vectorize=True
      )
  
      # Clean confounds on 10 subjects with confounds filtered to 10 subjects in
      # length
      cleaned_vectors = correlation_measure.fit_transform(
          signals, confounds=confounds[:10]
      )
  
      zero_matrix = np.zeros(
          (confounds.shape[1], cleaned_vectors.shape[1])
      )
      assert_array_almost_equal(
          np.dot(confounds[:10].T, cleaned_vectors), zero_matrix
      )
      assert isinstance(cleaned_vectors, np.ndarray)
  
      # Confounds as pandas DataFrame
      confounds_df = DataFrame(confounds[:10])
      correlation_measure.fit_transform(signals, confounds=confounds_df)
  
imports: import copy
  import warnings
  from math import cosh, exp, log, sinh, sqrt
  import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  from pandas import DataFrame
  from scipy import linalg
  from sklearn.covariance import EmpiricalCovariance, LedoitWolf
  from nilearn._utils.extmath import is_spd
  from nilearn.connectome.connectivity_matrices import ConnectivityMeasure, _check_spd, _check_square, _form_symmetric, _geometric_mean, _map_eigenvalues, prec_to_partial, sym_matrix_to_vec, vec_to_sym_matrix
  from nilearn.tests.test_signal import generate_signals
label_desc: Use image node_id 40 for calling a global function with example usage: test_confounds_connectome_measure() without return types