code: def _signals(n_subjects=N_SUBJECTS):
      """Generate signals and compute covariances \
      and apply confounds while computing covariances."""
      n_features = N_FEATURES
      signals = []
      for k in range(n_subjects):
          n_samples = 200 + k
          signal, _, confounds = generate_signals(
              n_features=n_features,
              n_confounds=5,
              length=n_samples,
              same_variance=False,
          )
          signals.append(signal)
          signal -= signal.mean(axis=0)
      return signals, confounds
  
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 3 for calling a global function with example usage: _signals(n_subjects) and returns: signals,  confounds