code: def _generate_signals_from_precisions(
      precisions, min_n_samples=50, max_n_samples=100, random_state=0
  ):
      """Generate timeseries according to some given precision matrices.
  
      Signals all have zero mean.
  
      Parameters
      ----------
      precisions : :obj:`list` of :obj:`numpy.ndarray`
          A list of precision matrices. Every matrix must be square (with the
          same size) and positive definite. The output of
          generate_group_sparse_gaussian_graphs() can be used here.
  
      min_samples, max_samples : :obj:`int`, optional
          The number of samples drawn for each timeseries is taken at random
          between these two numbers. Defaults are 50 and 100.
  
      random_state : :obj:`int` or :obj:`numpy.random.RandomState` instance, \
                     default=0
          Random number generator, or seed.
  
      Returns
      -------
      signals : :obj:`list` of :obj:`numpy.ndarray`
          Output signals. signals[n] corresponds to precisions[n], and has shape
          (sample number, precisions[n].shape[0]).
  
      """
      rand_gen = np.random.default_rng(random_state)
  
      signals = []
      n_samples = rand_gen.integers(
          min_n_samples, high=max_n_samples, size=len(precisions)
      )
  
      mean = np.zeros(precisions[0].shape[0])
      for n, prec in zip(n_samples, precisions):
          signals.append(
              rand_gen.multivariate_normal(
                  mean, np.linalg.inv(prec), (n,)
              )
          )
      return signals
  
imports: from __future__ import annotations
  import itertools
  import json
  import string
  from pathlib import Path
  import numpy
  import pandas
  import scipy.linalg
  import scipy.signal
  from nibabel import Nifti1Image
  from scipy.ndimage import binary_dilation
  from nilearn import datasets, image, maskers, masking
  from nilearn._utils import as_ndarray, logger
  from nilearn.interfaces.bids.utils import bids_entities, check_bids_label, create_bids_filename
  from nilearn.interfaces.fmriprep.tests._testing import get_legal_confound
label_desc: Use image node_id 11 for calling a global function with example usage: _generate_signals_from_precisions(precisions, min_n_samples, max_n_samples, random_state) and returns: signals