code: def test_full_rank(rng):
      n, p = 10, 5
      X = rng.standard_normal(size=(n, p))
      X_, _ = full_rank(X)
  
      assert_array_almost_equal(X, X_)
  
      X[:, -1] = X[:, :-1].sum(1)
      X_, cond = full_rank(X)
  
      assert cond > 1.0e10
      assert_array_almost_equal(X, X_)
  
imports: import numpy
  import pytest
  import scipy.linalg
  import scipy.stats
  from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from scipy.stats import norm
  from nilearn._utils.data_gen import generate_fake_fmri
  from nilearn.glm._utils import full_rank, multiple_fast_inverse, multiple_mahalanobis, pad_contrast, positive_reciprocal, z_score
  from nilearn.glm.first_level import FirstLevelModel, make_first_level_design_matrix
  from nilearn.maskers import NiftiMasker
label_desc: Use image node_id 1 for calling a global function with example usage: test_full_rank(rng) without return types