code: def set_up_glm():
      def _set_up_glm(rng, noise_model, bins=100):
          n, p, q = 100, 80, 10
          X, Y = rng.standard_normal(size=(p, q)), rng.standard_normal(
              size=(p, n)
          )
          labels, results = run_glm(Y, X, noise_model, bins=bins)
          return labels, results, q
  
      return _set_up_glm
  
imports: import numpy
  import pytest
  import scipy.stats
  from numpy.testing import assert_almost_equal
  from sklearn.datasets import make_regression
  from sklearn.linear_model import LinearRegression
  from nilearn.glm.contrasts import Contrast, _compute_fixed_effects_params, compute_contrast, compute_fixed_effect_contrast, expression_to_contrast_vector
  from nilearn.glm.first_level import run_glm
label_desc: Use image node_id 3 for calling a global function with example usage: set_up_glm() and returns: _set_up_glm, labels,  results,  q