code: def test_high_level_glm_contrasts(in_tmp_path):
      shapes, rk = ((5, 6, 7, 20), (5, 6, 7, 19)), 3
      mask, fmri_data, design_matrices = write_fake_fmri_data(
          shapes, rk
      )
      multi_session_model = FMRILinearModel(
          fmri_data, design_matrices, mask=None
      )
      multi_session_model.fit()
      (z_image,) = multi_session_model.contrast(
          [np.eye(rk)[:2]] * 2, contrast_type="tmin-conjunction"
      )
      (z1,) = multi_session_model.contrast([np.eye(rk)[:1]] * 2)
      (z2,) = multi_session_model.contrast([np.eye(rk)[1:2]] * 2)
      assert (
          z_image.get_fdata()
          < np.maximum(z1.get_fdata(), z2.get_fdata())
      ).all()
  
imports: import numpy
  import pytest
  from nibabel import Nifti1Image, load, save
  from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from nipy.io.nibcompat import get_affine
  from nipy.testing import funcfile
  from nipy.testing.decorators import if_example_data
  from ..glm import FMRILinearModel, GeneralLinearModel, data_scaling
label_desc: Use image node_id 5 for calling a global function with example usage: test_high_level_glm_contrasts(in_tmp_path) without return types