code: def test_design_matrix():
      data, gtab = dpd.dsi_voxels()
      sphere = dpd.get_sphere()
      # Make it with NNLS, so that it gets tested regardless of sklearn
      sparse_fascicle_model = sfm.SparseFascicleModel(
          gtab, sphere, solver="NNLS"
      )
      npt.assert_equal(
          sparse_fascicle_model.design_matrix.shape,
          (np.sum(~gtab.b0s_mask), sphere.vertices.shape[0]),
      )
  
file_path: dipy/dipy/reconst/tests/test_sfm.py
imports: import warnings
  import numpy
  import numpy.testing
  import pytest
  import dipy.reconst.sfm
  import dipy.data
  import dipy.core.gradients
  import dipy.sims.voxel
  import dipy.core.optimize
  import dipy.reconst.cross_validation
  from dipy.io.gradients import read_bvals_bvecs
  from dipy.io.image import load_nifti_data
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 1 for calling a global function with example usage: test_design_matrix() without return types