code: def test_csd_predict_multi(rng):
      """
      Check that we can predict reasonably from multi-voxel fits:
  
      """
      S0 = 123.0
      _, fbvals, fbvecs = get_fnames("small_64D")
      bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
      gtab = gradient_table(bvals, bvecs)
      response = (np.array([0.0015, 0.0003, 0.0003]), S0)
      with warnings.catch_warnings():
          warnings.filterwarnings(
              "ignore",
              message=descoteaux07_legacy_msg,
              category=PendingDeprecationWarning,
          )
          csd = ConstrainedSphericalDeconvModel(gtab, response)
      coeff = rng.random(45) - 0.5
      coeff[..., 0] = 10.0
      with warnings.catch_warnings():
          warnings.filterwarnings(
              "ignore",
              message=descoteaux07_legacy_msg,
              category=PendingDeprecationWarning,
          )
          S = csd.predict(coeff, S0=123.0)
      multi_S = np.array([[S, S], [S, S]])
      csd_fit_multi = csd.fit(multi_S)
      S0_multi = np.mean(multi_S[..., gtab.b0s_mask], -1)
      pred_multi = csd_fit_multi.predict(S0=S0_multi)
      npt.assert_array_almost_equal(pred_multi, multi_S)
  
imports: import warnings
  import numpy
  import numpy.testing
  from numpy.testing import assert_, assert_equal, assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from dipy.testing import assert_greater, assert_greater_equal
  from dipy.data import get_sphere, get_fnames, default_sphere, small_sphere
  from dipy.sims.voxel import multi_tensor, single_tensor, multi_tensor_odf, all_tensor_evecs, single_tensor_odf
  from dipy.core.gradients import gradient_table
  from dipy.core.sphere import Sphere, HemiSphere
  from dipy.core.sphere_stats import angular_similarity
  from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, ConstrainedSDTModel, forward_sdeconv_mat, odf_deconv, odf_sh_to_sharp, mask_for_response_ssst, response_from_mask_ssst, response_from_mask, auto_response_ssst, auto_response, recursive_response
  from dipy.direction.peaks import peak_directions
  from dipy.reconst.dti import TensorModel, fractional_anisotropy
  from dipy.reconst.shm import descoteaux07_legacy_msg, QballModel, sf_to_sh, sh_to_sf, real_sh_descoteaux, sph_harm_ind_list
  from dipy.reconst.shm import lazy_index
  from dipy.utils.deprecator import ExpiredDeprecationError
  from dipy.io.gradients import read_bvals_bvecs
  from dipy.testing.decorators import set_random_number_generator
label_desc: Use image node_id 15 for calling a global function with example usage: test_csd_predict_multi(rng) without return types