code: def test_predict():
      """
      Test model prediction API
      """
      psphere = get_sphere("symmetric362")
      bvecs = np.concatenate(([[1, 0, 0]], psphere.vertices))
      bvals = np.zeros(len(bvecs)) + 1000
      bvals[0] = 0
      gtab = grad.gradient_table(bvals, bvecs)
      mevals = np.array(
          ([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003])
      )
      mevecs = [
          np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
          np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]]),
      ]
      S = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
  
      dm = dti.TensorModel(gtab, "LS", return_S0_hat=True)
      dmfit = dm.fit(S)
      npt.assert_array_almost_equal(dmfit.predict(gtab, S0=100), S)
      npt.assert_array_almost_equal(dmfit.predict(gtab), S)
      npt.assert_array_almost_equal(
          dm.predict(dmfit.model_params, S0=100), S
      )
  
      fdata, fbvals, fbvecs = get_fnames()
      data = load_nifti_data(fdata)
      # Make the data cube a bit larger:
      data = np.tile(data.T, 2).T
      gtab = grad.gradient_table(fbvals, fbvecs)
      dtim = dti.TensorModel(gtab)
      dtif = dtim.fit(data)
      S0 = np.mean(data[..., gtab.b0s_mask], -1)
      p = dtif.predict(gtab, S0)
      npt.assert_equal(p.shape, data.shape)
      # Predict using S0_hat:
      dtim = dti.TensorModel(gtab, return_S0_hat=True)
      dtif = dtim.fit(data)
      p = dtif.predict(gtab)
      npt.assert_equal(p.shape, data.shape)
      p = dtif.predict(gtab, S0)
      npt.assert_equal(p.shape, data.shape)
  
      # Test iter_fit_tensor with S0_hat
      dtim = dti.TensorModel(gtab, step=2, return_S0_hat=True)
      dtif = dtim.fit(data)
      S0 = np.mean(data[..., gtab.b0s_mask], -1)
      p = dtif.predict(gtab, S0)
      npt.assert_equal(p.shape, data.shape)
  
      # Use a smaller step in predicting:
  
      dtim = dti.TensorModel(gtab, step=2)
      dtif = dtim.fit(data)
      S0 = np.mean(data[..., gtab.b0s_mask], -1)
      p = dtif.predict(gtab, S0)
      npt.assert_equal(p.shape, data.shape)
      # And with a scalar S0:
      S0 = 1
      p = dtif.predict(gtab, S0)
      npt.assert_equal(p.shape, data.shape)
      # Assign the step through kwarg:
      p = dtif.predict(gtab, S0, step=1)
      npt.assert_equal(p.shape, data.shape)
      # And without S0:
      p = dtif.predict(gtab, step=1)
      npt.assert_equal(p.shape, data.shape)
  
imports: import warnings
  import numpy
  import numpy.testing
  import scipy.optimize
  import dipy.reconst.dti
  from dipy.io.gradients import read_bvals_bvecs
  from dipy.reconst.dti import ols_resort_msg, axial_diffusivity, color_fa, fractional_anisotropy, from_lower_triangular, geodesic_anisotropy, lower_triangular, mean_diffusivity, radial_diffusivity, TensorModel, trace, linearity, planarity, sphericity, decompose_tensor, _decompose_tensor_nan
  from dipy.io.image import load_nifti_data
  from dipy.data import get_fnames, dsi_voxels, get_sphere
  from dipy.core.subdivide_octahedron import create_unit_sphere
  import dipy.core.gradients
  import dipy.core.sphere
  from dipy.sims.voxel import single_tensor
  from dipy.testing.decorators import set_random_number_generator
label_desc: Use image node_id 22 for calling a global function with example usage: test_predict() without return types