code: def test_predict():
      # Test signal reconstruction on ideal example
  
      sphere = default_sphere
  
      btable = np.loadtxt(get_fnames("dsi515btable"))
      bvals = btable[:, 0]
      bvecs = btable[:, 1:]
      gtab = gradient_table(bvals, bvecs)
  
      rumba = RumbaSDModel(gtab, n_iter=600, sphere=sphere)
  
      # Simulated data
      data = single_tensor(gtab, S0=1, evals=rumba.wm_response)
      rumba_fit = rumba.fit(data)
      data_pred = rumba_fit.predict()
  
      assert_allclose(data_pred, data, atol=0.01, rtol=0.05)
  
imports: import warnings
  import numpy
  from numpy.testing import assert_equal, assert_almost_equal, assert_array_equal, assert_allclose, assert_raises
  from numpy.testing import assert_
  from dipy.reconst.rumba import RumbaSDModel, generate_kernel
  from dipy.reconst.csdeconv import AxSymShResponse
  from dipy.data import get_fnames, dsi_voxels, default_sphere, get_sphere
  from dipy.core.gradients import gradient_table, unique_bvals_tolerance
  from dipy.core.geometry import cart2sphere
  from dipy.core.sphere_stats import angular_similarity
  from dipy.reconst.tests.test_dsi import sticks_and_ball_dummies
  from dipy.sims.voxel import sticks_and_ball, multi_tensor, single_tensor
  from dipy.direction.peaks import peak_directions
  from dipy.reconst.shm import descoteaux07_legacy_msg
label_desc: Use image node_id 2 for calling a global function with example usage: test_predict() without return types