code: def test_single_voxel_fit():
      """
      Test the implementation of the fitting for a single voxel.
  
      Here, we will use the multi_tensor function to generate a
      bi-exponential signal. The multi_tensor generates a multi
      tensor signal and expects eigenvalues of each tensor in mevals.
      Our basic test requires a scalar signal isotropic signal and
      hence we set the same eigenvalue in all three directions to
      generate the required signal.
  
      The bvals, f, D_star and D are inspired from the paper by
      Federau, Christian, et al. We use the function "generate_bvecs"
      to simulate bvectors corresponding to the bvalues.
  
      In the two stage fitting routine, initially we fit the signal
      values for bvals less than the specified split_b using the
      TensorModel and get an initial guess for f and D. Then, using
      these parameters we fit the entire data for all bvalues.
      """
      est_signal = ivim_prediction(ivim_fit_single.model_params, gtab)
  
      assert_array_equal(est_signal.shape, data_single.shape)
  
      assert_array_almost_equal(
          ivim_fit_single.model_params, params_trr
      )
      assert_array_almost_equal(est_signal, data_single)
  
      # Test predict function for single voxel
      p = ivim_fit_single.predict(gtab)
      assert_array_equal(p.shape, data_single.shape)
      assert_array_almost_equal(p, data_single)
  
imports: import warnings
  import numpy
  from numpy.testing import assert_array_equal, assert_array_almost_equal, assert_raises, assert_array_less, assert_, assert_equal
  from dipy.testing import assert_greater_equal
  import pytest
  from dipy.reconst.ivim import ivim_prediction, IvimModel
  from dipy.core.gradients import gradient_table, generate_bvecs
  from dipy.sims.voxel import multi_tensor
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 3 for calling a global function with example usage: test_single_voxel_fit() without return types