code: def test_multi_voxel_fit(rng):
      class SillyModel:
          @multi_voxel_fit
          def fit(self, data, mask=None):
              return SillyFit(model, data)
  
          def predict(self, S0):
              return np.ones(10) * S0
  
      class SillyFit:
          def __init__(self, model, data):
              self.model = model
              self.data = data
  
          model_attr = 2.0
  
          def odf(self, sphere):
              return np.ones(len(sphere.phi))
  
          @property
          def directions(self):
              n = rng.integers(0, 10)
              return np.zeros((n, 3))
  
          def predict(self, S0):
              return np.ones(self.data.shape) * S0
  
      # Test the single voxel case
      model = SillyModel()
      single_voxel = np.zeros(64)
      fit = model.fit(single_voxel)
      npt.assert_equal(type(fit), SillyFit)
  
      # Test without a mask
      many_voxels = np.zeros((2, 3, 4, 64))
      fit = model.fit(many_voxels)
      expected = np.empty((2, 3, 4))
      expected[:] = 2.0
      npt.assert_array_equal(fit.model_attr, expected)
      expected = np.ones((2, 3, 4, 12))
      npt.assert_array_equal(fit.odf(unit_icosahedron), expected)
      npt.assert_equal(fit.directions.shape, (2, 3, 4))
      S0 = 100.0
      npt.assert_equal(
          fit.predict(S0=S0), np.ones(many_voxels.shape) * S0
      )
  
      # Test with a mask
      mask = np.zeros((3, 3, 3)).astype("bool")
      mask[0, 0] = 1
      mask[1, 1] = 1
      mask[2, 2] = 1
      data = np.zeros((3, 3, 3, 64))
      fit = model.fit(data, mask)
      expected = np.zeros((3, 3, 3))
      expected[0, 0] = 2
      expected[1, 1] = 2
      expected[2, 2] = 2
      npt.assert_array_equal(fit.model_attr, expected)
      odf = fit.odf(unit_icosahedron)
      npt.assert_equal(odf.shape, (3, 3, 3, 12))
      npt.assert_array_equal(odf[~mask], 0)
      npt.assert_array_equal(odf[mask], 1)
      predicted = np.zeros(data.shape)
      predicted[mask] = S0
      npt.assert_equal(fit.predict(S0=S0), predicted)
  
      # Test fit.shape
      npt.assert_equal(fit.shape, (3, 3, 3))
  
      # Test indexing into a fit
      npt.assert_equal(type(fit[0, 0, 0]), SillyFit)
      npt.assert_equal(fit[:2, :2, :2].shape, (2, 2, 2))
  
file_path: dipy/dipy/reconst/tests/test_multi_voxel.py
imports: from functools import reduce
  import numpy
  import numpy.testing
  from dipy.reconst.multi_voxel import _squash, multi_voxel_fit, CallableArray
  from dipy.core.sphere import unit_icosahedron
  from dipy.testing.decorators import set_random_number_generator
label_desc: Use image node_id 3 for calling a global function with example usage: test_multi_voxel_fit(rng) and returns: SillyFit, unknown, np, np, unknown