code: def test_indexing_on_tensor_fit():
      params = np.zeros([2, 3, 4, 12])
      fit = dti.TensorFit(None, params)
  
      # Should return a TensorFit of appropriate shape
      npt.assert_equal(fit.shape, (2, 3, 4))
      fit1 = fit[0]
      npt.assert_equal(fit1.shape, (3, 4))
      npt.assert_equal(type(fit1), dti.TensorFit)
      fit1 = fit[0, 0, 0]
      npt.assert_equal(fit1.shape, ())
      npt.assert_equal(type(fit1), dti.TensorFit)
      fit1 = fit[[0], slice(None)]
      npt.assert_equal(fit1.shape, (1, 3, 4))
      npt.assert_equal(type(fit1), dti.TensorFit)
  
      # Should raise an index error if too many indices are passed
      npt.assert_raises(IndexError, fit.__getitem__, (0, 0, 0, 0))
  
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 5 for calling a global function with example usage: test_indexing_on_tensor_fit() without return types