code: def test_minmax_normalize():
      bvalue = 3000
      S0 = 1
      SNR = 100
  
      sphere = get_sphere("symmetric362")
      bvecs = np.concatenate(([[0, 0, 0]], sphere.vertices))
      bvals = np.zeros(len(bvecs)) + bvalue
      bvals[0] = 0
      gtab = gradient_table(bvals, bvecs)
  
      evals = np.array(
          ([0.0017, 0.0003, 0.0003], [0.0017, 0.0003, 0.0003])
      )
  
      multi_tensor(
          gtab,
          evals,
          S0,
          angles=[(0, 0), (90, 0)],
          fractions=[50, 50],
          snr=SNR,
      )
      odf = multi_tensor_odf(
          sphere.vertices,
          evals,
          angles=[(0, 0), (90, 0)],
          fractions=[50, 50],
      )
  
      odf2 = minmax_normalize(odf)
      assert_equal(odf2.max(), 1)
      assert_equal(odf2.min(), 0)
  
      odf3 = np.empty(odf.shape)
      odf3 = minmax_normalize(odf, odf3)
      assert_equal(odf3.max(), 1)
      assert_equal(odf3.min(), 0)
  
file_path: dipy/dipy/reconst/tests/test_odf.py
imports: import numpy
  from numpy.testing import assert_equal, assert_almost_equal, assert_
  from dipy.reconst.odf import OdfFit, OdfModel, minmax_normalize, gfa
  from dipy.core.subdivide_octahedron import create_unit_hemisphere
  from dipy.sims.voxel import multi_tensor, multi_tensor_odf
  from dipy.data import get_sphere
  from dipy.core.gradients import gradient_table, GradientTable
label_desc: Use image node_id 2 for calling a global function with example usage: test_minmax_normalize() without return types