code: def test_default_weights():
      input_arr = np.expand_dims(
          np.array(
              [
                  1.15428471,
                  0.37460899,
                  -0.16743798,
                  -0.02638639,
                  -0.02587842,
                  -0.24743459,
                  -0.11091634,
                  -0.01974129,
                  -0.03463564,
                  0.04234652,
                  0.00909119,
                  -0.02181194,
                  -0.01141419,
                  0.06747056,
                  -0.02881568,
                  0.0037776,
                  0.02069041,
                  0.01655271,
                  -0.00958642,
                  0.0103591,
                  -0.00579612,
                  0.00559265,
                  0.00311974,
                  0.0067629,
                  0.00140297,
                  0.01844978,
                  -0.00551951,
                  0.02215372,
                  0.00186543,
                  -0.01057652,
                  0.00189625,
                  -0.01114438,
                  0.00509697,
                  -0.00150783,
                  0.01585437,
                  0.00256389,
                  0.00196107,
                  -0.0108544,
                  0.01143742,
                  -0.00547229,
                  -0.01040528,
                  0.0114365,
                  -0.02261801,
                  0.00452243,
                  0.0015014,
              ]
          ),
          axis=0,
      )
  
      target_arr = np.expand_dims(
          np.array(
              [
                  0.17804961,
                  -0.18878266,
                  0.02026339,
                  -0.0100488,
                  -0.04045521,
                  0.20264171,
                  -0.16151273,
                  0.00508221,
                  0.01158303,
                  0.03848331,
                  0.04242867,
                  -0.00493216,
                  -0.05138939,
                  0.03944791,
                  -0.06210141,
                  -0.01777741,
                  0.00032369,
                  -0.00781484,
                  0.02685455,
                  0.00617174,
                  -0.01357785,
                  0.0112316,
                  0.02457713,
                  -0.00307974,
                  -0.00110319,
                  -0.0274653,
                  0.01606723,
                  -0.05088685,
                  0.0017358,
                  -0.00533427,
                  -0.00785866,
                  0.00529946,
                  0.00624491,
                  0.00682212,
                  -0.00551173,
                  -0.00760572,
                  -0.00145562,
                  0.02271283,
                  0.0238023,
                  -0.01574752,
                  0.00853913,
                  -0.00715324,
                  0.02677651,
                  0.01718479,
                  -0.01433261,
              ]
          ),
          axis=0,
      )
  
      resdnn_model = HistoResDNN()
      resdnn_model.fetch_default_weights()
      results_arr = resdnn_model._HistoResDNN__predict(input_arr)
      assert_almost_equal(results_arr, target_arr)
  
file_path: dipy/dipy/nn/tests/test_histo_resdnn.py
imports: import pytest
  from dipy.core.gradients import gradient_table
  from dipy.data import get_fnames
  from dipy.io.image import load_nifti
  from dipy.io.gradients import read_bvals_bvecs
  from dipy.nn.histo_resdnn import HistoResDNN
  from dipy.utils.optpkg import optional_package
  import numpy
  from numpy.testing import assert_almost_equal, assert_raises, assert_equal
label_desc: Use image node_id 1 for calling a global function with example usage: test_default_weights() without return types