code: def _db_params_frobenius_cg01():
      """Return dist-belief conditional_gradient values.
  
      Return values been generated from the dist-belief
      conditional_gradient unittest, running with a learning rate of 0.1
      and a lambda_ of 0.1.
  
      These values record how a parameter vector of size 10, initialized
      with 0.0, gets updated with 10 consecutive conditional_gradient
      steps.
      It uses random gradients.
  
      Returns:
          db_grad: The gradients to apply
          db_out: The parameters after the conditional_gradient update.
      """
      db_grad = [[]] * 10
      db_out = [[]] * 10
      db_grad[0] = [
          0.00096264342,
          0.17914793,
          0.93945462,
          0.41396621,
          0.53037018,
          0.93197989,
          0.78648776,
          0.50036013,
          0.55345792,
          0.96722615,
      ]
      db_out[0] = [
          -4.1555551e-05,
          -7.7334875e-03,
          -4.0554531e-02,
          -1.7870162e-02,
          -2.2895107e-02,
          -4.0231861e-02,
          -3.3951234e-02,
          -2.1599628e-02,
          -2.3891762e-02,
          -4.1753378e-02,
      ]
      db_grad[1] = [
          0.17075552,
          0.88821375,
          0.20873757,
          0.25236958,
          0.57578111,
          0.15312378,
          0.5513742,
          0.94687688,
          0.16012503,
          0.22159521,
      ]
      db_out[1] = [
          -0.00961733,
          -0.0507779,
          -0.01580694,
          -0.01599489,
          -0.03470477,
          -0.01264373,
          -0.03443632,
          -0.05546713,
          -0.01140388,
          -0.01665068,
      ]
      db_grad[2] = [
          0.35077485,
          0.47304362,
          0.44412705,
          0.44368884,
          0.078527533,
          0.81223965,
          0.31168157,
          0.43203235,
          0.16792089,
          0.24644311,
      ]
      db_out[2] = [
          -0.02462724,
          -0.03699233,
          -0.03154434,
          -0.03153357,
          -0.00876844,
          -0.05606323,
          -0.02447166,
          -0.03469437,
          -0.0124694,
          -0.01829169,
      ]
      db_grad[3] = [
          0.9694621,
          0.75035888,
          0.28171822,
          0.83813518,
          0.53807181,
          0.3728098,
          0.81454384,
          0.03848977,
          0.89759839,
          0.93665648,
      ]
      db_out[3] = [
          -0.04124615,
          -0.03371741,
          -0.0144246,
          -0.03668303,
          -0.02240246,
          -0.02052062,
          -0.03503307,
          -0.00500922,
          -0.03715545,
          -0.0393002,
      ]
      db_grad[4] = [
          0.38578293,
          0.8536852,
          0.88722926,
          0.66276771,
          0.13678469,
          0.94036359,
          0.69107032,
          0.81897682,
          0.5433259,
          0.67860287,
      ]
      db_out[4] = [
          -0.01979208,
          -0.0380417,
          -0.03747472,
          -0.0305847,
          -0.00779536,
          -0.04024222,
          -0.03156913,
          -0.0337613,
          -0.02578116,
          -0.03148952,
      ]
      db_grad[5] = [
          0.27885768,
          0.76100707,
          0.24625534,
          0.81354135,
          0.18959245,
          0.48038563,
          0.84163809,
          0.41172323,
          0.83259648,
          0.44941229,
      ]
      db_out[5] = [
          -0.01555188,
          -0.04084422,
          -0.01573331,
          -0.04265549,
          -0.01000746,
          -0.02740575,
          -0.04412147,
          -0.02341569,
          -0.0431026,
          -0.02502293,
      ]
      db_grad[6] = [
          0.27233034,
          0.056316052,
          0.5039115,
          0.24105175,
          0.35697976,
          0.75913221,
          0.73577434,
          0.16014607,
          0.57500273,
          0.071136251,
      ]
      db_out[6] = [
          -0.01890448,
          -0.00767214,
          -0.03367592,
          -0.01962219,
          -0.02374279,
          -0.05110247,
          -0.05128598,
          -0.01254396,
          -0.04094185,
          -0.00703416,
      ]
      db_grad[7] = [
          0.58697265,
          0.2494842,
          0.08106143,
          0.39954534,
          0.15892942,
          0.12683646,
          0.74053431,
          0.16033,
          0.66625422,
          0.73515922,
      ]
      db_out[7] = [
          -0.03772914,
          -0.01599993,
          -0.00831695,
          -0.02635719,
          -0.01207801,
          -0.01285448,
          -0.05034328,
          -0.01104364,
          -0.04477356,
          -0.04558991,
      ]
      db_grad[8] = [
          0.8215279,
          0.41994119,
          0.95172721,
          0.68000203,
          0.79439718,
          0.43384039,
          0.55561525,
          0.22567581,
          0.93331909,
          0.29438227,
      ]
      db_out[8] = [
          -0.03919835,
          -0.01970845,
          -0.04187151,
          -0.03195836,
          -0.03546333,
          -0.01999326,
          -0.02899324,
          -0.01083582,
          -0.04472339,
          -0.01725317,
      ]
      db_grad[9] = [
          0.68297005,
          0.67758518,
          0.1748755,
          0.13266537,
          0.70697063,
          0.055731893,
          0.68593478,
          0.50580865,
          0.12602448,
          0.093537711,
      ]
      db_out[9] = [
          -0.04510314,
          -0.04282944,
          -0.0147322,
          -0.0111956,
          -0.04617687,
          -0.00535998,
          -0.0442614,
          -0.03158399,
          -0.01207165,
          -0.00736567,
      ]
      return db_grad, db_out
  
imports: import numpy
  import pytest
  import platform
  import tensorflow
  from tensorflow_addons.utils import test_utils
  from tensorflow_addons.optimizers import conditional_gradient
label_desc: Use image node_id 14 for calling a global function with example usage: _db_params_frobenius_cg01() and returns: db_grad,  db_out