docstr: Return dist-belief momentum values.
  
  Return values been generated from the dist-belief momentum unittest,
  running with a learning rate of 0.1 and a momentum of 0.1.
  
  These values record how a parameter vector of size 10, initialized with 0.0,
  gets updated with 10 consecutive momentum steps.  It uses random gradients.
  
  Returns:
    db_grad: The gradients to apply
    db_out: The parameters after the momentum update.
docstr_tok: ["Return","dist-belief","momentum","values",".","Return","values","been","generated","from","the","dist-belief","momentum","unittest",",","running","with","a","learning","rate","of","0.1","and","a","momentum","of","0.1",".","These","values","record","how","a","parameter","vector","of","size","10",",","initialized","with","0.0",",","gets","updated","with","10","consecutive","momentum","steps",".","It","uses","random","gradients",".","Returns",":","db_grad",":","The","gradients","to","apply","db_out",":","The","parameters","after","the","momentum","update","."]
code: def _dbParamsMom01(self):
      """Return dist-belief momentum values.
  
      Return values been generated from the dist-belief momentum unittest,
      running with a learning rate of 0.1 and a momentum of 0.1.
  
      These values record how a parameter vector of size 10, initialized with 0.0,
      gets updated with 10 consecutive momentum steps.  It uses random gradients.
  
      Returns:
        db_grad: The gradients to apply
        db_out: The parameters after the momentum update.
      """
      db_grad = [[]] * 10
      db_out = [[]] * 10
      # pylint: disable=line-too-long
      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] = [
          -9.6264346e-05,
          -0.017914793,
          -0.093945466,
          -0.041396622,
          -0.053037018,
          -0.093197994,
          -0.078648776,
          -0.050036013,
          -0.055345792,
          -0.096722618,
      ]
      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] = [
          -0.017181443,
          -0.10852765,
          -0.12421377,
          -0.070773244,
          -0.11591884,
          -0.11783017,
          -0.14165108,
          -0.14972731,
          -0.076892875,
          -0.1285544,
      ]
      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] = [
          -0.053967446,
          -0.1648933,
          -0.1716533,
          -0.1180798,
          -0.13005978,
          -0.20151734,
          -0.17911947,
          -0.20289968,
          -0.095839672,
          -0.15638189,
      ]
      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] = [
          -0.15459226,
          -0.24556576,
          -0.20456907,
          -0.20662397,
          -0.18528105,
          -0.24716705,
          -0.2643207,
          -0.21206589,
          -0.18749419,
          -0.2528303,
      ]
      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] = [
          -0.20323303,
          -0.33900154,
          -0.29658359,
          -0.28175515,
          -0.20448165,
          -0.34576839,
          -0.34194785,
          -0.29488021,
          -0.25099224,
          -0.33033544,
      ]
      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] = [
          -0.23598288,
          -0.42444581,
          -0.33041057,
          -0.3706224,
          -0.22536094,
          -0.40366709,
          -0.43387437,
          -0.34433398,
          -0.34060168,
          -0.38302717,
      ]
      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] = [
          -0.26649091,
          -0.43862185,
          -0.38418442,
          -0.40361428,
          -0.26314685,
          -0.48537019,
          -0.51664448,
          -0.36529395,
          -0.40706289,
          -0.39540997,
      ]
      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] = [
          -0.32823896,
          -0.46498787,
          -0.39766794,
          -0.446868,
          -0.28281838,
          -0.50622416,
          -0.59897494,
          -0.38342294,
          -0.48033443,
          -0.47016418,
      ]
      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] = [
          -0.41656655,
          -0.50961858,
          -0.49418902,
          -0.51919359,
          -0.36422527,
          -0.55169362,
          -0.6627695,
          -0.40780342,
          -0.58099347,
          -0.50707781,
      ]
      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] = [
          -0.49369633,
          -0.58184016,
          -0.52132869,
          -0.5396927,
          -0.44306302,
          -0.56181377,
          -0.73774242,
          -0.46082234,
          -0.60366184,
          -0.52012295,
      ]
      # pylint: enable=line-too-long
      return db_grad, db_out
  
imports: import numpy
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import embedding_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  from tensorflow.python.training import momentum
label_desc: Use image node_id 12 for calling the MomentumOptimizerTest obj's underlying member method code with example usage: obj._dbParamsMom01() and returns: db_grad,  db_out