SETMI DL                                // 0f98c2
	SETMI R11                               // 410f98c3
	SFENCE                                  // 0faef8
	SGDT (BX)                               // 0f0103
	SGDT (R11)                              // 410f0103
	SHLW $1, (BX)                           // 66d123
	SHLW $1, (R11)                          // 6641d123

8Cf6vooOcWYPPGYS0NR7aB5RKeV7NR0nHx5MMrlywgT0WUtdv0EmLigSR02iR0pV
cWWCdv6/irriSC7KWBseKYkI27DBIhiNBTBe39cVgSd+RIlJ9ojEt7c7PWDQNFl2
qYQQYGc0lMwTFp3+Y3aDFfEsRsTqzvMrCOzlzSXwlanYT/fbFL7iukkYYhA5lmfB
sgdm+nSmwB35Bm7FBXMVSMvKD+SvAUGbkb7Kxutrbm76RQM7E4cNkNhQUcGqtJn5
jRoulS6lXh/GN4V3pg20ds9NY4bcN8uuzyU1iqHfKVQo6l9KkCUItrNY6J/arE7+
AuUSjKupp9fPjLgaM2PMTio1U8gdrhy3Yubfpxwo6Cswyys24QHfSMNYjjuEWhmI

  let results = (outs);
}

def TF_LoadTPUEmbeddingStochasticGradientDescentParametersOp : TF_Op<"LoadTPUEmbeddingStochasticGradientDescentParameters", [TF_MustExecute, TF_TPUEmbeddingReadEffect]> {
  let summary = "Load SGD embedding parameters.";

  let description = [{
An op that loads optimization parameters into HBM for embedding. Must be
preceded by a ConfigureTPUEmbeddingHost op that sets up the correct

			},
			outputs: []outputInfo{
				{0, 1071644664}, // R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R23 R24 R25 R26 R27 R28 R29 R31
			},
		},
	},
	{
		name:   "SGTU",
		argLen: 2,
		asm:    loong64.ASGTU,
		reg: regInfo{
			inputs: []inputInfo{
				{0, 1073741816}, // R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 g R23 R24 R25 R26 R27 R28 R29 R31

    *   Added `tf.keras.optimizers.experimental.Optimizer`. The reworked
        optimizer gives more control over different phases of optimizer calls,
        and is easier to customize. We provide Adam, SGD, Adadelta, AdaGrad and
        RMSprop optimizers based on
        `tf.keras.optimizers.experimental.Optimizer`. Generally the new
        optimizers work in the same way as the old ones, but support new