var t0 = new(P256Element) var t1 = new(P256Element) z.Square(x) z.Mul(x, z) z.Square(z) z.Mul(x, z) t0.Square(z) for s := 1; s < 3; s++ { t0.Square(t0) } t0.Mul(z, t0) t1.Square(t0) for s := 1; s < 6; s++ { t1.Square(t1) } t0.Mul(t0, t1) for s := 0; s < 3; s++ { t0.Square(t0) } z.Mul(z, t0) t0.Square(z) t0.Mul(x, t0) t1.Square(t0) for s := 1; s < 16; s++ { t1.Square(t1) } t0.Mul(t0, t1) for s := 0; s < 15; s++ { t0.Square(t0) } z.Mul(z, t0) for s := 0; s < 17; s++ { t0.Square(t0) } t0.Mul(x, t0) for...

                  TF_Status* s, const char* name) {
  return MinWithDevice(l, r, graph, /*op_device=*/"", s, name);
}

TF_Operation* Mul(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                  TF_Status* s, const char* name) {
  TF_Operation* op;
  BinaryOpHelper("Mul", l, r, graph, s, name, &op, "", true);
  return op;
}

TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s,

	BREAK				// 00002a00
	UNDEF				// 00002a00

	ANDN	R4, R5, R6		// a6901600
	ANDN	R4, R5			// a5901600
	ORN	R4, R5, R6		// a6101600
	ORN	R4, R5			// a5101600

	// mul
	MUL	R4, R5	  		// a5101c00
	MUL	R4, R5, R6	  	// a6101c00
	MULW	R4, R5	  		// a5101c00
	MULW	R4, R5, R6	  	// a6101c00
	MULV	R4, R5	   		// a5901d00
	MULV	R4, R5, R6	   	// a6901d00
	MULVU	R4, R5			// a5901d00

	// In some db (e.g. MySQL), Unique and UniqueIndex are indistinguishable.
	// When a column has a (not Mul) UniqueIndex, Migrator always reports its gorm.ColumnType is Unique.
	// It causes field unnecessarily migration.
	// Therefore, we need to record the UniqueIndex on this column (exclude Mul UniqueIndex) for MigrateColumnUnique.
	UniqueIndex string
}

func (field *Field) BindName() string {

	CZEROEQZ	X5, X6, X7			// b353530e
	CZEROEQZ	X5, X7				// b3d3530e
	CZERONEZ	X5, X6, X7			// b373530e
	CZERONEZ	X5, X7				// b3f3530e

	// 13.1: Multiplication Operations
	MUL	X5, X6, X7				// b3035302
	MULH	X5, X6, X7				// b3135302
	MULHU	X5, X6, X7				// b3335302
	MULHSU	X5, X6, X7				// b3235302
	MULW	X5, X6, X7				// bb035302

	// 13.2: Division Operations

TEST_F(CApiGradientsTest, MultipleCallsToAddGradients) {
  const float X = 3.0f, Y = 7.0f;
  TF_Operation* x = Placeholder(graph_, s_, "x", TF_FLOAT);
  TF_Operation* y = Placeholder(graph_, s_, "y", TF_FLOAT);
  TF_Operation* xy = Mul(x, y, graph_, s_, "xy");
  TF_Output dxy_dx, dxy_dy;

  TF_Output outputs[1] = {{xy, 0}};
  TF_Output inputs[1] = {{x, 0}};
  TF_AddGradients(graph_, outputs, 1, inputs, 1, nullptr, s_, &dxy_dx);

    *   Unary GPU kernels: Abs, Atanh, Acos, Acosh, Asin, Asinh, Atan, Cos, Cosh, Sin, Sinh, Tan, Tanh.
    *   Binary GPU kernels: AddV2, Sub, Div, DivNoNan, Mul, MulNoNan, FloorDiv, Equal, NotEqual, Greater, GreaterEqual, LessEqual, Less.

* `tf.lite`
    * Add experimental supports conversion of models that may be larger than 2GB before buffer deduplication