// 262657 = 1 + 512 + 512 * 512
    // expected-remark@+1 {{Cost: 262657}}
    %2 = "tf.MatMul"(%arg, %1) {device = "/job:localhost/replica:0/task:0/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<?x512xf32>, tensor<512x512xf32>) -> tensor<?x512xf32>
    // expected-remark@+1 {{Cost: 512}}

  %7 = "tf.Reshape"(%6, %cst_2) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<9xf32>, tensor<2xi32>) -> tensor<3x3xf32>
  %8 = "tf.MatMul"(%2, %7) {device = "/job:localhost/replica:0/task:0/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>

std::unique_ptr<OperationPass<mlir::func::FuncOp>> CreateOptimizePass();

// Creates an instance of the ReplaceCastHacksWithTFXLAOpsPass, which will
// replace mixed-type convolution and matmul cast hacks by XLA Conv2DOp and
// MatmulOp.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateReplaceCastHacksWithTFXLAOpsPass();

// Creates a pass that moves & merges initializer function's ops into the @main

	JMP dataOctaLoop

dataTLS:
	MOVOU (16*14)(pTbl), T1
	MOVOU (16*15)(pTbl), T2
	PXOR B0, B0
	MOVQ (aut), B0
	PINSRD $2, 8(aut), B0
	PINSRB $12, 12(aut), B0
	XORQ autLen, autLen
	JMP dataMul

dataOctaLoop:
		CMPQ autLen, $128
		JB startSinglesLoop
		SUBQ $128, autLen

		MOVOU (16*0)(aut), X0
		MOVOU (16*1)(aut), X1
		MOVOU (16*2)(aut), X2
		MOVOU (16*3)(aut), X3

DECL_CONVERT_OP(Assert);
DECL_CONVERT_OP(ConcatV2);
DECL_CONVERT_OP(BatchMatMul);
DECL_CONVERT_OP(BatchMatMulV2);
DECL_CONVERT_OP(BatchMatMulV3);
DECL_CONVERT_OP(MatMul);
DECL_CONVERT_OP(MatrixDiagV2);
DECL_CONVERT_OP(MatrixDiagV3);
DECL_CONVERT_OP(Pack);
DECL_CONVERT_OP(Split);
DECL_CONVERT_OP(SplitV);
DECL_CONVERT_OP(Unpack);
DECL_CONVERT_OP(Conv3D);

  %x = "tf.TensorArrayReadV3"(%handle_0, %index, %flow_0) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<2x!tf_type.resource<tensor<?x100xf32>>>, tensor<i32>, tensor<f32>) -> tensor<?x100xf32>
  %y = "tf.MatMul"(%x, %cst) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<?x100xf32>, tensor<100x512xf32>) -> (tensor<?x512xf32>)

	VLD1.P	(pTbl), [T1.B16, T2.B16]
	VEOR	B0.B16, B0.B16, B0.B16

	MOVD	(aut), H0
	VMOV	H0, B0.D[0]
	MOVW	8(aut), H0
	VMOV	H0, B0.S[2]
	MOVB	12(aut), H0
	VMOV	H0, B0.B[12]

	MOVD	$0, autLen
	B	dataMul

octetsLoop:
		CMP	$128, autLen
		BLT	startSinglesLoop
		SUB	$128, autLen

		VLD1.P	32(aut), [B0.B16, B1.B16]

		VLD1.P	32(pTbl), [T1.B16, T2.B16]
		VREV64	B0.B16, B0.B16

// identifier is the order of that attribute in `attributes`. This map
// is then used to set attributes in the quantized functions in the
// QuantizeCompositeFunctionsPass.
// For example, for tf.MatMul with `attributes` = {{"transpose_a", false},
// {"transpose_b", false}}, the generated attr_map is
// "0:transpose_a,1:transpose_b", where 0 and 1 are the respective attribute
// identifiers.

          "(LRN, LRNGrad)."
          " BN: TF FusedBatchNorm* operations."
          " FUSIBLE: All TF operations that XLA can fuse (All the above). "
          "You can also put any TF operation name, e.g. 'FUSIBLE,MatMul'."),
      Flag("tf_xla_cluster_exclude_ops",
           &mark_for_compilation_flags->tf_xla_cluster_exclude_ops,
           "(experimental) "
           "Exclude the operations from auto-clustering. "

	y.doinit()
	z.doinit()
	C.mpz_sub(&z.i[0], &x.i[0], &y.i[0])
	return z
}

// Mul sets z = x * y and returns z.
func (z *Int) Mul(x, y *Int) *Int {
	x.doinit()
	y.doinit()
	z.doinit()
	C.mpz_mul(&z.i[0], &x.i[0], &y.i[0])
	return z
}

// Div sets z = x / y, rounding toward zero, and returns z.
func (z *Int) Div(x, y *Int) *Int {
	x.doinit()
	y.doinit()
	z.doinit()