Output const0 = ops::Const(root.WithOpName("const0"), 1);
  Output const1 = ops::Const(root.WithOpName("const1"), 2);

  Output add = ops::Add(root.WithOpName("add"), const0, const1);

  root.graph()->AddControlEdge(id0.node(), const0.node());
  root.graph()->AddControlEdge(id1.node(), const1.node());

  std::unique_ptr<DeadnessAnalysis> result;

	PCs       []dwarf.Range // PC ranges, relative to Textp[0]
	DWInfo    *dwarf.DWDie  // CU root DIE
	FileTable []string      // The file table used in this compilation unit.

	Consts    LoaderSym   // Package constants DIEs
	FuncDIEs  []LoaderSym // Function DIE subtrees
	VarDIEs   []LoaderSym // Global variable DIEs
	AbsFnDIEs []LoaderSym // Abstract function DIE subtrees

    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<[1.0, 2.0]> : tensor<2xf16>} : () -> tensor<2xf16> loc("const2")
    tf_executor.fetch %0#0, %1#0 : tensor<1x2xf16>, tensor<2xf16>
  }
  func.return %graph#0, %graph#1 : tensor<1x2xf16>, tensor<2xf16>
})";
  std::string result;

  auto status = Translate(kMlirSource, result);

  ASSERT_TRUE(status.succeeded());

	for {
		val := auxIntToInt32(v.AuxInt)
		v.reset(OpMIPSMOVWconst)
		v.AuxInt = int32ToAuxInt(int32(val))
		return true
	}
}
func rewriteValueMIPS_OpConst8(v *Value) bool {
	// match: (Const8 [val])
	// result: (MOVWconst [int32(val)])
	for {
		val := auxIntToInt8(v.AuxInt)
		v.reset(OpMIPSMOVWconst)
		v.AuxInt = int32ToAuxInt(int32(val))
		return true
	}
}

		println("stringVal", stringVal, "!=", string(stringAsm[:]))
	}
	if longStringVal != string(longStringAsm[:]) {
		println("longStringVal", longStringVal, "!=", string(longStringAsm[:]))
	}

	// We also include boolean consts in go_asm.h, but they're
	// defined to be "true" or "false", and it's not clear how to
	// use that in assembly.

	if want := unsafe.Sizeof(typ{}); want != uintptr(typSize) {
		println("typSize", want, "!=", typSize)
	}

    TF_Operation* const3;
    if (grad_inputs_provided) {
      const float const3_val[] = {1.0, 1.0, 1.0, 1.0};
      const3 = FloatConst2x2(expected_graph_, s_, const3_val, "GradInputs");
    } else {
      const3 = OnesLike(expected_graph_, s_, matmul, "gradients/OnesLike");
    }

    TF_Operation* matmul1 = MatMul(expected_graph_, s_, const3, const1,

// ChaCha8 is ChaCha with 8 rounds.
// See https://cr.yp.to/chacha/chacha-20080128.pdf.
//
// ChaCha8 operates on a 4x4 matrix of uint32 values, initially set to:
//
//	const1 const2 const3 const4
//	seed   seed   seed   seed
//	seed   seed   seed   seed
//	counter64     0      0
//
// We use the same constants as ChaCha20 does, a random seed,
// and a counter. Running ChaCha8 on this input produces

  // done in parallel.
  //
  // This graph is:
  // (Const0, Const0) -> MatMul0
  // (Const1, Const1) -> MatMul1
  // (MatMul0, MatMul1) -> MatMulCombined
  //
  // Device0: [Const0, Const0, MatMul0]
  // Device1: [Const1, Const1, MatMul1, MatMulCombined]
  //
  // Cluster0: [Const0, Const0, MatMul0]
  // Cluster1: [Const1, Const1, MatMul1]
  // Cluster2: [MatMulCombined]

  %cst = arith.constant dense<0> : tensor<i32> loc("Const")
  %0 = "tfl.greater"(%arg0, %cst) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
  func.return %0 : tensor<i1>
}

func.func @WhileOp_body(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>) {
  %cst = arith.constant dense<1> : tensor<i32> loc("Const1")

	for {
		val := auxIntToFloat64(v.AuxInt)
		v.reset(OpLOONG64MOVDconst)
		v.AuxInt = float64ToAuxInt(float64(val))
		return true
	}
}
func rewriteValueLOONG64_OpConst8(v *Value) bool {
	// match: (Const8 [val])
	// result: (MOVVconst [int64(val)])
	for {
		val := auxIntToInt8(v.AuxInt)
		v.reset(OpLOONG64MOVVconst)
		v.AuxInt = int64ToAuxInt(int64(val))
		return true
	}
}