TF_DeleteStatus(status);
    ASSERT_EQ(TF_OK, code) << status_msg;

    // Test op list.
    TF_Buffer op_list_buf = TF_GetOpList(lib);
    tensorflow::OpList op_list;
    EXPECT_TRUE(op_list.ParseFromArray(op_list_buf.data, op_list_buf.length));
    ASSERT_EQ(op_list.op_size(), 1);
    EXPECT_EQ("TestCApi1", op_list.op(0).name());
    TF_DeleteLibraryHandle(lib);
  }
#endif  // !defined(TENSORFLOW_NO_SHARED_OBJECTS)

    TF_Operation* op;
    UseHelper(inputs, &op);
    return op;
  }

  // All the *Helper methods are used as a workaround for the restrictions that
  // one cannot call ASSERT_* methods in non-void-returning functions (when
  // exceptions are disabled during compilation)
  void UseHelper(const std::vector<TF_Output>& inputs, TF_Operation** op) {
    TF_OperationDescription* desc =

		}
		a3--
	}

	ops := oprange[p.As&obj.AMask]
	c1 := &xcmp[a1]
	c4 := &xcmp[a4]
	for i := range ops {
		op := &ops[i]
		if (int(op.reg) == a2) && int(op.from3) == a3 && c1[op.from1] && c4[op.to1] && (int(op.to2) == a5) {
			p.Optab = uint16(cap(optab) - cap(ops) + i + 1)
			return op
		}
	}

	c.ctxt.Diag("illegal combination %v %v %v %v %v %v", p.As, DRconv(a1), DRconv(a2), DRconv(a3), DRconv(a4), DRconv(a5))

				yy.SetFloat64(y)
				var (
					op string
					z  float64
					f  func(z, x, y *Float) *Float
				)
				switch i {
				case 0:
					op = "+"
					z = x + y
					f = (*Float).Add
				case 1:
					op = "-"
					z = x - y
					f = (*Float).Sub
				case 2:
					op = "*"
					z = x * y
					f = (*Float).Mul
				case 3:
					op = "/"
					z = x / y

	var n int
	defer func() { p.nestLev -= n }()
	for n = 1; ; n++ {
		incNestLev(p)
		op, oprec := p.tokPrec()
		if oprec < prec1 {
			return x
		}
		pos := p.expect(op)
		y := p.parseBinaryExpr(nil, oprec+1)
		x = &ast.BinaryExpr{X: x, OpPos: pos, Op: op, Y: y}
	}
}

// The result may be a type or even a raw type ([...]int).
func (p *parser) parseExpr() ast.Expr {

		fixShift(&p.To)
	}

	ops := oprange[p.As&obj.AMask]
	c1 := &xcmp[a1]
	c3 := &xcmp[a3]
	for i := range ops {
		op := &ops[i]
		if int(op.a2) == a2 && c1[op.a1] && c3[op.a3] {
			p.Optab = uint16(cap(optab) - cap(ops) + i + 1)
			checkSuffix(c, p, op)
			return op
		}
	}

  // CHECK: return %[[CAST]] : tensor<*xf32>
  func.return %val : tensor<*xf32>
}
// -----

// Test CaseRegion with gradient inside PartitionedCall Op. The gradient local
// variable should be inserted before the PartitionedCall op.

// CHECK-LABEL:   func @main()
func.func @main() -> () {
  %size = "tf.Const"() {value = dense<5> : tensor<i32>} : () -> tensor<i32>

(LEA(Q|L) [c] {s} (ADD(Q|L)const [d] x)) && is32Bit(int64(c)+int64(d)) => (LEA(Q|L) [c+d] {s} x)
(LEA(Q|L) [c] {s} (ADD(Q|L) x y)) && x.Op != OpSB && y.Op != OpSB => (LEA(Q|L)1 [c] {s} x y)
(ADD(Q|L) x (LEA(Q|L) [c] {s} y)) && x.Op != OpSB && y.Op != OpSB => (LEA(Q|L)1 [c] {s} x y)

// fold ADDQconst/ADDLconst into LEAQx/LEALx

// Expressions

func walkBinary(e *ast.BinaryExpr) (has4, has5 bool, maxProblem int) {
	switch e.Op.Precedence() {
	case 4:
		has4 = true
	case 5:
		has5 = true
	}

	switch l := e.X.(type) {
	case *ast.BinaryExpr:
		if l.Op.Precedence() < e.Op.Precedence() {
			// parens will be inserted.
			// pretend this is an *ast.ParenExpr and do nothing.
			break
		}

                                        const char* device);

// The calls to TF_AddInput and TF_AddInputList must match (in number,
// order, and type) the op declaration.  For example, the "Concat" op
// has registration:
//   REGISTER_OP("Concat")
//       .Input("concat_dim: int32")
//       .Input("values: N * T")
//       .Output("output: T")
//       .Attr("N: int >= 2")