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")

	if isDigit(c) {
		a = st.sourceName()
	} else if isLower(c) {
		a, _ = st.operatorName(false)
		if _, ok := a.(*Cast); ok {
			isCast = true
		}
		if op, ok := a.(*Operator); ok && op.Name == `operator"" ` {
			n := st.sourceName()
			a = &Unary{Op: op, Expr: n, Suffix: false, SizeofType: false}
		}
	} else if c == 'D' && len(st.str) > 1 && st.str[1] == 'C' {
		var bindings []AST
		st.advance(2)
		for {

    for (auto op : pair.second) {
      ASSERT_TRUE(all_ops.contains(op));
    }
  }

  // Check that all registered XLA operation are in the allowlist
  // table or are known to not be in it.

  absl::flat_hash_set<string> known_not_in_list =
      tensorflow::testing::GetKnownXLAAllowlistOp();
  std::vector<string> unknow_op;
  for (string op : vall_ops) {

				DynamicClient:       dynamicClient,
				APIExtensionsClient: apiExtensionClient,
			}

			for i, op := range c.Operations {
				t.Logf("Performing Operation: %v", op.Description())
				if err := op.Do(ctx); err != nil {
					t.Fatalf("failed %T operation %v: %v\n%v", op, i, err, op)
				}
			}

			// Reset resources

#include "tensorflow/core/framework/kernel_def.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/framework/types.h"