break // unary expr
		}
		switch x.Op {
		case Mul:
			if name, _ := x.X.(*Name); name != nil && (force || isTypeElem(x.Y)) {
				// x = name *x.Y
				op := *x
				op.X, op.Y = op.Y, nil // change op into unary *op.Y
				return name, &op
			}
		case Or:
			if name, lhs := extractName(x.X, force || isTypeElem(x.Y)); name != nil && lhs != nil {
				// x = name lhs|x.Y
				op := *x
				op.X = lhs
				return name, &op

			continue
		}

		mustBeFirst := func(v *Value) bool {
			return v.Op == OpPhi || v.Op.isLoweredGetClosurePtr() ||
				v.Op == OpArgIntReg || v.Op == OpArgFloatReg
		}

		blockPrologComplete := func(v *Value) bool {
			if b.ID != state.f.Entry.ID {
				return !opcodeTable[v.Op].zeroWidth
			} else {
				return v.Op == OpInitMem
			}
		}

// CHECK-SAME: _replication_info = "foo"
// CHECK: "tf.AssignAddVariableOp"([[VAR]], [[CONST]])

// Preceding user is using resource updated by the cluster within a nested op.
// Resource is updated by a cluster op, and opA (not in cluster) is using the
// resource in a nested op. We expect opA to be after the cluster.
// CHECK-LABEL: func @cluster_nested_op_using_resource
func.func @cluster_nested_op_using_resource() {

(ANDWload  [off1] {sym} x (ADDconst [off2] ptr) mem) && ptr.Op != OpSB && is20Bit(int64(off1)+int64(off2)) => (ANDWload  [off1+off2] {sym} x ptr mem)
(ORload    [off1] {sym} x (ADDconst [off2] ptr) mem) && ptr.Op != OpSB && is20Bit(int64(off1)+int64(off2)) => (ORload    [off1+off2] {sym} x ptr mem)

      quantized_func->emitError() << "At least one op is expected in the "
                                  << kQuantizedOpsAttribute << " attribute.";
      return failure();
    }

    // Use the first op as the representative name.
    return mlir::cast<StringAttr>(quantized_ops.front()).getValue();
  }

  bool IsInCompsiteFunction(Operation* op) {
    func::FuncOp parent = op->getParentOfType<func::FuncOp>();

    the attribute value of the op.

    Args:
      nodes: Iterable of NodeDefs.
      op_name: Name of the op to match.
      attr_name: Name of the attribute of the op to match.
      attr_val: Value of the attr_name to check.
      get_op_name: If set True, checks node.name rather than node.op.

    Returns:

  std::vector<Predicate*> simplified_ops;
  for (Predicate* op : operands) {
    // Simplify A&A => A and  A|A => A.
    if (!simplified_ops_set.insert(op).second) {
      continue;
    }

    if (op->kind() == pred_kind) {
      // "Inline" the operands of an inner And/Or into the parent And/Or.
      for (Predicate* subop : op->GetOperands()) {
        if (simplified_ops_set.insert(subop).second) {

    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)

		}
		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