// 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"

)

gentbl_cc_library(
    name = "tensorflow_lite_ops_inc_gen",
    compatible_with = get_compatible_with_portable(),
    tbl_outs = [
        (
            ["-gen-op-decls"],
            "ir/tfl_ops.h.inc",
        ),
        (
            ["-gen-op-defs"],
            "ir/tfl_ops.cc.inc",
        ),
        (
            [
                "-gen-dialect-doc",
                "-dialect=tfl",
            ],

	if p.trace {
		defer un(trace(p, "UnaryExpr"))
	}

	switch p.tok {
	case token.ADD, token.SUB, token.NOT, token.XOR, token.AND, token.RANGE:
		pos, op := p.pos, p.tok
		p.next()
		x := p.parseUnaryExpr(false)
		return &ast.UnaryExpr{pos, op, p.checkExpr(x)}

	case token.ARROW:
		// channel type or receive expression
		pos := p.pos
		p.next()
		if p.tok == token.CHAN {
			p.next()

        ),
        0.65,
    )

    if merge_fusion_with_dequantize:
      # Check activation functions are explicitly present.
      # If present the last op before return should be stablehlo.clamp for relu6
      # and stablehlo.maximum for relu.
      if activation_fn is nn_ops.relu6:
        self.assertRegex(module_str, r'stablehlo.clamp.*\n.*return')

# ops. Since other languages use the same libtensorflow_framework.so, op
# libraries are language agnostic.
#
# This shared object is not used unless framework_shared_object=true (set in the
# configure script unconditionally); otherwise if it is false or undefined, the
# build is static and TensorFlow symbols (in Python only) are loaded into the
# global symbol table in order to support op registration. This means that