#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h"

namespace mlir {
namespace TFL {

void ConvertTFLQuantOpsToMlirQuantOps(func::FuncOp func) {
  OpBuilder b(func);
  func.walk([&](Operation* op) {
    b.setInsertionPoint(op);
    if (auto dq = llvm::dyn_cast<DequantizeOp>(op)) {
      auto dcast = b.create<quantfork::DequantizeCastOp>(
          dq.getLoc(), dq.getOutput().getType(), dq.getInput());

				checkAtomicAddAssignment(pass, n.Lhs[i], call)
			}
		}
	})
	return nil, nil
}

// checkAtomicAddAssignment walks the atomic.Add* method calls checking
// for assigning the return value to the same variable being used in the
// operation
func checkAtomicAddAssignment(pass *analysis.Pass, left ast.Expr, call *ast.CallExpr) {

// function, so all stablehlo functions are of no use in the top-level module.
//
// Walk the module to find functions with `_from_xla_call_module` attribute,
// and remove them.
void RemoveSerializedStablehloFunctions(ModuleOp module) {
  module.walk([&](func::FuncOp f) {
    if (f->hasAttr(kFromXlaCallModuleAttrName)) {
      f->erase();
    }
  });
}

        parent.children.each {
            walk(it) {
                if (predicate.isSatisfiedBy(it)) {
                    matches << it
                }
            }
        }
        matches
    }

    List<BuildOperationRecord.Progress> progress(Class<?> clazz) {
        return all().collect { it.progress(clazz) }.flatten()
    }

    void walk(Action<? super BuildOperationRecord> action) {

      *mlir_module_, DeviceType(DEVICE_TPU_XLA_JIT)));

  FuncOp main = mlir_module_->lookupSymbol<FuncOp>("main");
  ASSERT_TRUE(main);

  bool has_cluster_op = false;
  main.walk([&](mlir::tf_device::ClusterOp) {
    has_cluster_op = true;
    return mlir::WalkResult::interrupt();
  });

  EXPECT_FALSE(has_cluster_op);
}

TEST_F(LowerClusterToRuntimeOpsTest, LowersClusterOpsCPU) {

  TF_ASSIGN_OR_RETURN(const CalibrationStatisticsFlatMap statistics_map,
                      ReadStatistics(calibration_data_dir));

  absl::Status status = absl::OkStatus();
  module_op.walk([&py_function_library, &calibration_options, &status,
                  &statistics_map](mlir::TF::CustomAggregatorOp aggregator_op) {
    mlir::StringRef id = aggregator_op.getId();
    auto iter = statistics_map.find(id);

#include "mlir/IR/SymbolTable.h"  // from @llvm-project
#include "mlir/IR/Visitors.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project

namespace mlir {
namespace TF {

// Walks the function by following function call chains and calling the callback
// for each reachable function (including `func`). Each function is visited only
// once even if it's called from multiple places and/or recursively.
//

  void runOnFunction(func::FuncOp func,
                     const TF::SideEffectAnalysis::Info& analysis) {
    int64_t next_id = 0;
    llvm::SmallDenseMap<Operation*, int64_t, 8> ids;
    func.walk([&](Operation* op) {
      ids[op] = next_id++;
      op->emitRemark("ID: ") << ids[op];
    });
    auto join_ids = [&](const llvm::ArrayRef<Operation*> ops) {
      llvm::SmallVector<std::string, 8> id_vec;

bool DescendantUsesVirtualDevice(
    const std::optional<DictionaryAttr>& virtual_devices,
    Operation* operation) {
  if (!virtual_devices.has_value()) return false;

  auto result = operation->walk([&](Operation* op) {
    if (DirectUseOfVirtualDevice(*virtual_devices, op))
      return WalkResult::interrupt();
    return WalkResult::advance();
  });
  return result.wasInterrupted();
}

#
# Build a MIB with each entry being an array containing the level, type and
# a hash that will contain additional entries if the current entry is a node.
# We then walk this MIB and create a flattened sysctl name to OID hash.
#

use strict;

my $debug = 0;
my %ctls = ();

my @headers = qw (
	sys/sysctl.h
	sys/socket.h
	sys/tty.h
	sys/malloc.h
	sys/mount.h