namespace tensorflow {
namespace tracing {
namespace graph {
GraphFunction::GraphFunction(FunctionDef fdef)
    : AbstractFunction(kGraph),
      func_record_(new FunctionRecord(std::move(fdef), {}, true)) {}
GraphFunction::~GraphFunction() {}
Status GraphFunction::GetFunctionDef(const FunctionDef **fdef) {
  *fdef = &(func_record_->fdef());
  return absl::OkStatus();
}
}  // namespace graph

    TF_RETURN_IF_ERROR(tsl::Env::Default()->NewWritableFile(filepath, &file));
    return absl::WrapUnique(new WritableFileWrapper(std::move(file)));
  }

 private:
  explicit WritableFileWrapper(std::unique_ptr<tsl::WritableFile> file)
      : file_(std::move(file)) {
    SetBuffered();
  }

  uint64_t current_pos() const override {
    int64_t position;
    if (file_->Tell(&position).ok()) {

namespace mlir {
namespace TFL {
namespace {
#define GEN_PASS_DEF_OPTIMIZEOPORDERPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

// Dequantize ops will produce 3x larger tensors, so we want to move it after
// some passthrough ops to reduce the memory consumption.
struct PushDownDequantize : public OpRewritePattern<DequantizeOp> {
  explicit PushDownDequantize(MLIRContext* context)

TEXT ·Or8(SB), NOSPLIT, $0-5
	MOVL	ptr+0(FP), AX
	MOVB	val+4(FP), BX
	LOCK
	ORB	BX, (AX)
	RET

// void	·And8(byte volatile*, byte);
TEXT ·And8(SB), NOSPLIT, $0-5
	MOVL	ptr+0(FP), AX
	MOVB	val+4(FP), BX
	LOCK
	ANDB	BX, (AX)
	RET

TEXT ·Store8(SB), NOSPLIT, $0-5
	MOVL	ptr+0(FP), BX
	MOVB	val+4(FP), AX
	XCHGB	AX, 0(BX)
	RET

// func Or(addr *uint32, v uint32)

        return hashCode;
    }

    @Override
    public String getHashCode() {
        return hashCode.toString();
    }

    // TODO Provide default implementation
    // TODO Deprecate and move this to BuildCacheKeyInternal
    @Override
    public byte[] toByteArray() {
        return hashCode.toByteArray();
    }

    // TODO Provide default implementation
    @Override
    @Deprecated

  opt_options.flib_def = &flib_def;

  BuildXlaOpsPass pass(/*enable_lazy_compilation=*/true);
  TF_RETURN_IF_ERROR(pass.Run(opt_options));
  VLOG(3) << graph->ToGraphDefDebug().DebugString();
  *result = std::move(graph);
  return absl::OkStatus();
}

Status MakeXlaCompiledKernel(Graph* graph, const string& callee_name,
                             const string& node_name, int num_constant_args,

  StablehloToOdmlTypeConverter converter;
  RewritePatternSet patterns(context);
  stablehlo::populateStablehloToVhloPatterns(&patterns, &converter, context);

  if (failed(applyPartialConversion(module, target, std::move(patterns)))) {
    return module->emitError("Failed partial conversion to VHLO");
  }
  return success();
}

LogicalResult ApplyVhloToVersionPatterns(ModuleOp module,

==============================================================================*/

include "mlir/IR/OpBase.td"
include "mlir/IR/PatternBase.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"

// TODO(jpienaar): Move this somewhere general.
class GetI64ScalarElementsAttr<int value> :
  NativeCodeCall<"DenseElementsAttr::get<int64_t>(RankedTensorType::get({}, $_builder.getIntegerType(64)), " # value # ")">;

def FuseMapAndBatch : Pat<

  TF_RETURN_IF_ERROR(ReadSavedModel(export_dir, &saved_model_proto));
  TF_ASSIGN_OR_RETURN(MetaGraphDef * m,
                      FindMetaGraphDef(tags, &saved_model_proto));
  *meta_graph_def = std::move(*m);
  return absl::OkStatus();
}

Status ReadSavedModelDebugInfoIfPresent(
    absl::string_view export_dir,
    std::unique_ptr<GraphDebugInfo>* debug_info_proto) {

    }

    @ServiceScope(Scope.BuildTree)
    static class BuildTreeScopedService {}