#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/analysis/resource_dataflow.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"

namespace mlir {
namespace TF {

namespace {

#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

//===----------------------------------------------------------------------===//
// The post-quantize Passes.
//
namespace mlir {
namespace quant {
namespace {

#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/passes.h"  // IWYU pragma: keep
#include "tensorflow/compiler/mlir/quantization/stablehlo/utils/bfloat16_type.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir::quant::stablehlo {

absl::StatusOr<std::string> ConvertSerializedStableHloModuleToBfloat16(
    const StringRef serialized_stablehlo_module) {

#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_config.pb.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir::quant {

// This attribute will be set for functions created by this pass.
// Presence of this attribute will mark the function as quantization target.

#include "mlir/Transforms/RegionUtils.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/analysis/resource_value_typed_analyzer.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"

namespace mlir {
namespace tf_saved_model {
namespace {

#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_device.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/attribute_utils.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

#define DEBUG_TYPE "tf-tpu-annotate-dynamic-shape-inputs"

namespace mlir {
namespace TFTPU {

#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"  // IWYU pragma: keep
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"  // IWYU pragma: keep
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir {
namespace odml {
namespace {

// This file is generated from `passes.td` and provides the implementation base
// class.

	// Score each call site.
	var resultNameTab map[*ir.Name]resultPropAndCS
	for _, cs := range csl {
		var cprops *FuncProps
		fihcprops := false
		desercprops := false
		if funcInlHeur, ok := fpmap[cs.Callee]; ok {
			cprops = funcInlHeur.props
			fihcprops = true
		} else if cs.Callee.Inl != nil {
			cprops = DeserializeFromString(cs.Callee.Inl.Properties)
			desercprops = true
		} else {

#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_attributes.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/core/platform/test.h"

namespace mlir {
namespace TFL {
namespace {

template <int NInput, int NOutput>
func::FuncOp createMaxUnpoolingFunc(

#include "stablehlo/dialect/Register.h"  // from @stablehlo
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/stablehlo_util.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/lower_tf.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf_with_tf2xla_passes.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/passes.h"