#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/passes.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/xla_legalize_targets.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "tensorflow/core/lib/monitoring/counter.h"
#include "tensorflow/core/platform/errors.h"

namespace mlir {
namespace mhlo {

namespace {

limitations under the License.
==============================================================================*/

#include "tensorflow/c/tf_status_helper.h"

#include <string>

#include "tensorflow/c/tf_status.h"
#include "xla/tsl/c/tsl_status_helper.h"

namespace tsl {

void Set_TF_Status_from_Status(TF_Status* tf_status,
                               const absl::Status& status) {
  TF_SetStatus(tf_status, TSLCodeFromStatusCode(status.code()),

#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_config.pb.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/cc/run_passes.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"
#include "tsl/platform/errors.h"

namespace mlir::quant::stablehlo {

using ::stablehlo::quantization::GetReportFilePath;
using ::stablehlo::quantization::PipelineConfig;

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

#ifndef TENSORFLOW_COMPILER_JIT_SHAPE_INFERENCE_H_
#define TENSORFLOW_COMPILER_JIT_SHAPE_INFERENCE_H_

#include <map>
#include <vector>

#include "xla/statusor.h"
#include "tensorflow/core/common_runtime/optimization_registry.h"
#include "tensorflow/core/common_runtime/shape_refiner.h"
#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"  // IWYU pragma: keep
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir {
namespace odml {

LogicalResult ConvertCustomCallOp::matchAndRewrite(
    mhlo::CustomCallOp mhlo_custom_call, OpAdaptor adaptor,

class Pass;

namespace mhlo {

/// Converter to be used along with the fallback Tf2Xla patterns below.
class Tf2XlaTypeConverter : public TypeConverter {
 public:
  Tf2XlaTypeConverter();
};

/// Adds the TF to XLA via TF2XLA rewrite patterns to the pattern list.
/// `prefer_tf2xla` means an op will be included iff it is not in
/// `MlirLegalizedUnderPreferTf2XlaSet`. `!prefer_tf2xla` mean an op will be

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

namespace mlir {
namespace odml {

namespace {

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

// get access to said protocol buffers at runtime.

#ifndef TENSORFLOW_COMPILER_AOT_EMBEDDED_PROTOCOL_BUFFERS_H_
#define TENSORFLOW_COMPILER_AOT_EMBEDDED_PROTOCOL_BUFFERS_H_

#include "absl/types/span.h"
#include "xla/statusor.h"
#include "tensorflow/core/platform/protobuf.h"

namespace tensorflow {
namespace tfcompile {
using absl::StatusOr;

// Represents a set of protocol buffers embedded into an object file and

#include "mlir/IR/Visitors.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassRegistry.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir {
namespace odml {
namespace {

#define DEBUG_TYPE "unfold-splat-constant-pass"

#define GEN_PASS_DEF_UNFOLDSPLATCONSTANTPASS

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"  // IWYU pragma: keep
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"  // IWYU pragma: keep
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"  // IWYU pragma: keep

namespace mlir::quant::stablehlo::testing {

#define GEN_PASS_DEF_TESTPOSTCALIBRATIONCOMPONENTPASS