#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 {

#include "tensorflow/compiler/mlir/tosa/tf_tfl_passes.h"
#include "tensorflow/compiler/mlir/tosa/tfl_passes.h"
#include "tensorflow/compiler/mlir/tosa/transforms/passes.h"
#include "xla/mlir/framework/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"
#include "xla/status_macros.h"
#include "tensorflow/core/common_runtime/eager/context.h"
#include "tensorflow/core/common_runtime/function_body.h"

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/tf2xla/api/v1/compile_mlir_util.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/passes.h"
#include "xla/mlir/framework/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/IR/register.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/statusor.h"

// variables instead).
std::unique_ptr<OperationPass<ModuleOp>>
CreateRemoveVariableInitializationByConstPass();

// Creates a pass that converts Tensorflow Xla ops to non-Xla ops.
std::unique_ptr<OperationPass<func::FuncOp>> CreateConvertTfXlaOpToTfOpPass();

// Creates a pass that converts TPU models for CPU by removing TPU related ops

      return kTrFailure;
    }

    auto content = buffer->getBuffer();
    if (hlo_import_type == HloImportType::hlotxt) {
      module = xla::HloTextToMlirHloTranslateFunction(content, &context, false);
    } else if (hlo_import_type == HloImportType::proto) {
      module = xla::HloToMlirHloTranslateFunction(content, &context, false);
    } else {
      module = mlir::OwningOpRef<mlir::ModuleOp>(

        "@llvm-project//mlir:Transforms",
        "@local_tsl//tsl/platform:str_util",
        "@local_tsl//tsl/protobuf:protos_all_cc",
        "@local_xla//xla:xla_data_proto_cc",
        "@local_xla//xla/mlir_hlo",
        "@stablehlo//:chlo_ops",
    ],
    # Alwayslink is required for registering the MLIR passes.
    # TODO(b/255530126): Split the pass registration from the definitions to avoid binary size bloat.

#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 {

QuantizedType CalculateUniformQuantParams(
    PatternRewriter& rewriter, TF::ConstOp op,
    tensorflow::quantization::QuantizationComponentSpec& weight_spec) {
  // TODO - b/278949920: Enable Per-Channel Quantization for XLA Opset
  // Currently, support symmetric, per-tensor, signed int8
  const bool kIsNarrowRange = true;
  const bool kIsSigned = true;
  const int kBitWidth = 8;

  DenseFPElementsAttr attr;