#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.

    // expected-warning @+1 {{TF/XLA TPU bridge input check: found invalid op. tf.Identity can't be both xla and non-xla}}
    %out, %c1 = tf_executor.island(%c0) wraps "tf.Identity"(%ri) {_tpu_replicate = "cluster", device = ""} : (tensor<*x!tf_type.string>) -> tensor<*x!tf_type.string>

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;

**Linux XLA**                 | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.html)         | TBA

#include "tensorflow/c/experimental/stream_executor/stream_executor_internal.h"
#include "tensorflow/c/experimental/stream_executor/stream_executor_test_util.h"
#include "xla/stream_executor/event.h"
#include "xla/stream_executor/platform_manager.h"
#include "xla/stream_executor/stream.h"
#include "xla/stream_executor/stream_executor.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"

#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_saved_model_passes.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/error_util.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"
#include "tensorflow/core/public/session.h"

namespace tensorflow {
namespace quantization {

using ::mlir::quant::stablehlo::AddXlaCallModuleOpDeserializationPasses;

          in (quant_opts_pb2.OpSet.XLA, quant_opts_pb2.OpSet.STABLEHLO)
          and quantization_options.quantization_method.preset_method
          == _PresetMethod.METHOD_STATIC_RANGE_INT8
      )
  ):
    raise ValueError(
        'Currently, per-channel quantization is supported for Uniform Quantized'
        ' opset, weight only quantization, or XLA/StableHLO opset with static'
        ' range quantization.'