"@local_tsl//tsl/platform:error_logging",
        "@local_tsl//tsl/platform:errors",
        "@local_xla//xla:xla_data_proto_cc",
        "@local_xla//xla:xla_proto_cc",
        "@local_xla//xla/mlir_hlo",
    ],
)

cc_library(
    name = "tpu_variable_runtime_reformatting",
    srcs = ["tpu_variable_runtime_reformatting.cc"],
    hdrs = [
        "runtime_passes.h",
    ],
    deps = [

#include "tensorflow/compiler/mlir/tensorflow/utils/mangling_util.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/utils.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "xla/translate/hlo_to_mhlo/attribute_importer.h"
#include "xla/xla_data.pb.h"
#include "tensorflow/core/framework/numeric_types.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"

#include "tensorflow/compiler/mlir/tensorflow/utils/xla_rewrite_util.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/xla_sharding_util.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "xla/xla.pb.h"
#include "xla/xla_data.pb.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.pb.h"

      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};

  Option<QuantMethod> quantization_method_{

}

def XlaBroadcastPass : Pass<"tf-xla-broadcast", "mlir::func::FuncOp"> {
  let summary = "Moves a broadcast from host into XLA, encoded as XlaAllReduce";

  let description = [{
    This pass moves brodcasts from host TF ops into XLA. This enables use of
    the inter-device network, which is faster than the inter-host network.
    Broadcasts in XLA are encoded as XlaAllReduce. An all_reduce with all 0

        "@llvm-project//mlir:FuncDialect",
        "@llvm-project//mlir:Pass",
        "@llvm-project//mlir:ReconcileUnrealizedCasts",
        "@llvm-project//mlir:Transforms",
        "@local_xla//xla/mlir_hlo:mhlo_passes",
    ],
)

cc_library(
    name = "pre_calibration",
    srcs = ["pre_calibration.cc"],
    hdrs = ["pre_calibration.h"],
    compatible_with = get_compatible_with_portable(),

#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"

#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>(