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"))};

  // Initialize for tests.
  void initializeForTest() {

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/utils.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "xla/mlir_hlo/utils/convert_op_folder.h"
#include "xla/mlir_hlo/utils/hlo_utils.h"
#include "xla/xla_data.pb.h"

namespace mlir {
namespace mhlo {

namespace {

constexpr absl::string_view kGroupSizeAttrName =

#include "tensorflow/compiler/jit/tests/device_compiler_test_helper.h"

#include <string>

#include "absl/strings/match.h"
#include "tensorflow/compiler/jit/xla_compilation_cache.pb.h"
#include "xla/service/hlo.pb.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/public/session.h"

namespace tensorflow {
namespace {

#include "tensorflow/compiler/mlir/tensorflow/utils/mlprogram_util.h"
#include "tensorflow/compiler/mlir/tools/kernel_gen/ir/tf_framework_ops.h"
#include "xla/mlir/framework/ir/xla_framework.h"
#include "xla/mlir_hlo/mhlo/IR/register.h"

namespace mlir {

void RegisterCommonToolingDialects(mlir::DialectRegistry& registry) {
  mlir::RegisterAllTensorFlowDialects(registry);

            "@local_xla//xla/service:hlo_profile_printer_data_cc",
        ] or []) + (include_standard_runtime_deps and [
            # TODO(cwhipkey): only depend on kernel code that the model actually
            # needed.
            "@local_xla//xla/service/cpu:runtime_conv2d",
            "@local_xla//xla/service/cpu:runtime_custom_call_status",
            "@local_xla//xla/service/cpu:runtime_key_value_sort",

#include "tensorflow/compiler/mlir/quantization/tensorflow/passes/passes.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"

namespace tensorflow {
namespace quantization {
namespace {

using ::tensorflow::quantization::QuantizationOptions;

void AddConvertTpuToCpuModelPasses(mlir::OpPassManager &pm) {

      return errors::InvalidArgument(
          "Undeclared output of XLA computation. Some common causes of this "
          "error are: 1) variable initializers that depend on the XLA "
          "computation; 2) gradient computations that depend on the XLA "
          "computation, which can be mitigated by moving gradient computations "
          "inside XLA computation. Offending edge: ",

#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tf2xla/internal/test_matchers.h"
#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/shape.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/lib/monitoring/cell_reader.h"
#include "tensorflow/core/protobuf/tpu/compile_metadata.pb.h"

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

#ifndef TENSORFLOW_C_TF_STATUS_INTERNAL_H_
#define TENSORFLOW_C_TF_STATUS_INTERNAL_H_

#include "xla/tsl/c/tsl_status_internal.h"

typedef struct TSL_Status TF_Status;

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "xla/client/sharding_builder.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "xla/primitive_util.h"
#include "xla/side_effect_util.h"
#include "xla/translate/mhlo_to_hlo/type_to_shape.h"

namespace mlir {

using func::FuncOp;

namespace mhlo {

namespace {