return time.Duration(val) * time.Millisecond
}

func printHeader(writer io.Writer) {
	w := tabwriter.NewWriter(writer, 13, 1, 2, ' ', tabwriter.AlignRight)
	_, _ = fmt.Fprintf(w, "%40s\tTOTAL RPS\tERROR RPS\tP50 LATENCY\tP90 LATENCY\tP99 LATENCY\t\n", "WORKLOAD")
	_ = w.Flush()
}

func printMetrics(writer io.Writer, wm workloadMetrics) {
	w := tabwriter.NewWriter(writer, 13, 1, 2, ' ', tabwriter.AlignRight)

  return absl::OkStatus();
}

}  // namespace

absl::StatusOr<tensorflow::XlaCompilationResult> LegalizeMlirToHlo(
    const std::variant<tpu::MlirToHloArgs, tpu::FunctionToHloArgs>& computation,
    const tpu::TPUCompileMetadataProto& metadata, bool use_tuple_args,
    llvm::StringRef device_type,
    std::vector<std::unique_ptr<mlir::Pass>>& custom_legalization_passes,

#include "tensorflow/core/lib/monitoring/cell_reader.h"
#include "tensorflow/core/protobuf/tpu/compile_metadata.pb.h"
#include "tensorflow/core/tpu/kernels/tpu_compile_op_support.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {
namespace tf2xla {
namespace internal {
namespace {

using tpu::MlirToHloArgs;
using tpu::ShardingAndIndex;
using tpu::TPUCompileMetadataProto;

static constexpr char kMlirModuleStr[] = R"(

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/core/tpu/tpu_defs.h"

namespace mlir {
namespace quant {
namespace {

#include "tensorflow/compiler/mlir/quantization/tensorflow/passes/convert_tpu_model_to_cpu.inc"

// Convert a TPU model to be compatible on CPU by rewriting/removing TPU ops.
class ConvertTpuModelToCpuPass

  if (!op->hasAttr(TF::kTpuReplicateAttr)) {
    op->emitOpError("TF2XLA TPU bridge input check: " + errormsg() +
                    "missing _tpu_replicate attr");
    return false;
  }
  auto opattr = op->getAttr(TF::kTpuReplicateAttr);
  if (opattr != attr) {
    op->emitOpError("TF2XLA TPU bridge input check: " + errormsg() +
                    "invalid _tpu_replicate attr.")

        if (!exported_model.ok()) return exported_model.status();

        // Remove the `tpu` tag from the debug quantized saved model as it is
        // for CPU. Note the 'tpu' value should be the same as `TPU` defined in
        // tensorflow/python/saved_model/tag_constants.py.
        if (quantization_options.has_debugger_config()) {
          tags.erase("tpu");
        }
        py_function_library.SaveExportedModel(

    mlir::ArrayAttr device_assignment_attr);

// Finds the TPU compilation device and execution devices from `devices` for a
// TPU computation subgraph. Compilation device is determined from looking up
// all TPU_SYSTEM:0 devices and choosing the CPU device associated to the first
// TPU_SYSTEM device sorted lexicographically by replica and task. Execution
// devices are determined by looking up all TPU devices associated with each

    // expected-warning @+1 {{TF2XLA TPU bridge input check: cluster op = tf.opA with cluster = cluster has successor as non cluster op tf.opB}}
    %out2, %c2 = tf_executor.island wraps "tf.opB"(%out) : (tensor<i32>) -> tensor<i32>
    // expected-error @+1 {{tf.TPUReplicatedOutput' op TF2XLA TPU bridge input check: non-cluster op = tf.opB has invalid successor op = tf.TPUReplicatedOutput}}

#include "tensorflow/core/protobuf/tpu/compile_metadata.pb.h"
#include "tsl/platform/errors.h"

namespace tensorflow {
namespace tf2xla {
namespace internal {
namespace {

constexpr char kEntryFuncName[] = "main";

absl::Status SetupArguments(mlir::ModuleOp module,
                            std::vector<TensorShape>& arg_shapes,
                            tpu::TPUCompileMetadataProto& metadata_proto) {

    arg->set_dtype(dtype);
    // TODO(lyandy): Support other arg kinds.
    if (dtype == tensorflow::DT_RESOURCE)
      arg->set_kind(tensorflow::tpu::TPUCompileMetadataProto::Arg::VARIABLE);
    else
      arg->set_kind(tensorflow::tpu::TPUCompileMetadataProto::Arg::PARAMETER);

    // Populate argument shapes.
    *arg->mutable_shape() = tensorflow::TensorShapeProto();
    if (auto ranked_tensor_type =