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,

    tf_executor.fetch
  }
  func.return
}
// CHECK: "tf.opA"
// device = "/TPU:0"
// CHECK: _parallel_execution_ids = "r0:0"
// CHECK: "tf.opA"
// device = "/TPU:0"
// CHECK: _parallel_execution_ids = "r0:1"
// CHECK: "tf.opA"
// device = "/TPU:1"
// CHECK: _parallel_execution_ids = "r1:0"
// CHECK: "tf.opA"
// device = "/TPU:1"

#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

# bazel test invocation as normal.
test:nonpip_filters --test_tag_filters=-no_oss,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-no_aarch64,-no_oss_py38,-no_oss_py39,-no_oss_py310
test:nonpip_filters --build_tag_filters=-no_oss,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-no_aarch64,-no_oss_py38,-no_oss_py39,-no_oss_py310

  %6 = "tf.TPUExecute"(%3#1, %1, %program#1) {_producer_name = "UNKNOWN", device = "/TPU:1"} : (tensor<1x1xf32>, tensor<1x1xf32>, tensor<3x!tf_type.string>) -> tensor<1x1xf32>

// RUN: tf-opt %s -tf-executor-tpu-v1-island-coarsening | FileCheck %s


// Test that islands with a function call are merged if the call is to a function
// that contains ops with the same attribute.
// CHECK-LABEL: func @control_input
func.func @control_input(%arg0 : tensor<i1>) -> tensor<i32> {
  %0:6 = tf_executor.graph {

# See the License for the specific language governing permissions and
# limitations under the License.

set -o errexit
set -o nounset
set -o pipefail

red=$(tput setaf 1)
reset=$(tput sgr0)
readonly red reset

KUBE_ROOT=$(dirname "${BASH_SOURCE[0]}")/../..
ALL_TARGETS=$(make -C "${KUBE_ROOT}" PRINT_HELP=y -rpn | sed -n -e '/^$/ { n ; /^[^ .#][^ ]*:/ { s/:.*$// ; p ; } ; }' | sort)

  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(