let description = [{
This operation holds a replicated output from a `tpu.replicate()` computation subgraph.
Each replicated output has the same shape and type alongside the input.

For example:
```
%computation = "tf.Computation"()
%replicated_output:2 = "tf.TPUReplicatedOutput"(%computation)
```

  %1 = "tf.ReadVariableOp"(%0) : (tensor<!tf_type.resource<tensor<i32>>>) -> tensor<i32>
  // CHECK:      [[COMPUTATION:%.+]] = "tf_device.cluster_func"([[INPUT]])
  %2 = "tf_device.cluster_func"(%1) {func = @computation, use_spmd_for_xla_partitioning = true} : (tensor<i32>) -> tensor<i32>
  // CHECK:      [[OUTPUT:%.+]]:2 = "tf.TPUPartitionedOutputV2"([[COMPUTATION]])
  // CHECK-SAME: _XlaSharding = ""
  // CHECK-SAME: partition_dims = []

// clang-format on

namespace tensorflow {
namespace tfcompile {
namespace {

void zero_buffers(XlaCompiledCpuFunction* computation) {
  for (int i = 0; i < computation->num_args(); ++i) {
    memset(computation->arg_data(i), 0, computation->arg_size(i));
  }
}

// Trivial test that runs the generated function to ensure it doesn't crash.
TEST(TEST_NAME, NoCrash) {

  return true;
}

namespace {

// Compiles the XLA computation into executable code.
Status CompileXla(xla::CompileOnlyClient* client,
                  const xla::XlaComputation& computation,
                  const xla::cpu::CpuAotCompilationOptions& aot_opts,
                  CompileResult* compile_result) {
  // Retrieves arg and result layouts from the computation.
  // TODO(toddw): Should we let the user choose the major/minor ordering?

  xla::Tuple(&builder, tuple_values);

  TF_ASSERT_OK_AND_ASSIGN(XlaComputation computation, builder.Build());
  EXPECT_EQ(computation.proto().computations_size(), 2);

  TF_ASSERT_OK(CreateMlirModule());
  TF_ASSERT_OK_AND_ASSIGN(TupleOp root_tuple,
                          ImportXlaComputationIntoModule(computation));
  EXPECT_TRUE(root_tuple);

  int num_func_ops = 0;

/**
 * Starts and exposes the given suspending [computation] as a [Future] value.
 *
 * The [computation] executes synchronously until its first suspension point.
 */
internal
fun <T> future(context: CoroutineContext = EmptyCoroutineContext, computation: suspend () -> T): Future<T> =
    FutureContinuation<T>(context).also { k ->
        computation.startCoroutine(completion = k)
    }


private

      /*lower_to_xla_hlo=*/true, computation, metadata, device_type,
      shape_determination_fns, use_tuple_args, compilation_result,
      custom_legalization_passes, arg_shapes, arg_core_mapping,
      per_core_arg_shapes);

  if (mlir_bridge_status.ok()) {
    VLOG(1) << "Successfully compiled MLIR computation to XLA HLO using MLIR "
               "tf2xla bridge";

//
// References:
//
// Baillie and Wagstaff, "Lucas Pseudoprimes", Mathematics of Computation 35(152),
// October 1980, pp. 1391-1417, especially page 1401.
// https://www.ams.org/journals/mcom/1980-35-152/S0025-5718-1980-0583518-6/S0025-5718-1980-0583518-6.pdf
//
// Grantham, "Frobenius Pseudoprimes", Mathematics of Computation 70(234),
// March 2000, pp. 873-891.

  Eigen::ThreadPool pool(1 /* num_threads */);
  Eigen::ThreadPoolDevice device(&pool, pool.NumThreads());

  CPP_CLASS computation;
  computation.set_thread_pool(&device);

  benchmark::Options options;
  benchmark::Stats stats;
  benchmark::Benchmark(options, [&] { computation.Run(); }, &stats);
  benchmark::DumpStatsToStdout(stats);
  return 0;
}

}  // namespace tfcompile
}  // namespace tensorflow

    }
}

kotlin {
    target.compilations.named("testFixtures") {
        associateWith(target.compilations["main"])
    }
    target.compilations.named("test") {
        associateWith(target.compilations["main"])
        associateWith(target.compilations["testFixtures"])
    }
    target.compilations.named("integTest") {
        associateWith(target.compilations["main"])