// CHECK-LABEL: @converts_cluster
  func.func @converts_cluster() {
    // CHECK: "tf.XlaLaunch"()

  let description = [{
    Verifies the input before exporting to TF executor. This includes checking whether the Ops are in TF functional, have device attributes & there are no tf_device.cluster_func ops.
  }];
  let constructor = "tensorflow::tf2xla::internal::CreateVerifyInputDialectToExecutorPass()";

// children except for the child with the ClusterFunc.
// Returns the index of the child with the ClusterFunc.
int MovePreservedParallelExecuteChildren(
    int num_cores_per_replica,
    llvm::SmallVector<mlir::Type, 8>& concatenated_output_types,
    mlir::OpBuilder* builder, mlir::tf_device::ClusterFuncOp cluster_func,
    mlir::tf_device::ParallelExecuteOp old_parallel_execute,

Bubbles up sharding configuration from `cluster_func` regions into
the attributes of `cluster_func`. This is done by parsing the
`XlaSharding` / `TPUPartitionedOutput` / `TPUPartitionedInput` ops inside
`cluster_func`.

For example, given the following `cluster_func` wrapping `func`:

```mlir
  func @test(%arg0: tensor<*xi32>) {
    "tf_device.cluster_func"(%arg0) {
        func = @func,

// exportable and verify some properties of the Tensorflow Executor MLIR that
// are required by Graph Export. It will return an error if it cannot.
//
// Input: A MLIR Module in the Tensorflow Dialect with no
// `tf_device.cluster_func` ops.
// Output: A MLIR module in the Tensorflow Executor Dialect.
tensorflow::Status ExportFromTensorflowDialectToExecutor(
    mlir::ModuleOp module, llvm::StringRef module_name = llvm::StringRef());

}  // namespace v2

            tf_device.return %4#0, %4#1 : tensor<2048xi32>, tensor<2048xi32>
            }) {device = "TPU_REPLICATED_HOST_0"} : () -> (tensor<2048xi32>, tensor<2048xi32>)
      %1 = "tf_device.cluster_func"(%0#0, %0#1) {_replication_info = "cluster_test_fn", func = @tpu_func} : (tensor<2048xi32>, tensor<2048xi32>) -> tensor<2048xi32>
      return %1: tensor<2048xi32>
   }
   // CHECK-LABEL: func @tpu_func

// exportable and verify some properties of the Tensorflow Executor MLIR that
// are required by Graph Export. It will return an error if it cannot.
//
// Input: A MLIR Module in the Tensorflow Dialect with no
// `tf_device.cluster_func` ops.
// Output: A MLIR module in the Tensorflow Executor Dialect.

ABSL_DEPRECATED(
    "Use v2/tf_dialect_to_executor.h::ExportFromTensorflowDialectToExecutor "
    "instead.")

}

void TPUAnnotateDynamicShapeInputsPass::runOnOperation() {
  getOperation().walk([&](tf_device::ClusterFuncOp cluster_func_op) {
    Builder builder(cluster_func_op->getContext());
    // Skip non-tpu device cluster_func.
    auto cluster_id =
        cluster_func_op->getAttrOfType<StringAttr>(TF::kReplicationInfoAttr);
    if (!cluster_id) return WalkResult::advance();

    llvm::SmallVector<int, 4> dynamic_shape_arg_index;

  }];

  let hasCanonicalizer = 1;
}

def TfDevice_ClusterFuncOp : TfDevice_Op<"cluster_func",
    [CallOpInterface, DeclareOpInterfaceMethods<SymbolUserOpInterface>]> {
  let summary = [{
The `tf_device.cluster_func` launches a function containing the body of a
cluster.
  }];

  let description = [{
This op is used for outlining a cluster.
  }];

  ReplaceClusterReturnWithReturn(return_op, builder);

  symbol_table->insert(outlined_func);
  return outlined_func;
}

// Outlines body of `tf_device.cluster` into a function and create a
// `tf_device.cluster_func` to invoke that function. `tf_device.cluster` is
// removed afterwards.`
void OutlineCluster(tf_device::ClusterOp cluster_op, SymbolTable* symbol_table,
                    OpBuilder* builder) {