func.func @check_sharding_attrs_exists_for_empty_cluster_func() {
  "tf_device.cluster_func"() {func = @empty_func, step_marker_location = "", num_cores_per_replica = 1 : i64} : () -> ()
  // CHECK: input_sharding_configuration = []
  // CHECK: output_sharding_configuration = []
  func.return
}

// CHECK-LABEL: func @empty_func() {
func.func @empty_func() {
  func.return
}

// -----

    func.return
  }
  func.func @empty_func() {
    func.return
  }
}

// -----

// Tests `tf_device.cluster_func` with missing `input_sharding_configuration` attribute.

    "tf_device.cluster_func"() {_xla_compile_device_type = "TPU", _replication_info = "cluster0", func = @empty_func, num_cores_per_replica = 1, step_marker_location = "", topology = "", device_assignment = [], input_sharding_configuration = [], output_sharding_configuration = [], use_spmd_for_xla_partitioning = false} : () -> ()
    func.return
  }
  func.func @empty_func() {
    func.return
  }

}

// -----

  %outputs  =  "tf.TPUCompilationResult"() {_tpu_compilation_status = "cluster", device = ""} : () -> tensor<!tf_type.string>

  func.func @main() {
    "tf_device.cluster_func"() {_xla_compile_device_type = "TPU", _replication_info = "cluster0", func = @empty_func, num_cores_per_replica = 1, step_marker_location = "", topology = "", device_assignment = [], input_sharding_configuration = [], output_sharding_configuration = [], use_spmd_for_xla_partitioning = false} : () -> ()
    func.return
  }
  func.func @empty_func() {
    func.return
  }

@empty_func}> {_dynamic_arg_index = [], _replication_info = "cluster", _xla_compile_device_type = "TPU", allow_soft_placement = false, computation_shape = [], device = "", device_assignment = [0, 0, 0, 0, 0, 0, 0, 1], host_compute_core = [], input_sharding_configuration = ["{devices=[2,1]0,1}"], num_cores_per_replica = 2 : i64, output_sharding_configuration = [""], padding_map = [], step_marker_location = "STEP_MARK_AT_ENTRY", topology = "\0A\04\02\02\01\02\10\01\18\08\22 \00\00\00\00\00\00\00\01\01\0...

    "tf_device.cluster_func"() {_xla_compile_device_type = "TPU", _replication_info = "cluster0", func = @empty_func, step_marker_location = "STEP_MARK_AT_TOP_LEVEL_WHILE_LOOP", topology = "", device_assignment = [], input_sharding_configuration = [], output_sharding_configuration = [], use_spmd_for_xla_partitioning = false} : () -> ()
    func.return
  }
  func.func @empty_func() {
    func.return
  }

    "tf_device.cluster_func"() {_xla_compile_device_type = "GPU", _replication_info = "cluster0", func = @empty_func, num_cores_per_replica = 1, step_marker_location = "", topology = "", device_assignment = [], input_sharding_configuration = [], output_sharding_configuration = [], use_spmd_for_xla_partitioning = false} : () -> ()
    func.return
  }
  func.func @empty_func() {
    func.return
  }

#include "xla/xla_data.pb.h"
#include "tensorflow/core/protobuf/tpu/compile_metadata.pb.h"

namespace tensorflow {

inline constexpr llvm::StringRef kInputShardingAttr =
    "input_sharding_configuration";
inline constexpr llvm::StringRef kOutputShardingAttr =
    "output_sharding_configuration";

// Parses the sharding string. This sharding string can be binary (serialized)
// or human readable.

    func @tf_tpu_rewrite(%arg0: tensor<8xi32>) -> tensor<8xi32> {
      %0 = "tf_device.cluster_func"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster0", func = @func, num_cores_per_replica = 2, input_sharding_configuration = ["\08\01\1A\01\01\22\01\00"], output_sharding_configuration = ["\08\01\1A\01\01\22\01\00"]} : (tensor<8xi32>) -> tensor<8xi32>
      return %0 : tensor<8xi32>
    }
    ```

    will be rewritten as: