%1 = "tf.UnsupportedOp"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    %2 = "tf.Identity"(%1) : (tensor<i32>) -> tensor<i32>
    tf_device.return %2 : tensor<i32>
  }) {allow_soft_placement = false, num_cores_per_replica = 1, topology =  "", device_assignment =  []} : () -> tensor<i32>
  func.return %0 : tensor<i32>
}

// -----

// CHECK-LABEL: func @assert_op_string_operand

    %control_4 = tf_executor.island(%control_2) wraps "tf.TPUReplicateMetadata"() {_replication_info = "cluster", _xla_compile_device_type = "TPU", allow_soft_placement = false, computation_shape = [], device = "", device_assignment = [], host_compute_core = [], num_cores_per_replica = 1 : i64, num_replicas = 1 : i64, padding_map = [], step_marker_location = "STEP_MARK_AT_ENTRY", topology = "", use_spmd_for_xla_partitioning...

  // CHECK-NEXT:     "tf.OpA"(%[[REDUCED]]) : (tensor<f32>) -> ()
  tf_device.replicate {n = 4 : i32} {
    "tf_device.cluster"() ({
      "tf.OpA"(%arg0) : (tensor<f32>) -> ()
      tf_device.return

      %0 = "tf_device.cluster"() ( {
        %1 = "tf.UnsupportedOp"() : () -> tensor<i32>
        %2 = "tf.Identity"(%1) : (tensor<i32>) -> tensor<i32>
        tf_device.return %2 : tensor<i32>
      }) {allow_soft_placement = true, num_cores_per_replica = 1, topology =  "", device_assignment =  []} : () -> tensor<i32>
      return %0 : tensor<i32>
    }
    ```

// Whether soft placement is allowed. If true, the marked node is eligible for
// outside compilation.
inline constexpr llvm::StringRef kAllowSoftPlacementAttr =
    "allow_soft_placement";

// Marks a node for XLA compilation. The attribute value indicates the
// compilation device type.
inline constexpr llvm::StringRef kCompileDeviceTypeAttr =
    "_xla_compile_device_type";

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1610 : i32}} {
    func.func @main(%arg0: tensor<3x3x10xbf16>, %arg1: tensor<3xi32>) -> tensor<1x?x4xbf16> attributes {allow_soft_placement = false, tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2", outputs = "_retval0"}} {
      %cst = "tf.Const"() {value = dense<[1, -1, 4]> : tensor<3xi32>} : () -> tensor<3xi32>

= "/job:tpu_host_worker/replica:0/task:0/device:CPU:0"}, %arg21: tensor<*x!tf_type.resource<tensor<1024x1xf32>>> {tf._user_specified_name = "939", tf.device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0"}) -> tensor<i64> attributes {allow_soft_placement = false, tf.entry_function = {control_outputs = "", inputs = "steps,unknown,unknown_0,unknown_1,unknown_2,unknown_3,unknown_4,unknown_5,unknown_6,unknown_7,unknown_8,unknown_9,unknown_10,unknown_11,unknown_12,unknown_13,unknown_14,unknown_...

  import_config.enable_shape_inference = false;
  import_config.xla_compile_device_type =
      function_options.xla_compile_device_type;
  import_config.enable_soft_placement = function_options.allow_soft_placement;

  static const char* kTfMlirCategory = "TfMlir";
  tensorflow::metrics::ScopedCounter<2> timings(
      tensorflow::metrics::GetGraphOptimizationCounter(),
      {kTfMlirCategory, "convert_graph_to_mlir"});

  // CHECK-NEXT:   "tf.NoOp"
  // CHECK-NEXT:   tf_device.return

  AddRewrittenEmbeddingOps(module.getContext(), &supported_ops);
  AddRewrittenCompositeOps(module.getContext(), &supported_ops);

  auto result = module.walk([&](mlir::tf_device::ClusterOp cluster) {
    // Only if `allow_soft_placement` attribute is true should we mark ops
    // for outside compilation.
    auto soft_placement_attr =
        cluster->getAttrOfType<BoolAttr>(mlir::TF::kAllowSoftPlacementAttr);