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

// 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";

= "/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"});

    unified attributes. For example, `_tpu_replicate="cluster"` in the
    following code

    ```mlir

    DefaultValuedOptionalAttr<StrArrayAttr, "{}">:$padding_map,
    DefaultValuedOptionalAttr<StrAttr, "\"STEP_MARK_AT_ENTRY\"">:$step_marker_location,
    DefaultValuedOptionalAttr<BoolAttr, "false">:$allow_soft_placement,
    DefaultValuedOptionalAttr<BoolAttr, "false">:$use_spmd_for_xla_partitioning,
    DefaultValuedOptionalAttr<StrAttr, "\"\"">:$tpu_compile_options_proto
  );

  let results = (outs);
}