*
     * <p>Global services are visible to these shared services, but not vice versa.</p>
     *
     * @see Scope.UserHome
     */
    void registerGradleUserHomeServices(ServiceRegistration registration);

    /**

 public:
  void SetDevice(const string& device_type) {
    auto device_factory = DeviceFactory::GetFactory(device_type);
    SessionOptions options;
    std::vector<std::unique_ptr<Device>> devices;
    Status s = device_factory->CreateDevices(
        options, "/job:worker/replica:0/task:0", &devices);
    device_ = std::move(devices[0]);

    AllocatorAttributes host_alloc_attr;

{% block metas %}
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
{% endblock %}

{% block styles %}
<style>
    table {
        color: #333;
        background: white;
        border: 1px solid grey;
        font-size: 12pt;
        border-collapse: collapse;
        width: 100%;
    }

    table thead th,
    table tfoot th {

  %output_1, %min_2, %max_3, %histogram_4 = "tf.CustomAggregator"(%0) <{calibration_method = 5 : i32, id = "keeping_id", num_bins = 32 : i32, max_percentile = 0.000000e+00...

	5:   "I/O error",
	6:   "no such device or address",
	7:   "arg list too long",
	8:   "exec format error",
	9:   "bad file number",
	10:  "no child processes",
	11:  "resource temporarily unavailable",
	12:  "not enough space",
	13:  "permission denied",
	14:  "bad address",
	15:  "block device required",
	16:  "device busy",
	17:  "file exists",
	18:  "cross-device link",

    compatible_with = get_compatible_with_portable(),
    flatc_args = [
        "--gen-mutable",
        "--gen-object-api",
    ],
    out_prefix = "mutable/",
)

# Generic schema for inference on device (but with reflections makes bigger).
flatbuffer_cc_library(
    name = "schema_fbs_with_reflection",
    srcs = ["schema.fbs"],
    compatible_with = get_compatible_with_portable(),
    flatc_args = [

#include "tensorflow/compiler/tf2xla/layout_util.h"
#include "tensorflow/compiler/tf2xla/xla_argument.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/client/xla_computation.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/framework/graph_debug_info.pb.h"
#include "tensorflow/core/framework/tensor_shape.h"

namespace tensorflow {

// Lowers MLIR module to XLA HLO inside an XlaComputation. The input module

  // CHECK-NEXT: tf_device.replicate([%arg0, %[[FULL_0]], %[[FULL_1]], %[[FULL_0]]] as %[[REPVAR:.*]]: tensor<f32>) {n = 4 : i32} {
  // CHECK-NEXT:   %[[ID:.*]] = "tf_device.launch"() <{device = "TPU_REPLICATED_HOST_0"}> ({
  // CHECK-NEXT:     %[[IDINSIDE:.*]] = "tf.Identity"(%[[REPVAR]]) {ici_weight_distribution_mlir_bridge_marker = true} : (tensor<f32>) -> tensor<f32>

    %5 = "quantfork.qcast"(%4) {volatile} : (tensor<1x3xf32>)...

  const tensorflow::DeviceMgr* mgr = nullptr;
  auto status = session->LocalDeviceManager(&mgr);
  if (!status.ok()) {
    module->emitError(
        absl::StrCat("failed to fetch device manager: ", status.message()));
    return failure();
  }

  // Fetch all VarHandleOp.
  llvm::StringSet<> variable_names;
  llvm::SmallVector<TF::VarHandleOp, 4> var_ops;