\
  REGISTER_KERNEL_BUILDER(                                                     \
      Name("VarHandleOp").Device(DEVICE).HostMemory("resource"), VarHandleOp); \
  REGISTER_KERNEL_BUILDER(                                                     \
      Name("_VarHandlesOp").Device(DEVICE).HostMemory("resources"),            \

   *
   * @since 8.0
   */
  public static final byte DLE = 16;

  /**
   * Device Control 1. Characters for the control of ancillary devices associated with data
   * processing or telecommunication systems, more especially switching devices "on" or "off." (If a
   * single "stop" control is required to interrupt or turn off ancillary devices, DC4 is the
   * preferred assignment.)
   *
   * @since 8.0
   */

// "replicate_on_last_tile_dim" and "last_tile_dims" should be deducted from the
// real Tensor dimensions when tiled.
// For example:
// f32[8,512](sharding={devices=[1,1,2]0,1 last_tile_dims={REPLICATED})
// also means a replicated tensor over all devices.
//
// See xla_data.proto for detailed explanations on the fields.
int GetDimsFromXLAShardingTiled(const xla::OpSharding& xla_sharding);

  LoggingDevice* device = new LoggingDevice;
  device->arrived_flag = arrived_flag;
  device->executed_flag = executed_flag;
  device->device_name = name;
  device->underlying_device = "/job:localhost/replica:0/task:0/device:CPU:0";
  device->strict_scope_placement = strict_scope_placement;
  TFE_RegisterCustomDevice(context, custom_device, name, device, status);
}

    // CHECK-SAME: <{device = "tpu0"}>
    // CHECK: %[[B_OUTPUT:[0-9]*]] = "tf.B"(%[[A_OUTPUT]]) : (tensor<?xi32>) -> tensor<?xi32>
    %3 = "tf.B"(%2) {device = "tpu0"} : (tensor<?xi32>) -> tensor<?xi32>

    // CHECK: %[[C_OUTPUT:[0-9]*]] = "tf.C"(%[[A_OUTPUT]], %[[B_OUTPUT]]) : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
    %4 = "tf.C"(%2, %3) {device = "tpu0"} : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>

func.func @main(%arg0: tensor<!tf_type.resource> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg1: tensor<!tf_type.variant> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg2: tensor<!tf_type.resource> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg3: tensor<!tf_type.variant> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg4: tensor<!tf_type.resource<tensor<7x7x3x64xf32>>> {tf.device = "/job:localhost/replica:0/task:0/device:TPU:0"},...

}

void FakeSession::BuildDeviceManager() {
  auto device =
      tensorflow::DeviceFactory::NewDevice("CPU", {}, kDeviceNamePrefix);
  device_mgr_ =
      std::make_unique<tensorflow::StaticDeviceMgr>(std::move(device));
}

void FakeSession::InitVariables() {
  tensorflow::Device* device = nullptr;
  auto status = device_mgr_->LookupDevice(kDeviceName, &device);
  if (status != absl::OkStatus()) return;

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-resource-device-inference %s | FileCheck %s

!tf_res = tensor<*x!tf_type.resource<tensor<32xf32>>>

// Tests that the pass can correctly propagate device attributes inside the same
// function.

// CHECK-LABEL: func @propagate_in_function
func.func @propagate_in_function(
  %arg0: !tf_res {tf.device = "/TPU:0"},
  %arg1: !tf_res {tf.device = "/TPU:1"}) {
  tf_executor.graph {

// RUN: tac-opt-all-backends -tfl-device-transform-gpu %s -split-input-file -verify-diagnostics | FileCheck %s

func.func @pack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<2x1xf32> {
  %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
  func.return %0 : tensor<2x1xf32>
}

// CHECK:   func @pack(%[[VAL_0:.*]]: tensor<1xf32>, %[[VAL_1:.*]]: tensor<1xf32>) -> tensor<2x1xf32> {

       const string& op_name);
  Impl(const Scope& other, Tags::ControlDeps,
       std::vector<Operation> control_deps, bool clear_control_deps);
  Impl(const Scope& other, Tags::Device, const string& device);
  Impl(const Scope& other, Tags::SingleUseScope, const string& op_name);
  Impl(const Scope& other, Tags::ExitOnError);
  Impl(const Scope& other, Tags::KernelLabel, const string& kernel_label);