t.scalar<tensorflow::ResourceHandle>()().set_device(kDeviceName);

      outputs->push_back(t);
    } else if (output_name == "var2") {
      Tensor t = Tensor(tensorflow::DT_RESOURCE, tensorflow::TensorShape({1}));
      t.scalar<tensorflow::ResourceHandle>()().set_name("var2");
      t.scalar<tensorflow::ResourceHandle>()().set_device(kDeviceName);

      outputs->push_back(t);

  auto& test_params = GetParam();
  DataType dtype = std::get<0>(test_params);
  TensorShape shape(std::get<1>(test_params));

  SavedVariable saved_variable;
  saved_variable.set_dtype(dtype);
  saved_variable.set_device("/job:localhost/replica:0/task:0/device:CPU:1"),
      shape.AsProto(saved_variable.mutable_shape());

  std::unique_ptr<Variable> var;
  TF_ASSERT_OK(internal::LoadSavedVariable(context(), saved_variable, &var));

    if (otherDevice->device_) {
      return SetDevice(otherDevice->device_);
    }
    return ChangeResult::NoChange;
  }

  void print(raw_ostream &os) const override {
    if (device_) {
      os << "<" << device_ << ">";
    } else {
      os << "<no device>";
    }
  }

  ChangeResult SetDevice(mlir::StringAttr device) {
    bool changed = (device != device_);

#include "tensorflow/core/framework/tensor_testutil.h"
#include "tsl/lib/core/status_test_util.h"

namespace tensorflow {
namespace {

class XlaHostSendRecvDeviceContextTest : public ::testing::Test {
 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(

  tensorflow::GetXlaDeviceFlags()->tf_xla_enable_xla_devices = true;
  return true;
}();

class DeviceContextTest : public ::testing::Test {
 public:
  void SetDevice(const string& device_type) {
    auto& rollout_config = GetXlaOpsCommonFlags()->tf_xla_use_device_api;
    rollout_config.AllowForDeviceInXlaLaunch(DeviceType(device_type));

// `send_device`, `recv_device`, and `send_device_incarnation` set.
TFE_Op* SendOp(TFE_Context* ctx, TFE_TensorHandle* in,
               const std::string& op_name, const std::string& send_device,
               const std::string& recv_device,
               tensorflow::uint64 send_device_incarnation);

// Return a RecvOp op `op_name` with the attributes `send_device`,

      event_handle_ = nullptr;
    }
  }

  SP_Event Handle() { return event_handle_; }

 private:
  SP_Device* device_;
  SP_StreamExecutor* stream_executor_;
  SP_Event event_handle_;
};

class CStream : public StreamCommon {
 public:
  CStream(SP_Device* device, SP_StreamExecutor* stream_executor,
          StreamExecutor* executor)
      : StreamCommon(executor),
        device_(device),

TEST(PjrtCompileUtilTest, CompileToPjRtLoadedExecutable) {
  DeviceSetup device_setup;
  TF_ASSERT_OK_AND_ASSIGN(auto fdef, SampleFuntionAddXY("foo"));
  device_setup.AddDevicesAndSetUp({DEVICE_GPU}, fdef);

  Device* device = device_setup.GetDevice(DEVICE_GPU);
  const XlaPlatformInfo platform_info = XlaPlatformInfoFromDevice(device);

  NameAttrList function;
  function.set_name("foo");

  ResourceMgr resource_mgr("");

        return WalkResult::advance();
      } else if (device_str_attr.getValue() != launch.getDevice()) {
        return launch.emitOpError()
               << "inner op has conflicting 'device' attribute, "
                  "got '"
               << device_str_attr.getValue() << "' but expected '"
               << launch.getDevice() << "'";
      }
    } else {
      return launch.emitOpError()

  // Ensure that users of the launch op's results appear after the launch op
  // in order to preserve the dominance property.
  TF::ReorderOpResultUses(launch_op);
}

std::string GetDevice(Operation* op) {
  auto device_attr = op->getAttrOfType<StringAttr>("device");
  return device_attr ? device_attr.getValue().str() : "";
}

bool CanBeIgnoredInCluster(Operation* op) {