}
  absl::Status s;
  const char* device_name = DeviceName(&s);
  if (!s.ok()) {
    device_name = "<error fetching device name>";
  }
  return absl::StrCat("TensorHandle(", value_string, ", shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()), ", device=\"",
                      device_name, "\")");
}

absl::Status ImmediateExecutionTensorHandle::SummarizeValue(

}
}  // namespace

extern "C" {

bool TFE_IsCustomDevice(TFE_Context* ctx, const char* device_name) {
  return tensorflow::unwrap(ctx)->IsCustomDevice(device_name);
}

void TFE_RegisterCustomDevice(TFE_Context* ctx, TFE_CustomDevice device,
                              const char* device_name, void* device_info,
                              TF_Status* status) {

                      std::unique_ptr<ParallelDevice> parallel_device)
      : device_name_(name), parallel_device_(std::move(parallel_device)) {}
  const std::string& name() const { return device_name_; }
  const ParallelDevice& device() const { return *parallel_device_; }

 private:
  std::string device_name_;
  std::unique_ptr<ParallelDevice> parallel_device_;
};

    return absl::OkStatus();
  }
  const string& Name() const override { return op_type_; }
  const string& DeviceName() const override { return device_name_; }

  absl::Status SetDeviceName(const char* name) override {
    // TODO(srbs): Implement this.
    device_name_ = name;
    return absl::OkStatus();
  }

  absl::Status AddInput(AbstractTensorHandle* input) override {

      cpu_value.get(), context.get(), device_name, status.get()));
  EXPECT_EQ(TF_GetCode(status.get()), TF_UNIMPLEMENTED);

  std::array<TFE_TensorHandle*, 2> components{cpu_value.get(), cpu_value.get()};
  TensorHandlePtr device_value = CreatePerDeviceValues(
      context.get(), components, device_name, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  const char* second_device = "/job:worker/replica:0/task:2/device:CPU:0";
  const char* device_name = "/job:localhost/replica:0/task:0/device:CUSTOM:0";
  std::array<const char*, 2> underlying_devices{first_device, second_device};
  RegisterParallelDevice(context.get(), device_name, underlying_devices,
                         status.get());

  // returned by DeviceName should be "/device:GPU:*" until a particular GPU is
  // chosen for the operation by the device placement logic in the
  // executor. After that, the value returned by DeviceName will be a full
  // device name such as "/job:localhost/replica:0/task:0/device:GPU:1".
  virtual const string& DeviceName() const = 0;

  // Sets the operation device name.
  //

DLDevice GetDlContext(TFE_TensorHandle* h, TF_Status* status) {
  DLDevice ctx;
  const char* device_name =
      tensorflow::unwrap(h)->BackingDeviceName(&status->status);
  DeviceNameUtils::ParsedName parsed_name;
  tensorflow::DeviceNameUtils::ParseFullName(device_name, &parsed_name);
  std::string device_type = parsed_name.type;
  int device_id = 0;
  if (parsed_name.has_id) {

  //===--------------------------------------------------------------------===//
  virtual CustomDeviceOpHandler& GetCustomDeviceOpHandler() = 0;

  // Returns whether `device_name` is registered as a custom device.
  virtual bool IsCustomDevice(const string& device_name) = 0;

  // Register a custom device. It will return error is the device name is
  // already registered.

  TFE_TensorHandle* var_handle0 = TestVariable(ctx, 1.0, dev0_name);
  EXPECT_NE(var_handle0, nullptr);

  absl::Status status2;
  EXPECT_EQ(tensorflow::unwrap(var_handle0)->DeviceName(&status2), dev0_name);

  // Rename local device
  // This server def has the task index set to 0.
  string serialized = server_def.SerializeAsString();