"Not enough storage is available to process this command.", "The media is write protected.", "The device is not ready.",
        "A device attached to the system is not functioning.", "A device attached to the system is not functioning.",
        "The process cannot access the file because it is being used by another process.",

        "The network name cannot be found.",
        "Not enough storage is available to process this command.",
        "The media is write protected.",
        "The device is not ready.",
        "A device attached to the system is not functioning.",
        "A device attached to the system is not functioning.",
        "The process cannot access the file because it is being used by another process.",

#else
    ctx.device_type = DLDeviceType::kDLCUDA;
#endif
  } else {
    status->status = tensorflow::errors::InvalidArgument(
        "Unsupported Device Type for dlpack");
  }

  return ctx;
}

// Converts DLDevice to TF device name.
absl::optional<std::string> DeviceNameFromDlContext(const DLDevice& ctx,
                                                    TF_Status* status) {

        return item

    @app.get("/items/")
    def read_items() -> List[Item]:
        return [
            Item(
                name="Portal Gun",
                description="Device to travel through the multi-rick-verse",
                sub=SubItem(subname="subname"),
            ),
            Item(name="Plumbus"),
        ]

    client = TestClient(app)
    return client

    }

    val expectedBucketSize = list.sumOf(toIntFunction) / expectedBucketNumber

    if (expectedBucketSize == 0) {
        // The elements in the list are so small that they can't even be divided into {expectedBucketNumber}.
        // For example, how do you split [0,0,0,0,0] into 3 buckets?
        // In this case, we simply put the elements into these buckets evenly.

      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =
          tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(
              unwrapped_handle);
      tensorflow::ImmediateExecutionTensorHandle* result;
      status->status = custom_device_handle->device()->Pack(

   * href="https://iabtechlab.com/wp-content/uploads/2019/06/VAST_4.2_final_june26.pdf">{@code
   * X-Device-IP}</a> header field name. Header used for VAST requests to provide the IP address of
   * the device on whose behalf the request is being made.
   *
   * @since 31.0
   */
  public static final String X_DEVICE_IP = "X-Device-IP";
  /**
   * The HTTP <a

	if err := syscall.Flock(int(lock.Fd()), syscall.LOCK_EX); err != nil {
		return 0, err
	}

	// In case we're booting a device or emulator alongside all.bash, wait for
	// it to be ready. adb wait-for-device is not enough, we have to
	// wait for sys.boot_completed.
	if err := adb("wait-for-device", "exec-out", "while [[ -z $(getprop sys.boot_completed) ]]; do sleep 1; done;"); err != nil {
		return 0, err
	}

@app.post("/items/")
def create_item(item: Item):
    return item


@app.get("/items/")
def read_items() -> list[Item]:
    return [
        Item(
            name="Portal Gun",
            description="Device to travel through the multi-rick-verse",
        ),
        Item(name="Plumbus"),

@app.post("/items/")
def create_item(item: Item):
    return item


@app.get("/items/")
def read_items() -> list[Item]:
    return [
        Item(
            name="Portal Gun",
            description="Device to travel through the multi-rick-verse",
        ),
        Item(name="Plumbus"),