std::vector<std::unique_ptr<tensorflow::Device>> devices;

  if (prefix == nullptr || strlen(prefix) == 0)
    prefix = "/job:localhost/replica:0/task:0";

  tensorflow::SessionOptions sess_options;
  (*sess_options.config.mutable_device_count())["CPU"] = num_cpus;
  status->status =
      tensorflow::DeviceFactory::AddCpuDevices(sess_options, prefix, &devices);

@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"),

응답의 콘텐츠 타입은 `application/json` 대신 `application/jsonl`이고, 본문은 다음과 같습니다:

```json
{"name": "Plumbus", "description": "A multi-purpose household device."}
{"name": "Portal Gun", "description": "A portal opening device."}
{"name": "Meeseeks Box", "description": "A box that summons a Meeseeks."}
```

JSON 배열(Python의 list에 해당)과 매우 비슷하지만, 항목들을 `[]`로 감싸고 항목 사이에 `,`를 넣는 대신, 줄마다 하나의 JSON 객체가 있고, 새 줄 문자로 구분됩니다.

    TF_ImportGraphDefOptions* opts, const char* prefix);

// Set the execution device for nodes in `graph_def`.
// Only applies to nodes where a device was not already explicitly specified.
// `device` is copied and has no lifetime requirements.
TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsSetDefaultDevice(
    TF_ImportGraphDefOptions* opts, const char* device);

// Set whether to uniquify imported operation names. If true, imported operation

<%@page pageEncoding="UTF-8" contentType="text/html; charset=UTF-8"%>
<meta content='width=device-width, initial-scale=1' name='viewport'>
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<link href="${fe:url('/css/admin/bootstrap.min.css')}" rel="stylesheet" type="text/css" />
<link href="${fe:url('/css/admin/font-awesome.min.css')}" rel="stylesheet" type="text/css" />
<link href="${fe:url('/css/admin/adminlte.min.css')}" rel="stylesheet" type="text/css" />

	{
		Name:   "symlink",
		Data:   []byte("../link/target"),
		Method: Deflate,
		Mode:   0755 | fs.ModeSymlink,
	},
	{
		Name:   "device",
		Data:   []byte("device file"),
		Method: Deflate,
		Mode:   0755 | fs.ModeDevice,
	},
	{
		Name:   "chardevice",
		Data:   []byte("char device file"),
		Method: Deflate,
		Mode:   0755 | fs.ModeDevice | fs.ModeCharDevice,
	},
}

func TestWriter(t *testing.T) {

    /** The handle is invalid */
    int NT_STATUS_INVALID_HANDLE = 0xC0000008;
    /** The parameter is incorrect */
    int NT_STATUS_INVALID_PARAMETER = 0xC000000d;
    /** The system cannot find the device specified */
    int NT_STATUS_NO_SUCH_DEVICE = 0xC000000e;
    /** The system cannot find the file specified */
    int NT_STATUS_NO_SUCH_FILE = 0xC000000f;
    /** End of file reached */

#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) {

---
name: TensorFlow Lite in Play Services issue
about: Use this template for issues with TensorFlow Lite in Google Play Services
labels: 'comp:lite-in-play-services'

---

**System information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
  if possible):
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- Google Play Services version