TF_Status* status) {
  status->status = tensorflow::FindKernelDef(
      tensorflow::DeviceType(device_type), builder->BuildNodeDef(),
      /* def = */ nullptr, /* kernel_class_name = */ nullptr);
}

const char* TF_GetNumberAttrForOpListInput(const char* op_name, int input_index,
                                           TF_Status* status) {

	}
	if s, err := buf.Peek(2); string(s) != "de" || err != nil {
		t.Fatalf("want %q got %q, err=%v", "de", string(s), err)
	}
	if _, err := buf.Read(p[0:3]); string(p[0:3]) != "def" || err != nil {
		t.Fatalf("want %q got %q, err=%v", "def", string(p[0:3]), err)
	}
	if s, err := buf.Peek(4); string(s) != "ghij" || err != nil {
		t.Fatalf("want %q got %q, err=%v", "ghij", string(s), err)
	}

		{
			srcVol:          "success-vol",
			srcPath:         "abc",
			expectedListDir: []string{"def/", "xyz/"},
			expectedErr:     nil,
		},
		// TestXLStorage case - 1.
		// valid case with existing volume and file to delete.
		{
			srcVol:          "success-vol",
			srcPath:         "abc/def",
			expectedListDir: []string{"ghi/"},
			expectedErr:     nil,
		},
		// TestXLStorage case - 1.

/// tip

Notice that here as we are using standard `open()` that doesn't support `async` and `await`, we declare the path operation with normal `def`.

///

### `FileResponse`

Asynchronously streams a file as the response.

Takes a different set of arguments to instantiate than the other response types:

```Python
something(some_argument, some_keyword_argument="foo")
```

これを「呼び出し可能」なものと呼びます。

## 依存関係としてのクラス

Pythonのクラスのインスタンスを作成する際に、同じ構文を使用していることに気づくかもしれません。

例えば:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name


fluffy = Cat(name="Mr Fluffy")
```

この場合、`fluffy`は`Cat`クラスのインスタンスです。

そして`fluffy`を作成するために、`Cat`を「呼び出している」ことになります。

そのため、Pythonのクラスもまた「呼び出し可能」です。

* Create a `main.py` file with:

```Python
from typing import Union

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):
    return {"item_id": item_id, "q": q}
```

### Dockerfile

/// tip | Dica

Perceba que aqui estamos utilizando o `open()` da biblioteca padrão que não suporta `async` e `await`, e declaramos a operação de rota com o `def` básico.

///

### `FileResponse`

Envia um arquivo  de forma assíncrona e contínua (stream).
*
Recebe um conjunto de argumentos do construtor diferente dos outros tipos de resposta:

Bạn viết chuẩn Python với kiểu dữ liệu như sau:

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

Sau đó có thể được sử dụng:

```Python

编写带有类型标注的标准 Python：

```Python
from datetime import date

from pydantic import BaseModel

# Declare a variable as a str
# and get editor support inside the function
def main(user_id: str):
    return user_id


# A Pydantic model
class User(BaseModel):
    id: int
    name: str
    joined: date
```

可以像这样来使用：

```Python

			nesting--
		}
		tokens = append(tokens, Make(tok, in.Stack.Text()))
	}
}

// #ifdef and #ifndef processing.
func (in *Input) ifdef(truth bool) {
	name := in.macroName()
	in.expectNewline("#if[n]def")
	if !in.enabled() {
		truth = false
	} else if _, defined := in.macros[name]; !defined {
		truth = !truth
	}
	in.ifdefStack = append(in.ifdefStack, truth)
}

// #else processing