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

现在在相同的project目录创建一个名为`Dockerfile`的文件:

```{ .dockerfile .annotate }
# (1)
FROM python:3.9

# (2)
WORKDIR /code

print(f"Hello {name} from Python")
```

/// tip

<a href="https://docs.python.org/3.8/library/os.html#os.getenv" class="external-link" target="_blank">`os.getenv()`</a> 的第二个参数是要返回的默认值。

如果没有提供默认值，默认为 `None`，此处我们提供了 `"World"` 作为要使用的默认值。

///

然后，您可以调用该 Python 程序：

<div class="termy">

```console
// 这里我们还没有设置环境变量
$ python main.py

// 因为我们没有设置环境变量，所以我们得到默认值

Hello World from Python

        if ("always".equals(styleColor) || "yes".equals(styleColor) || "force".equals(styleColor)) {
            context.coloredOutput = true;
        } else if ("never".equals(styleColor) || "no".equals(styleColor) || "none".equals(styleColor)) {
            context.coloredOutput = false;
        } else if (!"auto".equals(styleColor) && !"tty".equals(styleColor) && !"if-tty".equals(styleColor)) {
            throw new IllegalArgumentException(

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}
```

### 도커파일

이제 같은 프로젝트 디렉터리에 다음과 같은 파일 `Dockerfile`을 생성합니다:

```{ .dockerfile .annotate }
# (1)
FROM python:3.9

# (2)
WORKDIR /code

# google internal build on configurable attributes.
#
# For non-configurable distinction between OSS and Google builds, see
# `if_oss()` and `if_google()` macros in tensorflow.bzl.
config_setting(
    name = "oss",
    flag_values = {":oss_setting": "True"},
    visibility = ["//visibility:public"],
)

# Non-configurable setting to indicate open source build.
bool_setting(
    name = "oss_setting",

        data, or by relaxing `Input` shape assumptions (note that you can pass
        shapes with `None` entries for axes that are meant to be dynamic). You
        can also disable the input checking entirely by setting
        `model.input_spec = None`.
    *   Several changes have been made to
        `tf.keras.mixed_precision.experimental`. Note that it is now recommended
        to use the non-experimental `tf.keras.mixed_precision` API.

grafana-node.png...

			switch {
			case arch.IsARM64STLXR(op):
				// ARM64 instructions with one input and two outputs.
				prog.From = a[0]
				prog.To = a[1]
				if a[2].Type != obj.TYPE_REG {
					p.errorf("invalid addressing modes for third operand to %s instruction, must be register", op)
					return
				}
				prog.RegTo2 = a[2].Reg
			case arch.IsARM64TBL(op):
				// one of its inputs does not fit into prog.Reg.
				prog.From = a[0]

}

const (
	// WorkerMaxLimit max number of workers per node for "fast" mode
	WorkerMaxLimit = 500

	// WorkerMinLimit min number of workers per node for "slow" mode
	WorkerMinLimit = 50

	// WorkerAutoDefault is default number of workers for "auto" mode
	WorkerAutoDefault = 100

	// MRFWorkerMaxLimit max number of mrf workers per node for "fast" mode
	MRFWorkerMaxLimit = 8

          <version>4.0.0+</version>
          <type>String</type>
          <description>The name of the file that must be missing to activate the profile. Please note, that missing and exists
          fields cannot be used together. Only one of them should be used at any one time.</description>
        </field>
        <field>
          <name>exists</name>
          <version>4.0.0+</version>
          <type>String</type>