1. The id_token is an identifier that is hard to guess. For example, a randomly generated string of sufficient length, that the server handling the protected resource can use to lookup the associated authorization information.
2. The id_token self-contains the authorization information in a manner that can be verified. For example, by encoding authorization information along with a signature into the token.

```JSON
{
  "detail": "Item not found"
}
```

/// tip

When raising an `HTTPException`, you can pass any value that can be converted to JSON as the parameter `detail`, not only `str`.

You could pass a `dict`, a `list`, etc.

They are handled automatically by **FastAPI** and converted to JSON.

///

		expectedQuery string
	}{
		{"", ""},
		{
			"?Action=AssumeRoleWithLDAPIdentity&LDAPUsername=myusername&LDAPPassword=can+youreadthis%3F&Version=2011-06-15",
			"?Action=AssumeRoleWithLDAPIdentity&LDAPUsername=myusername&LDAPPassword=*REDACTED*&Version=2011-06-15",
		},
		{
			"LDAPPassword=can+youreadthis%3F&Version=2011-06-15&?Action=AssumeRoleWithLDAPIdentity&LDAPUsername=myusername",

# Background Tasks - `BackgroundTasks`

You can declare a parameter in a *path operation function* or dependency function with the type `BackgroundTasks`, and then you can use it to schedule the execution of background tasks after the response is sent.

You can import it directly from `fastapi`:

```python
from fastapi import BackgroundTasks
```

여러분은 이진 데이터, 텍스트, JSON 데이터를 받을 수 있고 전송할 수 있습니다.

## 시도해보기

파일 이름이 `main.py`라고 가정하고 응용 프로그램을 실행합니다:

<div class="termy">

```console
$ fastapi dev main.py

<span style="color: green;">INFO</span>:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

</div>

브라우저에서 <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000</a>을 열어보세요.

- Existing or newer buckets can be created with versioning enabled and eventually can be suspended as well. Existing versions of objects stay as is and can still be accessed using the version ID.
- All versions, including delete-markers should be deleted before deleting a bucket.

   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.

/**
 * A call is a request that has been prepared for execution. A call can be canceled. As this object
 * represents a single request/response pair (stream), it cannot be executed twice.
 */
interface Call : Cloneable {
  /** Returns the original request that initiated this call. */
  fun request(): Request

  /**
   * Invokes the request immediately, and blocks until the response can be processed or is in error.
   *

ECL0048=The constructor of {0} for arguments({1}) not found
ECL0049=The method({1}) of {0} not found
ECL0050=Can not parse, because {0}
ECL0051=Can not parse of ({0})
ECL0053=Bad parser configuration, because {0}
ECL0054=SAXException occurred, because {0}
ECL0055=Resource({0}) not found
ECL0057=Method({1}) of class({0}) not found
ECL0059=Can not configure property({1}) of {0}, because {2}
ECL0061=SystemException occurred, because {0}

                Arguments.of(WinError.ERROR_ACCESS_DENIED, "Access is denied."),
                Arguments.of(WinError.ERROR_REQ_NOT_ACCEP,
                        "No more connections can be made to this remote computer at this time because there are already as many connections as the computer can accept."),
                Arguments.of(WinError.ERROR_BAD_PIPE, "The pipe state is invalid."),
                Arguments.of(WinError.ERROR_PIPE_BUSY, "All pipe instances are busy."),