The dynamic header is on by default, even without any
		references to dynamic libraries, because many common
		system tools now assume the presence of the header.
	-dumpdep
		Dump symbol dependency graph.
	-extar ar
		Set the external archive program (default "ar").
		Used only for -buildmode=c-archive.
	-extld linker
		Set the external linker (default "clang" or "gcc").
	-extldflags flags

	flagTmpdir     = flag.String("tmpdir", "", "use `directory` for temporary files")

	flagExtld      quoted.Flag
	flagExtldflags quoted.Flag
	flagExtar      = flag.String("extar", "", "archive program for buildmode=c-archive")

	flagCaptureHostObjs = flag.String("capturehostobjs", "", "capture host object files loaded during internal linking to specified dir")

echo "testar" > PWD/testar.ran
`

func TestExtar(t *testing.T) {
	switch GOOS {
	case "windows":
		t.Skip("skipping signal test on Windows")
	}
	if runtime.Compiler == "gccgo" {
		t.Skip("skipping -extar test when using gccgo")
	}
	globalSkip(t)
	testenv.MustHaveGoBuild(t)
	testenv.MustHaveCGO(t)
	testenv.MustHaveBuildMode(t, "c-archive")
	testenv.MustHaveExecPath(t, "bash") // This test uses a bash script

So, we get a Pydantic model from the data in another Pydantic model.

#### Unwrapping a `dict` and extra keywords

And then adding the extra keyword argument `hashed_password=hashed_password`, like in:

```Python
UserInDB(**user_in.dict(), hashed_password=hashed_password)
```

...ends up being like:

```Python

# Declare Request Example Data

You can declare examples of the data your app can receive.

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

=== "Python 3.10+ Pydantic v2"

    ```Python hl_lines="13-24"
    {!> ../../../docs_src/schema_extra_example/tutorial001_py310.py!}
    ```

apiVersion: release-notes/v2
kind: bug-fix
area: telemetry
issue:
- 31270
releaseNotes:
- |
  **Fixed** an issue where envoy does not start up properly when

Então, obtemos um modelo Pydantic a partir dos dados em outro modelo Pydantic.

#### Desembrulhando um `dict` e palavras-chave extras

E, então, adicionando o argumento de palavra-chave extra `hashed_password=hashed_password`, como em:

```Python
UserInDB(**user_in.dict(), hashed_password=hashed_password)
```

...acaba sendo como:

```Python
UserInDB(

# Extra Data Types

Up to now, you have been using common data types, like:

* `int`
* `float`
* `str`
* `bool`

But you can also use more complex data types.

And you will still have the same features as seen up to now:

* Great editor support.
* Data conversion from incoming requests.
* Data conversion for response data.
* Data validation.
* Automatic annotation and documentation.

## Other data types

jaystone776 <******@****.***> 1711934153 +0800

Guillaume Nodet <******@****.***> 1688033030 +0200