Notice that `response_model` is a parameter of the "decorator" method (`get`, `post`, etc). Not of your *path operation function*, like all the parameters and body.

///

`response_model` receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e.g. a `list` of Pydantic models, like `List[Item]`.

They're also concrete: each URL identifies a specific path (like `/square/okhttp`) and query (like `?q=sharks&lang=en`). Each webserver hosts many URLs.

### [Addresses](https://square.github.io/okhttp/4.x/okhttp/okhttp3/-address/)

Addresses specify a webserver (like `github.com`) and all of the **static** configuration necessary to connect to that server: the port number, HTTPS settings, and preferred network protocols (like HTTP/2).

///

## Multiple body parameters { #multiple-body-parameters }

In the previous example, the *path operations* would expect a JSON body with the attributes of an `Item`, like:

```JSON
{
    "name": "Foo",
    "description": "The pretender",
    "price": 42.0,
    "tax": 3.2
}
```

But you can also declare multiple body parameters, e.g. `item` and `user`:

<div class="termy">

```console
$ OPENAPI_URL= uvicorn main:app

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

</div>

Then if you go to the URLs at `/openapi.json`, `/docs`, or `/redoc` you will just get a `404 Not Found` error like:

```JSON
{
    "detail": "Not Found"
}

	}

	if name != "groupby" || total != 60 {
		t.Errorf("name should be groupby, but got %v, total should be 60, but got %v", name, total)
	}

	if err := DB.Model(&User{}).Select("name, sum(age) as total").Where("name LIKE ?", "groupby%").Group("name").Having("name = ?", "groupby1").Row().Scan(&name, &total); err != nil {
		t.Errorf("no error should happen, but got %v", err)
	}

	if name != "groupby1" || total != 660 {

The "data" directory is for data loading like this:
/- - - - - - - - - - - - - - - - - - - -
playsql
  |-data
     |-common
     |  |-xls
     |     |-10-master.xls
     |     |-defaultValueMap.dataprop
     |-ut
        |-xls
           |-20-member.xls  
           |-30-product.xls  
           |-defaultValueMap.dataprop
- - - - - - - - - -/

The format of a xls file is like this:
/- - - - - - - - - - - - - - - - - - - -

* Ele expõe um objeto python <a href="https://docs.python.org/3/library/tempfile.html#tempfile.SpooledTemporaryFile" class="external-link" target="_blank">`SpooledTemporaryFile`</a> que você pode passar diretamente para outras bibliotecas que esperam um objeto semelhante a um arquivo("file-like").

### `UploadFile`

# JSON Compatible Encoder { #json-compatible-encoder }

There are some cases where you might need to convert a data type (like a Pydantic model) to something compatible with JSON (like a `dict`, `list`, etc).

For example, if you need to store it in a database.

For that, **FastAPI** provides a `jsonable_encoder()` function.

## Using the `jsonable_encoder` { #using-the-jsonable-encoder }

 *
 * The first element on each line is a single hex code point (like 0041) or a hex code point range
 * (like 0030..0039).
 *
 * The second element on each line is a mapping type, like `valid` or `mapped`.
 *
 * For lines that contain a mapping target, the next thing is a sequence of hex code points (like
 * 0031 2044 0034).
 *
 * All other data is ignored.
 */

# limitations under the License.
# ==============================================================================


"""Converts MSYS Linux-like paths stored in env variables to Windows paths.

This is necessary on Windows, because some applications do not understand/handle
Linux-like paths MSYS uses, for example, Docker.
"""

import argparse
import os


def should_convert(var_name: str,