netns1 := p.UpsertPodCacheWithNetns(string(pod.UID), wl)
	if !reflect.DeepEqual(netns1, ns) {
		t.Fatalf("Expected the same Netns for the same uid, got %v and %v", netns1, ns)
	}

	ns2 := newFakeNsInode(inc(), 1)
	wl2 := WorkloadInfo{
		Workload: podToWorkload(pod),
		Netns:    ns2,
	}
	// when using same uid, the original netns should be returned
	netns2 := p.UpsertPodCacheWithNetns(string(pod.UID), wl2)
	if netns2 != ns {

```

### Testing file

Then you could have a file `test_main.py` with your tests. It could live on the same Python package (the same directory with a `__init__.py` file):

``` hl_lines="5"
.
├── app
│   ├── __init__.py
│   ├── main.py
│   └── test_main.py
```

Because this file is in the same package, you can use relative imports to import the object `app` from the `main` module (`main.py`):

```Python hl_lines="3"

Do you want to just have a `str`? Or just a `dict`? Or a database class model instance directly? It all works the same way.

You actually don't have users that log in to your application but robots, bots, or other systems, that have just an access token? Again, it all works the same.

    * So, by using FastAPI you are saving development time, bugs, lines of code, and you would probably get the same performance (or better) you would if you didn't use it (as you would have to implement it all in your code).

* Use the `templates` you created to render and return a `TemplateResponse`, pass the name of the template, the request object, and a "context" dictionary with key-value pairs to be used inside of the Jinja2 template.

```Python hl_lines="4  11  15-18"
{!../../../docs_src/templates/tutorial001.py!}
```

!!! note
    Before FastAPI 0.108.0, Starlette 0.29.0, the `name` was the first parameter.

# to reference the most recent versions of the SIG Build Docker images.
name: Update RBE Configs
on:
  workflow_dispatch:

permissions:
  contents: read

jobs:
  rbe:
    name: Update RBE Configs
    runs-on: ubuntu-latest
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    steps:
    - name: Checkout code
      uses: actions/checkout@755da8c3cf115ac066823e79a1e1788f8940201b # v3.2.0

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

- **Each object is written to a single EC set, and therefore is spread over no more than 16 drives.**
- **All the nodes running distributed MinIO setup are recommended to be homogeneous, i.e. same operating system, same number of drives and same network interconnects.**

  ]
}
```

because the path parameter `item_id` had a value of `"foo"`, which is not an `int`.

The same error would appear if you provided a `float` instead of an `int`, as in: <a href="http://127.0.0.1:8000/items/4.2" class="external-link" target="_blank">http://127.0.0.1:8000/items/4.2</a>

!!! check
    So, with the same Python type declaration, **FastAPI** gives you data validation.

 ${project.home}/maven.properties file.

Plugin configuration

 The configuration of plugins is of the same form as the maven {{{plugin-descriptors}plugin descriptors}}
 themselves:

+-----+
<plugins>
  <plugin>
   <id>xdoc</id>
   <version>1.0</version>
   <parameters>
     <parameter>
       <name>theme</name>
       <value>classic</value>
     </parameter>
   </parameters>
  </plugin>
</plugins>