}

  @Test
  fun testNoUncompress() {
    val response = response("https://httpbin.org/brotli", "XXXX".encodeUtf8())

    val same = uncompress(response)

    val responseString = same.body.string()
    assertThat(responseString).isEqualTo("XXXX")
  }

  @Test
  fun testFailsUncompress() {
    val response =

   *       the same.
   *   <li>The unreserved characters ".", "-", "~", and "_" remain the same.
   *   <li>The general delimiters "@" and ":" remain the same.
   *   <li>The subdelimiters "!", "$", "&amp;", "'", "(", ")", "*", "+", ",", ";", and "=" remain
   *       the same.
   *   <li>The space character " " is converted into %20.

* There can be **multiple processes** of the **same program** running at the same time.

If you check out the "task manager" or "system monitor" (or similar tools) in your operating system, you will be able to see many of those processes running.

After that, all of the processing logic is the same.

But because of our changes in `GzipRequest.body`, the request body will be automatically decompressed when it is loaded by **FastAPI** when needed.

## Accessing the request body in an exception handler

/// tip

  // +optional
  optional string apiGroup = 1;

  // Kind is the type of resource being referenced. This is the same
  // value as in the parameter object's metadata, for example "ConfigMap".
  optional string kind = 2;

  // Name is the name of resource being referenced.
  optional string name = 3;
}

// ResourceClaimSchedulingStatus contains information about one particular

	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 {

# Cookie Parameters

You can define Cookie parameters the same way you define `Query` and `Path` parameters.

## Import `Cookie`

First import `Cookie`:

//// tab | Python 3.10+

```Python hl_lines="3"
{!> ../../docs_src/cookie_params/tutorial001_an_py310.py!}
```

////

//// tab | Python 3.9+

```Python hl_lines="3"
{!> ../../docs_src/cookie_params/tutorial001_an_py39.py!}
```

////

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.

```Python hl_lines="3  5"
{
    "name": "Bar",
    "description": "The bartenders",
    "price": 62,
    "tax": 20.2
}
```

they will be included in the response.

#### Data with the same values as the defaults

If the data has the same values as the default ones, like the item with ID `baz`:

```Python hl_lines="3  5-6"
{
    "name": "Baz",
    "description": None,

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.