*  Fix: Use literal IP addresses directly rather than passing them to `DnsOverHttps`.
 *  Fix: Embed Proguard rules to prevent warnings from tools like DexGuard and R8. These warnings
    were triggered by OkHttp’s feature detection for TLS packages like `org.conscrypt`,
    `org.bouncycastle`, and `org.openjsse`.
 *  Upgrade: Explicitly depend on `kotlin-stdlib-jdk8`. This fixes a problem with dependency

So, you would run **multiple containers** with different things, like a database, a Python application, a web server with a React frontend application, and connect them together via their internal network.

All the container management systems (like Docker or Kubernetes) have these networking features integrated into them.

## Containers and Processes { #containers-and-processes }

	fmt.Println(bytes.ContainsAny([]byte("I like seafood."), "fÄo!"))
	fmt.Println(bytes.ContainsAny([]byte("I like seafood."), "去是伟大的."))
	fmt.Println(bytes.ContainsAny([]byte("I like seafood."), ""))
	fmt.Println(bytes.ContainsAny([]byte(""), ""))
	// Output:
	// true
	// true
	// false
	// false
}

func ExampleContainsRune() {
	fmt.Println(bytes.ContainsRune([]byte("I like seafood."), 'f'))

{* ../../docs_src/body_nested_models/tutorial002.py hl[1] *}

### Declare a `list` with a type parameter { #declare-a-list-with-a-type-parameter }

To declare types that have type parameters (internal types), like `list`, `dict`, `tuple`:

* If you are in a Python version lower than 3.9, import their equivalent version from the `typing` module
* Pass the internal type(s) as "type parameters" using square brackets: `[` and `]`

# words checklist
segfault_memory:
   - segfault
   - memory leaks
# assignees
filesystem_security_assignee:
   - mihaimaruseac
   
# Cuda Comment
cuda_comment: >
   From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:
      * For TF-GPU - See point 1
      * For TF-CPU - See point 2
   -----------------------------------------------------------------------------------------------
   

</ul>
<p>
Some memory operations are <i>read-like</i>, including read, atomic read, mutex lock, and channel receive.
Other memory operations are <i>write-like</i>, including write, atomic write, mutex unlock, channel send, and channel close.
Some, such as atomic compare-and-swap, are both read-like and write-like.
</p>

<p>

## Pydantic Models for Input and Output { #pydantic-models-for-input-and-output }

Let's say you have a Pydantic model with default values, like this one:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### Model for Input { #model-for-input }

If you use this model as an input like here:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}

     */
    public List<FacetQueryView> getFacetQueryViewList() {
        return facetQueryViewList;
    }

    /**
     * Adds a MIME type to the inline display set.
     *
     * @param mimeType the MIME type to display inline
     */
    public void addInlineMimeType(final String mimeType) {
        inlineMimeTypeSet.add(mimeType);
    }

    /**

# Help FastAPI - Get Help { #help-fastapi-get-help }

Do you like **FastAPI**?

Would you like to help FastAPI, other users, and the author?

Or would you like to get help with **FastAPI**?

There are very simple ways to help (several involve just one or two clicks).

And there are several ways to get help too.

## Subscribe to the newsletter { #subscribe-to-the-newsletter }

///

## Response Status Code { #response-status-code }

You can define the (HTTP) `status_code` to be used in the response of your *path operation*.

You can pass directly the `int` code, like `404`.

But if you don't remember what each number code is for, you can use the shortcut constants in `status`:

{* ../../docs_src/path_operation_configuration/tutorial001_py310.py hl[1,15] *}