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 }

## 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] *}

 *
 * @since 4.0.0
 */
@Experimental
public interface UpgradeOptions extends Options {
    /**
     * Returns the list of upgrade goals to be executed.
     * These goals can include operations like "check", "dependencies", "plugins", etc.
     *
     * @return an {@link Optional} containing the list of goals, or empty if not specified
     */
    @Nonnull
    Optional<List<String>> goals();

    /**

* They are not synced with the code to reflect the eventual solution that is committed
* Google Docs is not a "code-oriented" tool, like Markdown can be
* Review in Google Docs is not as simple as a PR code review in GitHub

## Decision

The *Build Tool Team* has decided to use Architectural Decision Records (aka ADR) to track decisions we want to follow.

		t.Fatalf("got error: %v", err)
	}

	if len(users) != 4 {
		t.Errorf("Four users should be found, instead found %d", len(users))
	}

	DB.Select("*").Where("name LIKE ?", "subquery%").Where("age >= (?)", DB.
		Select("AVG(age)").Table("users").Where("name LIKE ?", "subquery%")).Find(&users)

</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>

	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'))

# 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
   -----------------------------------------------------------------------------------------------
   

# Static Files - `StaticFiles`

You can use the `StaticFiles` class to serve static files, like JavaScript, CSS, images, etc.

Read more about it in the [FastAPI docs for Static Files](https://fastapi.tiangolo.com/tutorial/static-files/).

You can import it directly from `fastapi.staticfiles`:

```python
from fastapi.staticfiles import StaticFiles
```

        } else {
            /*
             * MNG-4900: Hack to workaround deficiency of legacy API which makes it impossible for plugins to access the
             * global profile manager which is required to build a POM like a CLI invocation does. Failure to consider
             * the activated profiles can cause repo declarations to be lost which in turn will result in artifact