// Second call should return cached value (same as first)
        Subject second = auth.getSubject();
        if (first == null && second == null) {
            // Both null - caching is working
            assertNull(second, "Second getSubject should return same result as first (both null)");
        } else if (first != null && second != null) {
            // Both non-null - should be same instance

            }
        }

        return resultBuf.toString();

    }

    /**
     * Executes the Python script with the configured parameters.
     * Sets up the command list, working directory, and environment variables,
     * then starts the Python process and waits for completion.
     *
     * @throws JobProcessingException if the Python script execution fails
     */

from MinIO before returning it to an application. You can register a Lambda Function target on MinIO, once successfully registered it can be used to transform the data for application GET requests on demand.

This document focuses on showing a working example on how to use Object Lambda with MinIO, you must have [MinIO deployed in your environment](https://docs.min.io/community/minio-object-store/operations/installation.html) before you can start using external lambda functions. You also must install...

## Tests { #tests }

Make sure you have [tests](../tutorial/testing.md){.internal-link target=_blank} for your app and you run them on continuous integration (CI).

This way, you can do the upgrade and make sure everything is still working as expected.

## `bump-pydantic` { #bump-pydantic }

In many cases, when you use regular Pydantic models without customizations, you will be able to automate most of the process of migrating from Pydantic v1 to Pydantic v2.

And you get editor support everywhere.

Even for items inside of lists:

<img src="/img/tutorial/body-nested-models/image01.png">

You couldn't get this kind of editor support if you were working directly with `dict` instead of Pydantic models.

But you don't have to worry about them either, incoming dicts are converted automatically and your output is converted automatically to JSON too.

Instead of that, by being an "asynchronous" system, once finished, the task can wait in line a little bit (some microseconds) for the computer / program to finish whatever it went to do, and then come back to take the results and continue working with them.

    }
    assertThat(actual.getBounds())
        .asList()
        .containsExactlyElementsIn(asList(expected.getBounds()))
        .inOrder();
  }

  /**
   * Working with arrays requires defensive code. Verify that we clone the type array for both input
   * and output.
   */
  public void testNewParameterizedTypeImmutability() {
    Type[] typesIn = {String.class, Integer.class};

<img src="/img/tutorial/path-params/image03.png">

### Mit Python-*Enumerationen* arbeiten { #working-with-python-enumerations }

Der *Pfad-Parameter* wird ein *<abbr title="Member – Mitglied: Einer der möglichen Werte einer Enumeration">Member</abbr> einer Enumeration* sein.

Using a relative URL is important to make sure your application keeps working even in an advanced use case like [Behind a Proxy](../../advanced/behind-a-proxy.md){.internal-link target=_blank}.

///

This, of course, is not optimal and you wouldn't use it for production.

In production you would have one of the options above.

But it's the simplest way to focus on the server-side of WebSockets and have a working example:

{* ../../docs_src/websockets/tutorial001_py39.py hl[2,6:38,41:43] *}

## Create a `websocket` { #create-a-websocket }

In your **FastAPI** application, create a `websocket`: