If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines to your cluster).

You pay. 💸

The cashier gives you the number of your turn.

<img src="/img/async/concurrent-burgers/concurrent-burgers-04.png" class="illustration">

While you are waiting, you go with your crush and pick a table, you sit and talk with your crush for a long time (as your burgers are very fancy and take some time to prepare).

    In Pydantic version 1, you would use an internal class `Config` and `schema_extra`, as described in <a href="https://docs.pydantic.dev/1.10/usage/schema/#schema-customization" class="external-link" target="_blank">Pydantic's docs: Schema customization</a>.

    You can set `schema_extra` with a `dict` containing any additional data you would like to show up in the generated JSON Schema, including `examples`.

!!! tip

* Then check that the tests **pass** after the PR. ✅

* Many PRs don't have tests, you can **remind** them to add tests, or you can even **suggest** some tests yourself. That's one of the things that consume most time and you can help a lot with that.

* Then also comment what you tried, that way I'll know that you checked it. 🤓

## Create a Pull Request

For example, let's say that you want to use <a href="https://github.com/ijl/orjson" class="external-link" target="_blank">`orjson`</a>, but with some custom settings not used in the included `ORJSONResponse` class.

Let's say you want it to return indented and formatted JSON, so you want to use the orjson option `orjson.OPT_INDENT_2`.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices

    ```

Although you use `Depends` in the parameters of your function the same way you use `Body`, `Query`, etc, `Depends` works a bit differently.

You only give `Depends` a single parameter.

This parameter must be something like a function.

You **don't call it** directly (don't add the parenthesis at the end), you just pass it as a parameter to `Depends()`.

    {!> ../../../docs_src/security/tutorial003.py!}
    ```

!!! tip
    By the spec, you should return a JSON with an `access_token` and a `token_type`, the same as in this example.

    This is something that you have to do yourself in your code, and make sure you use those JSON keys.

    It's almost the only thing that you have to remember to do correctly yourself, to be compliant with the specifications.

			// If the object you request does not exist,
			// the error Amazon S3 returns depends on
			// whether you also have the s3:ListBucket
			// permission.
			// * If you have the s3:ListBucket permission
			//   on the bucket, Amazon S3 will return an
			//   HTTP status code 404 ("no such key")
			//   error.
			// * if you don’t have the s3:ListBucket

   * following conditions:
   *
   * <ul>
   *   <li>You want to assign the same fraction of inputs to each bucket.
   *   <li>When you reduce the number of buckets, you can accept that the most recently added
   *       buckets will be removed first. More concretely, if you are dividing traffic among tasks,
   *       you can decrease the number of tasks from 15 and 10, killing off the final 5 tasks, and