* Edit the text of the PR title to start in "imperative", like giving an order. So, instead of `Adding support for teleporting` use `Add support for teleporting`.
* Try to make the title descriptive about what it achieves. If it's a feature, try to describe it, for example `Add support for teleporting` instead of `Create TeleportAdapter class`.
* Do not finish the title with a period (`.`).

      }
    }
  }

  /**
   * Test for avalanche. Avalanche means that output bits differ with roughly 1/2 probability on
   * different input keys. This test verifies that each possible 1-bit key delta achieves avalanche.
   *
   * <p>For more information: http://burtleburtle.net/bob/hash/avalanche.html
   */
  static void checkAvalanche(HashFunction function, int trials, double epsilon) {
    Random rand = new Random(0);

///

### [NestJS](https://nestjs.com/) (and [Angular](https://angular.io/)) { #nestjs-and-angular }

This isn't even Python, NestJS is a JavaScript (TypeScript) NodeJS framework inspired by Angular.

It achieves something somewhat similar to what can be done with Flask-apispec.

    validations:
      required: true
  - type: textarea
    id: current-behavior
    attributes:
      label: Current Behavior (optional)
      description: Is there a way to achieve the same or similar outcome today?
    validations:
      required: false
  - type: textarea
    id: context
    attributes:
      label: Context
      description: |

But you also want it to accept new items. And when the items didn't exist before, it creates them, and returns an HTTP status code of 201 "Created".

To achieve that, import `JSONResponse`, and return your content there directly, setting the `status_code` that you want:

{* ../../docs_src/additional_status_codes/tutorial001_an_py310.py hl[4,25] *}

/// warning

That way, Pydantic will serialize the data into JSON bytes on the Rust side, which will achieve better performance than these custom JSON responses.

Read more about it in [Custom Response - HTML, Stream, File, others - `orjson` or Response Model](https://fastapi.tiangolo.com/advanced/custom-response/#orjson-or-response-model).

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.

So, you might still need to use Pydantic models.

We are going to enforce that even though `q` is optional, whenever it is provided, **its length doesn't exceed 50 characters**.

### Import `Query` and `Annotated` { #import-query-and-annotated }

To achieve that, first import:

* `Query` from `fastapi`
* `Annotated` from `typing`

{* ../../docs_src/query_params_str_validations/tutorial002_an_py310.py hl[1,3] *}

/// info

        DEVELOCITY_ACCESS_KEY: ${{ secrets.DEVELOCITY_ACCESS_KEY }}
        # Potential stop-gap solution for ReadTimeout issues with the Gradle Build Cache
        # https://gradle.slack.com/archives/CHDLT99C6/p1636477584059200
        GRADLE_OPTS: -Dhttp.keepAlive=false

    - name: Compile with Gradle without publishing a Build Scan
      if: ${{ matrix.language == 'java' && github.repository_owner != 'gradle' }}

        assertLeftoverFilesCleanedUpEventually("successful-test-with-leftover/build/tmp/teŝt files")
    }

    @Test
    fun `flaky tests without leftovers get reports achieved`() {
        val result = run(":flaky-test-without-leftover:test").build()
        assertEquals(TaskOutcome.SUCCESS, result.task(":flaky-test-without-leftover:test")!!.outcome)