* @param result Outcome of the current Maven build.
     * @return A {@link BuildResumptionData} instance or {@link Optional#empty()} if resuming the build is not possible.
     */
    Optional<BuildResumptionData> determineBuildResumptionData(MavenExecutionResult result);

## OpenAPI { #openapi }

OpenAPI (previously known as Swagger) is the open specification for building APIs (now part of the Linux Foundation).

**FastAPI** is based on **OpenAPI**.

That's what makes it possible to have multiple automatic interactive documentation interfaces, code generation, etc.

OpenAPI has a way to define multiple security "schemes".

    validations:
      required: true

  - type: input
    id: testgrid
    attributes:
      label: Testgrid link

  - type: textarea
    id: reason
    attributes:
      label: Reason for failure (if possible)

  - type: textarea
    id: additional
    attributes:
      label: Anything else we need to know?

  - type: textarea
    id: sigs
    attributes:
      label: Relevant SIG(s)

    validations:
      required: true
  - type: textarea
    id: description
    attributes:
      label: Problem description
      description: |
        Please describe the problem as concisely as possible.
    validations:
      required: true
  - type: textarea
    id: context
    attributes:
      label: Context (optional)
      description: |

By only using `BackgroundTasks` (and not `BackgroundTask`), it's then possible to use it as a *path operation function* parameter and have **FastAPI** handle the rest for you, just like when using the `Request` object directly.

It's still possible to use `BackgroundTask` alone in FastAPI, but you have to create the object in your code and return a Starlette `Response` including it.

stated above is already equivalent to code execution. In any case, the
recommendation is not to serve models using Eager mode since it also has
performance limitations.

## Multi-Tenant environments

It is possible to run multiple TensorFlow models in parallel. For example,
`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running

   * response code like 404 or 500.
   *
   * @throws IOException if the request could not be executed due to cancellation, a connectivity
   *     problem or timeout. Because networks can fail during an exchange, it is possible that the
   *     remote server accepted the request before the failure.
   * @throws IllegalStateException when the call has already been executed.
   */
  @Throws(IOException::class)
  fun execute(): Response

0002 0.554 responseBodyEnd
0002 0.554 connectionReleased
0002 0.554 callEnd
0001 0.624 responseBodyEnd
0001 0.624 connectionReleased
0001 0.624 callEnd
```

The `EventListener.Factory` also makes it possible to limit metrics to a subset of calls. This one captures metrics on a random 10%:

```java
class MetricsEventListener extends EventListener {
  private static final Factory FACTORY = new Factory() {

    validations:
      required: true

  - type: input
    id: testgrid
    attributes:
      label: Testgrid link

  - type: textarea
    id: reason
    attributes:
      label: Reason for failure (if possible)

  - type: textarea
    id: additional
    attributes:
      label: Anything else we need to know?

  - type: textarea
    id: sigs
    attributes:
      label: Relevant SIG(s)

I'll tell you a bit more about these **concepts** here, and that would hopefully give you the **intuition** you would need to decide how to deploy your API in very different environments, possibly even in **future** ones that don't exist yet.