}
  }

  /**
   * Promotes eligible calls from [readyAsyncCalls] to [runningAsyncCalls] and runs them on the
   * executor service. Must not be called with synchronization because executing calls can call
   * into user code.
   *
   * @param enqueuedCall a call to enqueue in the synchronized block
   * @param finishedCall a call to finish in the synchronized block

        <deploy>org.apache.maven.plugins:maven-deploy-plugin:deploy</deploy>
      </phases>
    </lifecycle>
  </lifecycles>
</configuration>

We need to turn this list of phases into a set of plugin objects that have an XML representation like the following:

<plugins>
  <plugin>
    <groupId>org.apache.maven.plugins</groupId>
    <artifactId>maven-resources-plugin</artifactId>
    <executions>

In this example, we didn't declare any Pydantic model. In fact, the request body is not even <abbr title="converted from some plain format, like bytes, into Python objects">parsed</abbr> as JSON, it is read directly as `bytes`, and the function `magic_data_reader()` would be in charge of parsing it in some way.

Nevertheless, we can declare the expected schema for the request body.

</div>

That would work for most of the cases. 😎

You could use that command for example to start your **FastAPI** app in a container, in a server, etc.

## ASGI Servers { #asgi-servers }

Let's go a little deeper into the details.

FastAPI uses a standard for building Python web frameworks and servers called <abbr title="Asynchronous Server Gateway Interface">ASGI</abbr>. FastAPI is an ASGI web framework.

        if (method.changeStatus == JApiChangeStatus.REMOVED) {
            return false
        }
        // @Override has source retention - so we need to peek into the sources
        return repository.isOverride(method)
    }

    Violation acceptOrReject(JApiCompatibility member, Violation rejection) {

        smb.setParamWords(params);
        smb.setBytes(bytes);

        byte[] buffer = new byte[1024];
        int length = smb.encode(buffer, 0);

        // Create a new SMB to decode into
        TestServerMessageBlock decodedSmb = new TestServerMessageBlock();
        decodedSmb.setParamWords(new byte[params.length]);
        decodedSmb.setBytes(new byte[bytes.length]);
        decodedSmb.decode(buffer, 0);

        }
    }

    /**
     * Returns {@code true} if passed in artifact is originating from local repository. In other words, we want
     * to process and store tracking information ONLY into local repository, not to any other place. This method
     * filters out currently built artifacts, as events are fired for them as well, but their resolved artifact

      for (int insertion = 0; insertion < totalInsertions; insertion++) {
        RandomHasherAction.pickAtRandom(random).performAction(random, sinksAndControl);
      }
      // We need to ensure that at least 4 bytes have been put into the hasher or else
      // Hasher#hash will throw an ISE.
      int intToPut = random.nextInt();
      for (Hasher hasher : sinksAndControl) {
        hasher.putInt(intToPut);
      }
      for (Sink sink : sinks) {

async def run_endpoint_function(
    *, dependant: Dependant, values: dict[str, Any], is_coroutine: bool
) -> Any:
    # Only called by get_request_handler. Has been split into its own function to
    # facilitate profiling endpoints, since inner functions are harder to profile.
    assert dependant.call is not None, "dependant.call must be a function"

    if is_coroutine:

)

config_setting(
    name = "with_xla_support",
    define_values = {"with_xla_support": "true"},
    visibility = ["//visibility:public"],
)

# By default, XLA GPU is compiled into tensorflow when building with
# --config=cuda even when `with_xla_support` is false. The config setting
# here allows us to override the behavior if needed.
config_setting(
    name = "no_xla_deps_in_cuda",