Isso, mais o simples fato que Python é a principal linguagem para **Data Science**, Machine Learning e especialmente Deep Learning, faz do FastAPI uma ótima escolha para APIs web e aplicações com Data Science / Machine Learning (entre muitas outras).

}

// loadConfig - loads a new config from disk, overrides params
// from env if found and valid
// data is optional. If nil it will be loaded from backend.
func loadConfig(objAPI ObjectLayer, data []byte) error {
	bootstrapTraceMsg("load the configuration")
	srvCfg, err := readServerConfig(GlobalContext, objAPI, data)
	if err != nil {
		return err
	}

	bootstrapTraceMsg("lookup the configuration")

		if err != nil {
			fmt.Printf("Error getting configmap %s: %v\n", configMapName, err)
		}
		meshData := make(map[string]interface{})
		if data, exists := configMap.Data["mesh"]; exists {
			if err := yaml.Unmarshal([]byte(data), &meshData); err != nil {
				fmt.Printf("Error parsing meshConfig: %v\n", err)
				return err
			}
		}

// DownloadProfileData - download profiled data from a remote node.
func (client *peerRESTClient) DownloadProfileData(ctx context.Context) (data map[string][]byte, err error) {
	respBody, err := client.callWithContext(ctx, peerRESTMethodDownloadProfilingData, nil, nil, -1)
	if err != nil {
		return
	}
	defer xhttp.DrainBody(respBody)
	err = gob.NewDecoder(respBody).Decode(&data)
	return data, err
}

                request.getLocalRepository(),
                request.getRemoteRepositories());

        if (cached != null
                // if the POM has no file, we cached a missing artifact, only return the cached data if no update forced
                && (!request.isForceUpdate() || hasFile(cached.getPomArtifact()))) {
            return cached;
        }

        List<Dependency> dependencies;

	for _, rule := range lc.Rules {
		if !rule.Expiration.IsNull() || !rule.NoncurrentVersionExpiration.IsNull() {
			return true
		}
	}
	return false
}

// UnmarshalXML - decodes XML data.
func (lc *Lifecycle) UnmarshalXML(d *xml.Decoder, start xml.StartElement) (err error) {
	switch start.Name.Local {
	case "LifecycleConfiguration", "BucketLifecycleConfiguration":
	default:

 * arbitrary Charset.
 *
 * <p>This is an alternative to copying the data to an {@code OutputStream} via a {@code Writer},
 * which is necessarily blocking. By implementing an {@code InputStream} it allows consumers to
 * "pull" as much data as they can handle, which is more convenient when dealing with flow
 * controlled, async APIs.
 *
 * @author Chris Nokleberg
 */

 * arbitrary Charset.
 *
 * <p>This is an alternative to copying the data to an {@code OutputStream} via a {@code Writer},
 * which is necessarily blocking. By implementing an {@code InputStream} it allows consumers to
 * "pull" as much data as they can handle, which is more convenient when dealing with flow
 * controlled, async APIs.
 *
 * @author Chris Nokleberg
 */

		statusCode: 400,
		cause:      err,
	}
}

func errInvalidDataSource(err error) *s3Error {
	return &s3Error{
		code:       "InvalidDataSource",
		message:    "Invalid data source type. Only CSV, JSON, and Parquet are supported.",
		statusCode: 400,
		cause:      err,
	}
}

func errInvalidRequestParameter(err error) *s3Error {
	return &s3Error{
		code:       "InvalidRequestParameter",

 *       once. This reduces the expense of habitually making defensive copies at API boundaries.
 *       However, the precise conditions for skipping the copy operation are undefined.
 *   <li><b>Warning:</b> a view collection such as {@link ImmutableMap#keySet} or {@link
 *       ImmutableList#subList} may retain a reference to the entire data set, preventing it from