*
 * [rfc_2045]: http://tools.ietf.org/html/rfc2045
 */
class MediaType internal constructor(
  internal val mediaType: String,
  /**
   * Returns the high-level media type, such as "text", "image", "audio", "video", or "application".
   */
  @get:JvmName("type") val type: String,
  /**
   * Returns a specific media subtype, such as "plain" or "png", "mpeg", "mp4" or "xml".
   */

MinIO server configuration file allows users to provide topology that allows for heterogeneous hostnames, allowing MinIO to deployed in pre-existing environments without any further OS level configurations.

### Usage

        // Test with various MIME types
        String[] supportedTypes = { "application/pdf", "image/jpeg", "image/png" };
        String[] unsupportedTypes = { "text/plain", "application/xml", "audio/mp3" };

        for (String mimeType : supportedTypes) {
            Map<String, Object> docMap = new HashMap<>();
            docMap.put("mimetype", mimeType);

Vous pouvez également l'utiliser pour diffuser de gros fichiers binaires, en envoyant chaque bloc de données au fur et à mesure de la lecture, sans tout charger en mémoire d'un coup.

Vous pouvez aussi diffuser de la **vidéo** ou de l'**audio** de cette manière ; cela peut même être généré au fil du traitement et de l'envoi.

## Utiliser une `StreamingResponse` avec `yield` { #a-streamingresponse-with-yield }

/// tip | Tipp

Wenn Sie Binärdaten streamen möchten, zum Beispiel Video oder Audio, sehen Sie sich den erweiterten Leitfaden an: [Daten streamen](../advanced/stream-data.md).

///

## JSON Lines mit FastAPI streamen { #stream-json-lines-with-fastapi }

        with:
          api-level: ${{ matrix.api-level }}
          force-avd-creation: false
          arch: ${{ matrix.api-level >= 30 && 'x86_64' || 'x86' }}
          # No window, no audio, and use swiftshader for headless environments
          emulator-options: >
            -no-window
            -gpu swiftshader_indirect
            -noaudio
            -no-boot-anim
            -camera-back none

running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can
process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

	// Some standard content-types which we strictly dis-allow for compression.
	standardExcludeCompressContentTypes = []string{"video/*", "audio/*", "application/zip", "application/x-gzip", "application/x-zip-compressed", " application/x-compress", "application/x-spoon"}

	// AuthZ Plugin system.
	globalAuthZPlugin *polplugin.AuthZPlugin

    /** The key of the message: OGG Audio */
    public static final String LABELS_facet_filetype_ogg = "{labels.facet_filetype_ogg}";

    /** The key of the message: WAV Audio */
    public static final String LABELS_facet_filetype_wav = "{labels.facet_filetype_wav}";

    /** The key of the message: WebP Audio */

    vbox.memory = Integer(ENV['VAGRANT_MEMORY'] || 8192)
    vbox.cpus = Integer(ENV['VAGRANT_CPUS'] || 4)

    # see https://github.com/hashicorp/vagrant/issues/9524
    vbox.customize ["modifyvm", :id, "--audio", "none"]
  end

  # Switch the default share for the project root from /vagrant to
  # /elasticsearch because /vagrant is confusing when there is a project inside
  # the elasticsearch project called vagrant....