application/zip                zip jar          # ZIP Compressed File
audio/basic                    au snd           # Audio Sound File
audio/x-aiff                   aif aiff aifc    # AIFF Sound File
audio/x-midi                   mid              # MIDI Sound File
audio/x-pn-realaudio           ra ram rm rpm    # REALAUDIO Sound File
audio/x-wav                    wav              # WAV Sound File
audio/x-mpegurl                mp3              # MP3 Sound File

 | `7z`  | (7-Zip)     |
 | `xz`  | (LZMA)      |
 | `mp4` | (MP4)       |
 | `mkv` | (MKV media) |
 | `mov` | (MOV)       |

- Content-Types

 | `video/*`                |
 | `audio/*`                |
 | `application/zip`        |
 | `application/x-gzip`     |
 | `application/zip`        |
 | `application/x-bz2`      |
 | `application/x-compress` |
 | `application/x-xz`       |

 *
 * [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".
   */

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.

---

Common examples of CPU bound operations are things that require complex math processing.

For example:

* **Audio** or **image processing**.
* **Computer vision**: an image is composed of millions of pixels, each pixel has 3 values / colors, processing that normally requires computing something on those pixels, all at the same time.

---

Typische Beispiele für CPU-lastige Vorgänge sind Dinge, die komplexe mathematische Berechnungen erfordern.

Zum Beispiel:

* **Audio-** oder **Bildbearbeitung**.
* **Computer Vision**: Ein Bild besteht aus Millionen von Pixeln, jedes Pixel hat 3 Werte / Farben, die Verarbeitung erfordert normalerweise, Berechnungen mit diesen Pixeln durchzuführen, alles zur gleichen Zeit.

---

Ejemplos típicos de operaciones dependientes de CPU son cosas que requieren un procesamiento matemático complejo.

Por ejemplo:

* **Audio** o **procesamiento de imágenes**.

---

Des exemples communs d'opérations "CPU bounds" sont les procédés qui requièrent des traitements mathématiques complexes.

Par exemple :

* Traitements d'**audio** et d'**images**.

  fun shoutcast() {
    server.enqueue(
      MockResponse.Builder()
        .status("ICY 200 OK")
        .addHeader("Accept-Ranges: none")
        .addHeader("Content-Type: audio/mpeg")
        .addHeader("icy-br:128")
        .addHeader("ice-audio-info: bitrate=128;samplerate=44100;channels=2")
        .addHeader("icy-br:128")
        .addHeader("icy-description:Rock")
        .addHeader("icy-genre:riders")

        `tf.keras.layers.EinsumDense`.
    *   Added `tf.keras.utils.audio_dataset_from_directory` utility to easily
        generate audio classification datasets from directories of `.wav` files.
    *   Added `subset="both"` support in
        `tf.keras.utils.image_dataset_from_directory`,`tf.keras.utils.text_dataset_from_directory`,
        and `audio_dataset_from_directory`, to be used with the