video/3g2=3g2\n\
video/x-msvideo=avi\n\
video/x-flv=flv\n\
video/mpeg=mpeg\n\
video/mp4=mp4\n\
video/ogv=ogv\n\
video/quicktime=qt\n\
video/x-m4v=m4v\n\
audio/x-aif=aif\n\
audio/midi=midi\n\
audio/mpga=mpga\n\
audio/mp4=mp4a\n\
audio/ogg=oga\n\
audio/x-wav=wav\n\
image/webp=webp\n\
image/bmp=bmp\n\
image/x-icon=ico\n\
image/x-icon=ico\n\
image/png=png\n\
image/svg+xml=svg\n\
image/tiff=tiff\n\

	// 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

---

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**.

     * video/mpeg=mpeg<br>
     * video/mp4=mp4<br>
     * video/ogv=ogv<br>
     * video/quicktime=qt<br>
     * video/x-m4v=m4v<br>
     * audio/x-aif=aif<br>
     * audio/midi=midi<br>
     * audio/mpga=mpga<br>
     * audio/mp4=mp4a<br>
     * audio/ogg=oga<br>
     * audio/x-wav=wav<br>
     * image/webp=webp<br>
     * image/bmp=bmp<br>
     * image/x-icon=ico<br>
     * image/x-icon=ico<br>

---

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

Par exemple :

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

---

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.

        `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