have different security properties and provide different levels of confidence
when dealing with untrusted data. Based on the security history of these
libraries we consider that it is safe to work with untrusted inputs for PNG,
BMP, GIF, WAV, RAW, RAW\_PADDED, CSV and PROTO formats. All other input formats,
including tensorflow-io should be sandboxed if used to process untrusted data.

For example, if an attacker were to upload a malicious video file, they could

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\
image/jpeg=jpg\n\

index.reindex.size=100

     * 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>
     * image/png=png<br>
     * image/svg+xml=svg<br>
     * image/tiff=tiff<br>

        `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