*
 * [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".
   */

xe=!0,Le=!0,Ie=!1,Me=!0,Ue=!1,ze=!0,Pe=!1,Fe=!1,He=!1,Be=!1,Ge=!1,We=!1,Ye=!0,je=!1,Xe=!0,qe=!1,$e={},Ke=null;const Ve=R({},["annotation-xml","audio","colgroup","desc","foreignobject","head","iframe","math","mi","mn","mo","ms","mtext","noembed","noframes","noscript","plaintext","script","style","svg","template","thead","title","video","xmp"]);let Ze=null;const Je=R({},["audio","video","img","source","image","track"]);let Qe=null;const et=R({},["alt","class","for","id","label","name","pattern","placeholder"...

### Key Features

- **Multi-Protocol Support**: HTTP/HTTPS, File System, FTP, SMB/CIFS, Cloud Storage (MinIO, S3)
- **Comprehensive Content Extraction**: Office documents, PDFs, archives, images, audio/video files
- **Multi-Threading**: Configurable thread pools for high-performance crawling
- **Fault Tolerance**: Built-in retry mechanisms and error handling

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\

 | `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`       |

You could also use it to stream **large binary files**, where you stream each chunk of data as you read it, without having to read it all in memory at once.

You could also stream **video** or **audio** this way, it could even be generated as you process and send it.

## A `StreamingResponse` with `yield` { #a-streamingresponse-with-yield }

        // 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);

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.

    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....

        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