or networks.

For performance reasons, the default TensorFlow server does not include any
authorization protocol and sends messages unencrypted. It accepts connections
from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user

## Supported Protocols and Formats

### Protocols
- **HTTP/HTTPS**: Full web crawling support with cookies, authentication, redirects
- **File System**: Local and network file system access
- **FTP**: FTP server crawling with authentication
- **SMB/CIFS**: Windows network shares
- **Storage**: Cloud storage systems (MinIO, S3-compatible)

### Content Formats

#### Office Documents
- Microsoft Office (Word, Excel, PowerPoint)

libcurand-12-1
libcusolver-dev-12-1
libcusparse-dev-12-1
libcublas-12-1
libcublas-dev-12-1
libnccl-dev=2.18.3-1+cuda12.1
libnccl2=2.18.3-1+cuda12.1
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.8.0.87-1

libcurand-12-3
libcusolver-dev-12-3
libcusparse-dev-12-3
libcublas-12-3
libcublas-dev-12-3
libnccl-dev=2.19.3-1+cuda12.3
libnccl2=2.19.3-1+cuda12.3
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.1.1.17-1

libcurand-12-8
libcusolver-dev-12-8
libcusparse-dev-12-8
libcublas-12-8
libcublas-dev-12-8
libnccl-dev=2.25.1-1+cuda12.8
libnccl2=2.25.1-1+cuda12.8
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.8.0.87-1

libcurand-13-0
libcusolver-dev-13-0
libcusparse-dev-13-0
libcublas-13-0
libcublas-dev-13-0
libnccl-dev=2.27.7-1+cuda13.0
libnccl2=2.27.7-1+cuda13.0
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-13=9.12.0.46-1

libcurand-12-1
libcusolver-dev-12-1
libcusparse-dev-12-1
libcublas-12-1
libcublas-dev-12-1
libnccl-dev=2.18.3-1+cuda12.1
libnccl2=2.18.3-1+cuda12.1
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.1.1.17-1

       "$ErrorActionPreference='Stop'; $ProgressPreference='SilentlyContinue';$VerbosePreference = 'Continue';"]

# This should only be necessary when running on A GCP VM, on a default
# network, which has the MTU of 1460,
# due to 40 bytes being reserved for GCP's internal usage.
# Note, an invalid sub-interface name will lead to an obscure error, e.g.:
# "The filename, directory name, or volume label syntax is incorrect."

		}
	}
}

// TestWriterReadFromCounts tests that using io.Copy to copy into a
// bufio.Writer does not prematurely flush the buffer. For example, when
// buffering writes to a network socket, excessive network writes should be
// avoided.
func TestWriterReadFromCounts(t *testing.T) {
	var w0 writeCountingDiscard
	b0 := NewWriterSize(&w0, 1234)
	b0.WriteString(strings.Repeat("x", 1000))
	if w0 != 0 {

    <acronym>AI</acronym>
    <_comment>Adobe Illustrator Artwork</_comment>
    <tika:link>http://en.wikipedia.org/wiki/Adobe_Illustrator_Artwork</tika:link>
    <glob pattern="*.ai"/>
    <sub-class-of type="application/pdf"/>
  </mime-type>
  <mime-type type="application/illustrator+ps">
    <acronym>AI</acronym>
    <_comment>Adobe Illustrator Artwork -- the older postscript based AI files</_comment>