-   type: dropdown id: source attributes: label: Source description: Tensorflow

		}

		for j, tf := range v.tests {
			switch tf := tf.(type) {
			case testRead:
				b := make([]byte, tf.cnt)
				n, err := fr.Read(b)
				if got := string(b[:n]); got != tf.wantStr || err != tf.wantErr {
					t.Errorf("test %d.%d, Read(%d):\ngot  (%q, %v)\nwant (%q, %v)", i, j, tf.cnt, got, err, tf.wantStr, tf.wantErr)
				}
			case testWriteTo:

		}

		for j, tf := range v.tests {
			switch tf := tf.(type) {
			case testWrite:
				got, err := fw.Write([]byte(tf.str))
				if got != tf.wantCnt || err != tf.wantErr {
					t.Errorf("test %d.%d, Write(%s):\ngot  (%d, %v)\nwant (%d, %v)", i, j, tf.str, got, err, tf.wantCnt, tf.wantErr)
				}
			case testReadFrom:
				f := &testFile{ops: tf.ops}
				got, err := fw.ReadFrom(f)

typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.10.0.dev
tb-nightly ~= 2.20.0.a

2)  Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).

```

    public byte[] getChallenge(final CIFSContext tf, final Address dc) throws SmbException {
        return getChallenge(tf, dc, 0);
    }

    @Override
    public byte[] getChallenge(final CIFSContext tf, final Address dc, final int port) throws SmbException {
        try (SmbTransportInternal trans = tf.getTransportPool()

#     monolithic:       Build all TF C++ code into a single shared object.
#     dynamic_kernels:  Try to link all kernels dynamically (experimental).
#     dbg:              Build with debug info
#
# TF version options;
#     v2: Build TF v2
#
# Feature and Third party library support options:
#     xla:          Build TF with XLA
#     tpu:          Build TF with TPU support
#     cuda:         Build with CUDA support.

     * @param tf the CIFS context
     * @param domain the domain name to check
     * @return whether the given domain is trusted
     * @throws CIFSException if the operation fails
     */
    boolean isTrustedDomain(CIFSContext tf, String domain) throws CIFSException;

    /**
     * Get a connection to the domain controller for a given domain
     *
     * @param tf the CIFS context

cat <<EOF
IMPORTANT: These tests ran under docker. This script does not clean up the
container for you! You can delete the container with:

$ docker rm -f tf

You can also execute more commands within the container with e.g.:

$ docker exec tf bazel clean
$ docker exec -it tf bash
EOF

typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.10.0.dev
tb-nightly ~= 2.20.0.a