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).

```

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

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

		}

		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)

     * @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

    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()

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

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

#   TF_BISECT_BAD: First bad commit (e.g. commit from the first failing job)
#   TF_BISECT_SCRIPT: The build script path relative to the TF root dir, e.g.
#     ci/official/wheel.sh
#   TFCI: The env config path, relative to the TF root dir, e.g.
#     ci/official/envs/an_env_config
#
# Note that you can combine bisect.sh with any.sh to bisect a single test:
#
#   export TFCI=...

_current_module.app.flags = flags  # pylint: disable=undefined-variable
setattr(_current_module, "flags", flags)

_major_api_version = 1

# Add module aliases from Keras to TF.
# Some tf endpoints actually lives under Keras.
_current_module.layers = _KerasLazyLoader(
    globals(),
    submodule="__internal__.legacy.layers",
    name="layers",
    mode="v1")
if _tf_uses_legacy_keras: