Since models are practically programs that TensorFlow executes, using untrusted
models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.

### Compilation

        "search": ["testSearch"],
        "options": ["testOption"]
    },
    "prevResult": {
        "cniversion": "%s",
        "interfaces": [
            {
                "name": "%s",
                "sandbox": "%s"
            }
        ],
        "ips": [
            {
                "version": "4",
                "address": "10.0.0.2/24",
                "gateway": "10.0.0.1",
                "interface": 0

   * Supports-Loading-Mode}</a> header field name. This can be used to specify, for example, <a
   * href="https://developer.chrome.com/docs/privacy-sandbox/fenced-frame/#server-opt-in">fenced
   * frames</a>.
   *
   * @since 32.0.0
   */
  public static final String SUPPORTS_LOADING_MODE = "Supports-Loading-Mode";

  /**

    and
    [`tf.keras.layers.CuDNNLSTM`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNLSTM)
    layers.
    [Try it](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb?linkId=53292082).
*   Adding support of core