public void testIsPresent_yes() {
    assertTrue(Optional.of("training").isPresent());
  }

  public void testGet_absent() {
    Optional<String> optional = Optional.absent();
    assertThrows(IllegalStateException.class, optional::get);
  }

  public void testGet_present() {
    assertThat(Optional.of("training").get()).isEqualTo("training");
  }

the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In...

          (c-beginning-of-statement-1)
          (let ((start (point)))
            (forward-line -1)
            (if (search-forward " -> {" start t) t nil))))

      (defun my/trailing-paren-p ()
        "Returns true if point is a training paren and semicolon"
        (save-excursion
          (end-of-line)
          (let ((endpoint (point)))
            (beginning-of-line)

  virtual void ClearCachesAndThreadExecutors() = 0;

  // Initialize the step resource container for a training step. This is used
  // in current TF runtime. For tfrt, it is used by fallback op handler.
  virtual void StartStep() = 0;
  // Destroy the step resource container for a training step.
  virtual void EndStep() = 0;

  // Return the Eager Executor for current thread. Please note that Eager

# Using TensorFlow Securely

This document discusses the TensorFlow security model. It describes the security
risks to consider when using models, checkpoints or input data for training or
serving. We also provide guidelines on what constitutes a vulnerability in
TensorFlow and how to report them.

This document applies to other repositories in the TensorFlow organization,
covering security practices for the entirety of the TensorFlow ecosystem.

    [`tf.distribute.Strategy`](https://www.tensorflow.org/beta/guide/distribute_strategy)
    API to distribute training with minimal code changes, yielding great
    out-of-the-box performance. It supports distributed training with Keras
    model.fit, as well as with custom training loops. Multi-GPU support is
    available, along with experimental support for multi worker and Cloud TPUs.
    Check out the

- **[Community Resources](https://gradle.org/resources/)** - Find more community-contributed materials to expand your knowledge.

> 🌟 **Stay connected with the Gradle Community and access the latest news, training, and updates via [Slack](https://gradle.org/slack-invite), [Forum](https://discuss.gradle.org/), and our [Newsletter](https://newsletter.gradle.org)**.

<br><br>
<div align="center" style="margin-top: 20px;">

 [CertificatePinningKotlin]: https://github.com/square/okhttp/blob/master/samples/guide/src/main/java/okhttp3/recipes/kt/CertificatePinning.kt
 [provider_installer]: https://developer.android.com/training/articles/security-gms-provider

   *     not support creating temporary directories securely
   * @deprecated For Android users, see the <a
   *     href="https://developer.android.com/training/data-storage" target="_blank">Data and File
   *     Storage overview</a> to select an appropriate temporary directory (perhaps {@code
   *     context.getCacheDir()}), and create your own directory under that. (For example, you might

    bool clear_existing_contexts);

// Set server def with retries and timeout. This is helpful for fault-tolerant
// initial connection in high-preemption environments, such as
// ParameterServerStrategy training.
// This API is for experimental usage and may be subject to change.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDefWithTimeoutAndRetries(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,