# RECORD file which contains hashes of all included files.
  python3 -m wheel pack "${pkg_name}"
done

# Switch back to the original working directory. This is needed to ensure that
# cleanup steps at the end of the script works as expected.
popd

echo "Following installer wheels were generated: "
ls "${TFCI_OUTPUT_DIR}"/*.whl

if [[ "$TFCI_ARTIFACT_STAGING_GCS_ENABLE" == 1 ]]; then
  # Note: -n disables overwriting previously created files.

      fail("setValue on unmodifiable Map.Entry succeeded");
    } catch (UnsupportedOperationException expected) {
    }
  }

  /**
   * Verifies that an Iterator is unmodifiable.
   *
   * <p>This test only works with iterators that iterate over a finite set.
   */
  public static void assertIteratorIsUnmodifiable(Iterator<?> iterator) {
    while (iterator.hasNext()) {
      iterator.next();
      try {
        iterator.remove();

register_toolchains("@rules_ml_toolchain//cc:linux_aarch64_linux_aarch64_cuda")

# Initialize the TensorFlow repository and all dependencies.
#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.

 * <p>If you have an instance of one of the primary {@code common.graph} types ({@link Graph},
 * {@link ValueGraph}, and {@link Network}):
 *
 * {@snippet :
 * someGraphAlgorithm(startNode, graph);
 * }
 *
 * This works because those types each implement {@code PredecessorsFunction}. It will also work
 * with any other implementation of this interface.
 *
 * <p>If you have your own graph implementation based around a custom node type {@code MyNode},

In fact, in some cases, it will even have **two JSON Schemas** in OpenAPI for the same Pydantic model, for input and output, depending on if they have **default values**.

Let's see how that works and how to change it if you need to do that.

## Pydantic Models for Input and Output { #pydantic-models-for-input-and-output }

Let's say you have a Pydantic model with default values, like this one:

### Existing object replication

  - 'Recipes': recipes.md
  - 'Security':
    - 'Security': security/security.md
    - 'Providers': security/security_providers.md
    - 'Configuration History': security/tls_configuration_history.md
  - 'Works with OkHttp': works_with_okhttp.md
  - 'API': 5.x/okhttp/okhttp3/
  - 'Change Logs':
    - 'Change Log': changelogs/changelog.md
    - '4.x Change Log': changelogs/changelog_4x.md

   *
   * @param pattern the pattern on which to filter file names
   */
  public PatternFilenameFilter(Pattern pattern) {
    this.pattern = Preconditions.checkNotNull(pattern);
  }

  /*
   * Our implementation works fine with a null `dir`. However, there's nothing in the documentation
   * of the supertype that suggests that implementations are expected to tolerate null. That said, I

///

To **learn the basics of HTTPS**, from a consumer perspective, check <a href="https://howhttps.works/" class="external-link" target="_blank">https://howhttps.works/</a>.

Now, from a **developer's perspective**, here are several things to keep in mind while thinking about HTTPS:

A major advantage of certificate-based authentication compared to other STS authentication methods, like OpenID Connect or LDAP/AD, is that client authentication works without any additional/external component that must be constantly available. Therefore, certificate-based authentication may provide better availability / lower operational complexity.