* IllegalStateException} if it is accessed in any way, with the following exceptions:
   *
   * <ul>
   *   <li>{@code view.equals(view)} evaluates to {@code true} (but any other {@code equals()}
   *       expression involving {@code view} will throw)
   *   <li>{@code hashCode()} does not throw
   *   <li>if {@code node} is re-added to the graph after having been removed, {@code view}'s
   *       behavior is undefined
   * </ul>
   *

                new TypeDeriver());
    }

    /**
     * This method produces "surrogate" type registry that is static: it aims users that want to use
     * Maven-Resolver without involving Maven Core and related things.
     * <p>
     * This type registry is NOT used by Maven Core: Maven replaces it during Session creation with a type registry
     * that supports extending it (i.e. via Maven Extensions).

        for (final RequestData requestData : anchorList) {
            urlSet.add(requestData.getUrl());
        }
        return new ArrayList<>(urlSet);
    }

    /**
     * Gets the base URL for resolving relative URLs.
     *
     * @param currentUrl the current URL
     * @param baseHref the base href value from HTML
     * @return the base URL
     * @throws MalformedURLException if the URL is malformed

* A PR should have a specific use case that it is solving.

* If the PR is for a feature, it should have docs.
    * Unless it's a feature we want to discourage, like support for a corner case that we don't want users to use.

  }

  /**
   * The supplied multimap will be mutated and an unmodifiable instance used in its stead. If the
   * multimap does not support null keys or values, alternatives may be specified for tests
   * involving nulls.
   */
  private static void checkUnmodifiableMultimap(
      Multimap<@Nullable String, @Nullable Integer> multimap,
      boolean permitsDuplicates,
      @Nullable String nullKey,

*   `tf.debugging`
    *   Add `tf.debugging.enable_check_numerics()` and
        `tf.debugging.disable_check_numerics()` to help debugging the root
        causes of issues involving infinities and `NaN`s.
*   `tf.distribute`
    *   Custom training loop support on TPUs and TPU pods is available through
        `strategy.experimental_distribute_dataset`,