* Subclasses only differ in whether they save modifications to disk.
 *
 * <h2>Supported Upgrades</h2>
 *
 * <h3>Model Version Upgrades</h3>
 * <ul>
 *   <li><strong>4.0.0 → 4.1.0</strong>: Upgrades Maven 3.x POMs to Maven 4.1.0 format</li>
 * </ul>
 *
 * <h3>4.0.0 → 4.1.0 Upgrade Process</h3>
 * <ol>
 *   <li><strong>Namespace Update</strong>: Changes namespace from Maven 4.0.0 to 4.1.0 for all elements</li>

 * </ul>
 *
 * <h2>Usage Examples</h2>
 *
 * <h3>Check for Available Upgrades</h3>
 * <pre>{@code
 * mvnup check
 * }</pre>
 *
 * <h3>Apply All Upgrades</h3>
 * <pre>{@code
 * mvnup apply --all
 * }</pre>
 *
 * <h3>Upgrade to Maven 4.1.0 with Inference</h3>
 * <pre>{@code
 * mvnup apply --model 4.1.0 --infer
 * }</pre>
 *
 * <h2>Extension Points</h2>
 *

 *
 * <h3>String utilities</h3>
 *
 * <ul>
 *   <li>{@link Ascii}
 *   <li>{@link CaseFormat}
 *   <li>{@link CharMatcher}
 *   <li>{@link Splitter}
 *   <li>{@link Strings}
 * </ul>
 *
 * <h3>Function types</h3>
 *
 * <ul>
 *   <li>{@link Converter}
 *   <li>{@link Equivalence}
 * </ul>
 *
 * <h3>Other</h3>
 *
 * <ul>
 *   <li>{@link Enums}

   * values. The count must be non-zero.
   *
   * <p>If these values are a sample drawn from a population, this is also an unbiased estimator of
   * the arithmetic mean of the population.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains {@link Double#NaN} then the result is {@link Double#NaN}. If it
   * contains both {@link Double#POSITIVE_INFINITY} and {@link Double#NEGATIVE_INFINITY} then the

   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link
   * Double#NEGATIVE_INFINITY}, or {@link Double#NaN}) then the result is {@link Double#NaN}.
   *

   * values. The count must be non-zero.
   *
   * <p>If these values are a sample drawn from a population, this is also an unbiased estimator of
   * the arithmetic mean of the population.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains {@link Double#NaN} then the result is {@link Double#NaN}. If it
   * contains both {@link Double#POSITIVE_INFINITY} and {@link Double#NEGATIVE_INFINITY} then the

<p>
The Go memory model specifies the conditions under which
reads of a variable in one goroutine can be guaranteed to
observe values produced by writes to the same variable in a different goroutine.
</p>


<h3 id="advice">Advice</h3>

<p>
Programs that modify data being simultaneously accessed by multiple goroutines
must serialize such access.
</p>

<p>

   * is not guaranteed to return zero when the dataset consists of the same pair of values multiple
   * times, due to numerical errors.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link
   * Double#NEGATIVE_INFINITY}, or {@link Double#NaN}) then the result is {@link Double#NaN}.
   *

 *
 * <p>This interface is meant to be used as the type of a parameter to graph algorithms (such as
 * topological sort) that only need a way of accessing the predecessors of a node in a graph.
 *
 * <h3>Usage</h3>
 *
 * Given an algorithm, for example:
 *
 * {@snippet :
 * public <N> someGraphAlgorithm(N startNode, PredecessorsFunction<N> predecessorsFunction);
 * }
 *

 *
 * <p>This interface is meant to be used as the type of a parameter to graph algorithms (such as
 * breadth first traversal) that only need a way of accessing the successors of a node in a graph.
 *
 * <h3>Usage</h3>
 *
 * Given an algorithm, for example:
 *
 * {@snippet :
 * public <N> someGraphAlgorithm(N startNode, SuccessorsFunction<N> successorsFunction);
 * }
 *