* value slightly too high than slightly too low. If the value is not exact, the {@link
   * ImmutableLongArray} that is built will very likely occupy more memory than strictly necessary;
   * to trim memory usage, build using {@code builder.build().trimmed()}.
   */
  public static Builder builder(int initialCapacity) {
    checkArgument(initialCapacity >= 0, "Invalid initialCapacity: %s", initialCapacity);

   * value slightly too high than slightly too low. If the value is not exact, the {@link
   * ImmutableIntArray} that is built will very likely occupy more memory than strictly necessary;
   * to trim memory usage, build using {@code builder.build().trimmed()}.
   */
  public static Builder builder(int initialCapacity) {
    checkArgument(initialCapacity >= 0, "Invalid initialCapacity: %s", initialCapacity);

        configuration.setLocalRepository(getLocalRepository());
        initRepoSession(configuration);

        return projectBuilder.build(pom, allowStub, configuration).getProject();
    }

    /**
     * Check that we can build ok from the middle pom of a (parent,child,grandchild) hierarchy
     */
    @Test
    void testBuildFromMiddlePom() throws Exception {

     * since it represents a POM file that is actively being built and may change during the build process.
     * <p>
     * The request-scoped retention policy ensures that:
     * <ul>
     *   <li>Changes to the POM file during the build are detected</li>
     *   <li>Cache entries don't persist beyond the current build request</li>
     *   <li>Memory is freed once the build request completes</li>
     * </ul>
     */

fi
# Import tf nightly wheel built with numpy2 from PyPI in numpy1 env for testing.
# This aims to maintain TF compatibility with NumPy 1.x until 2025 b/361369076.
if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  # Uninstall tf nightly wheel built with numpy1.
  "$python" -m pip uninstall -y tf_nightly_numpy1
  # Install tf nightly cpu wheel built with numpy2.x from PyPI in numpy1.x env.

    /**
     * Returns the path to the built project artifact file, if the project has been built.
     * This path is only available after the artifact has been produced during the build lifecycle.
     *
     * @param project the project to get the artifact path for
     * @return an Optional containing the path to the built artifact if available,
     *         or empty if the artifact hasn't been built yet
     */
    @Nonnull

      }

      ImmutableRangeSet<Integer> built = builder.build();
      assertEquals(mutable, built);
      assertEquals(ImmutableRangeSet.copyOf(mutable), built);
      assertEquals(mutable.complement(), built.complement());

      for (int i = 0; i <= 11; i++) {
        assertEquals(mutable.contains(i), built.contains(i));
      }

      SerializableTester.reserializeAndAssert(built);

 */
package org.apache.maven.execution;

import java.util.List;

/**
 * This class holds the information required to enable resuming a Maven build with {@code --resume}.
 */
public class BuildResumptionData {
    /**
     * The list of projects that remain to be built.
     */
    private final List<String> remainingProjects;

    public BuildResumptionData(final List<String> remainingProjects) {

     * @throws ModelBuildingException If the effective model could not be built.
     */
    ModelBuildingResult build(ModelBuildingRequest request) throws ModelBuildingException;

    /**
     * Builds the effective model by completing the specified interim result which was produced by a previous call to
     * {@link #build(ModelBuildingRequest)} with {@link ModelBuildingRequest#isTwoPhaseBuilding()} being {@code true}.

labels: 'TFLiteConverter'

---

### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):

### 2. Code

Provide code to help us reproduce your issues using one of the following options:

#### Option A: Reference colab notebooks