@Test
    void testNoProblems() {
        ToolchainsBuildingException e = new ToolchainsBuildingException(Collections.emptyList());
        assertEquals("0 problems were encountered while building the effective toolchains" + LS, e.getMessage());
    }

    @Test
    void testOneProblem() {
        ProblemCollector problemCollector = ProblemCollectorFactory.newInstance(null);

  public void testSingleSimpleStats() {
    StatsCounter counter = new SimpleStatsCounter();
    for (int i = 0; i < 11; i++) {
      counter.recordHits(1);
    }
    for (int i = 0; i < 13; i++) {
      counter.recordLoadSuccess(i);
    }
    for (int i = 0; i < 17; i++) {
      counter.recordLoadException(i);
    }
    for (int i = 0; i < 23; i++) {
      counter.recordMisses(1);
    }

    // Our executor hasn't run any tasks yet.
    assertEquals(0, totalCalls.get());
    fakePool.runAll();
    assertEquals(2, totalCalls.get());
    // Queue is empty so no runner should be scheduled.
    assertFalse(fakePool.hasNext());

    // Check that execute can be safely repeated
    e.execute(intCounter);
    e.execute(intCounter);
    e.execute(intCounter);
    // No change yet.

//
// The code under test will have created streamz counters like this:
// auto* streamz = tensorflow::monitoring::Counter<1>::New("name",
// "description", "label");
// and then incremented that counter for various values of label:
// streamz->GetCell("label-value")->IncrementBy(1);
//
// The test code can then read and test the value of that counter:
//
// auto* reader = TFE_MonitoringNewCounterReader("name");
// test();

          echo "Database initialized successfully";
          break;
        fi;
        echo "Waiting for database to be ready... ($$counter/12)";
        sleep 5;
        counter=$$(($$counter + 1));
      done;
      # timeout handling
      if [ $$counter -gt 20 ]; then
        echo "Error: Database failed to start within timeout";
        exit 1;
      fi;

class ContributorsResults(BaseModel):
    contributors: Counter[str]
    translation_reviewers: Counter[str]
    translators: Counter[str]
    authors: dict[str, Author]


def get_contributors(pr_nodes: list[PullRequestNode]) -> ContributorsResults:
    contributors = Counter[str]()
    translation_reviewers = Counter[str]()
    translators = Counter[str]()
    authors: dict[str, Author] = {}

    // Our executor hasn't run any tasks yet.
    assertEquals(0, totalCalls.get());
    fakePool.runAll();
    assertEquals(2, totalCalls.get());
    // Queue is empty so no runner should be scheduled.
    assertFalse(fakePool.hasNext());

    // Check that execute can be safely repeated
    e.execute(intCounter);
    e.execute(intCounter);
    e.execute(intCounter);
    // No change yet.

      void doIt(ImmutableDoubleArray.Builder builder, AtomicInteger counter) {
        builder.add(counter.getAndIncrement());
      }
    },
    ADD_ARRAY {
      @Override
      void doIt(ImmutableDoubleArray.Builder builder, AtomicInteger counter) {
        double[] array = new double[random.nextInt(10)];
        for (int i = 0; i < array.length; i++) {
          array[i] = counter.getAndIncrement();
        }

      void doIt(ImmutableIntArray.Builder builder, AtomicInteger counter) {
        builder.add(counter.getAndIncrement());
      }
    },
    ADD_ARRAY {
      @Override
      void doIt(ImmutableIntArray.Builder builder, AtomicInteger counter) {
        int[] array = new int[random.nextInt(10)];
        for (int i = 0; i < array.length; i++) {
          array[i] = counter.getAndIncrement();
        }
        builder.addAll(array);

        a new API for creating `Dataset`s from lists of elements.
    *   Graduated experimental APIs:
        *   [`tf.data.Dataset.counter`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset/#counter),
            which creates `Dataset`s of indefinite sequences of numbers.
        *   [`tf.data.Dataset.ignore_errors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset/#ignore_errors),