*  Fix: Recover gracefully when a cache entry's certificate is corrupted.

 *  Fix: Fail permanently when there's a failure loading the bundled public suffix database.
    This is the dataset that powers `HttpUrl.topPrivateDomain()`.

 *  Fix: Immediately update the connection's flow control window instead of waiting for the
    receiving stream to process it.

(!element) {\n      return {}\n    }\n\n    const attributes = {}\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '')\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n      attributes[pureKey] = normalizeData(element.dataset[key])\n    }\n\n    return attributes\n  },\n\n  getDataAttribute(element, key) {\n    return normali...

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    assertThat(manyValuesAccumulatorByAddAllStatsAccumulator.mean())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    // For datasets of many double values created from an iterable, we test many combinations of
    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();

    assertThat(manyValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =

#### Option A: Reference colab notebooks

1)  Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.

    <acronym>DO</acronym>
    <tika:link>http://www.stata.com/help.cgi?do</tika:link>
    <glob pattern="*.do"/>
  </mime-type>

  <mime-type type="application/x-stata-dta">
    <_comment>Stata DTA Dataset</_comment>
    <acronym>DTA</acronym>
    <tika:link>http://www.stata.com/help.cgi?dta</tika:link>
	 <root-XML localName="stata_dta"/>
    <magic priority="50">

  public static LinearTransformation horizontal(double y) {
    checkArgument(isFinite(y));
    double slope = 0.0;
    return new RegularLinearTransformation(slope, y);
  }

  /**
   * Builds an instance for datasets which contains {@link Double#NaN}. The {@link #isHorizontal}
   * and {@link #isVertical} methods return {@code false} and the {@link #slope}, and {@link

        assertThrows(CIFSException.class, () -> sidResolver.getServerSid(mockContext, testServerName));
    }

    // Test with large datasets
    @Test
    void testResolveSids_LargeArray() throws CIFSException {
        SID[] largeSidArray = new SID[1000];
        for (int i = 0; i < 1000; i++) {
            largeSidArray[i] = mock(SID.class);
        }

    assertFalse(powerSet.contains(Collections.<@Nullable Integer>singleton(null)));
    assertFalse(powerSet.contains(null));
    assertFalse(powerSet.contains((Object) "notASet"));
  }

  public void testPowerSetIteration_manual() {
    ImmutableSet<Integer> elements = ImmutableSet.of(1, 2, 3);
    Set<Set<Integer>> powerSet = powerSet(elements);

    }

    /**
     * Exports all crawling information parameters to CSV format.
     * The CSV output includes: SessionId, SessionCreatedTime, Key, Value, CreatedTime.
     * Uses cursor-based selection to handle large datasets efficiently.
     *
     * @param writer the Writer to output CSV data to
     */
    public void exportCsv(final Writer writer) {
        final CsvConfig cfg = new CsvConfig(',', '"', '"');