long[] dataset = Longs.toArray(SIXTEEN_SQUARES_LONGS);
    assertThat(Quantiles.scale(10).index(1).compute(dataset))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_DECILE_1);
    assertThat(dataset).asList().isEqualTo(SIXTEEN_SQUARES_LONGS);
  }

  public void testScale_index_compute_intVarargs() {
    int[] dataset = Ints.toArray(SIXTEEN_SQUARES_INTEGERS);

        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);
        }

  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

    }

    /**
     * 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(',', '"', '"');

*   `tf.data`

    *   `tf.data.Dataset.zip` now supports Python-style zipping, i.e. `Dataset.zip(a, b, c)`.
    * `tf.data.Dataset.shuffle` now supports `tf.data.UNKNOWN_CARDINALITY` When doing a "full shuffle" using  `dataset = dataset.shuffle(dataset.cardinality())`. But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).

#### 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.

   *
   * <p>This is guaranteed to return zero if the dataset contains only exactly one finite value. It
   * is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

          .immutableCopy();

  private double[] dataset;

  @Override
  protected void setUp() {
    dataset = new double[DATASET_SIZE];
    for (int i = 0; i < DATASET_SIZE; i++) {
      dataset[i] = rng.nextDouble();
    }
  }

  public void testSingleQuantile_median() {
    double referenceValue = REFERENCE_ALGORITHM.singleQuantile(1, 2, dataset.clone());

   *
   * <p>This is guaranteed to return zero if the dataset contains a single pair of finite values. It
   * 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

  SORTING {

    @Override
    double singleQuantile(int index, int scale, double[] dataset) {
      Arrays.sort(dataset);
      return singleQuantileFromSorted(index, scale, dataset);
    }

    @Override
    Map<Integer, Double> multipleQuantiles(
        Collection<Integer> indexes, int scale, double[] dataset) {
      Arrays.sort(dataset);
      ImmutableMap.Builder<Integer, Double> builder = ImmutableMap.builder();