assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);

    assertThat(Stats.of(NEGATIVE_INFINITY).mean()).isNegativeInfinity();
    assertThat(Stats.of(NaN).mean()).isNaN();
    assertThat(TWO_VALUES_STATS.mean()).isWithin(ALLOWED_ERROR).of(TWO_VALUES_MEAN);
    // For datasets of many double values created from an array, we test many combinations of finite
    // and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      double mean = Stats.of(values.asArray()).mean();

                int type = SMBUtil.readInt2(buffer, ncpos);
                int dataLen = SMBUtil.readInt2(buffer, ncpos + 2);
                ncpos += 4;
                ncpos += 4; // Reserved
                NegotiateContextResponse ctx = createContext(type);
                if ( ctx != null ) {
                    ctx.decode(buffer, ncpos, dataLen);
                    contexts[ i ] = ctx;
                }

    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 =

				int64(testCase.dataLen), testCase.chunkSize, bytes.NewReader(testCase.data),
				testCase.accessKey, testCase.secretKey)
		case testCase.contentEncoding == "":
			req, err = newTestStreamingSignedRequest(http.MethodPut,
				getPutObjectURL("", testCase.bucketName, testCase.objectName),
				int64(testCase.dataLen), testCase.chunkSize, bytes.NewReader(testCase.data),

    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 =

        .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();

        .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();

        byte[] dataHmac = getHmac(dataChecksum, codeHmac);
        SecretKeySpec dataKey = new SecretKeySpec(dataHmac, KerberosConstants.RC4_ALGORITHM);

        cipher = Cipher.getInstance(KerberosConstants.RC4_ALGORITHM);
        cipher.init(Cipher.DECRYPT_MODE, dataKey);

        int plainDataLength = data.length - KerberosConstants.CHECKSUM_SIZE;

#### 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.