*  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();

				notZero = notZero || !zero
			}

			if notZero {
				dataKey := utils.ToStringKey(fieldValues...)
				if _, ok := dataResults[dataKey]; !ok {
					results = append(results, fieldValues)
					dataResults[dataKey] = []reflect.Value{elem}
				} else {
					dataResults[dataKey] = append(dataResults[dataKey], elem)
				}
			}
		}
	}

	return dataResults, results
}

    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 =

        setEncoding((String) src.get(ENCODING));
        final String dataStr = (String) src.get(DATA);
        if (dataStr != null) {
            try {
                setData(Base64Util.decode(dataStr));
            } catch (final Exception e) {
                throw new OpenSearchAccessException("data: " + dataStr, e);
            }
        }
    }

    /**

                dstIndex += 4;
                SMBUtil.writeInt4(0, dst, dstIndex);
                dstIndex += 4; // Reserved
                final int dataLen = size8(nc.encode(dst, dstIndex));
                SMBUtil.writeInt2(dataLen, dst, lenOffset);
                dstIndex += dataLen;
            }
        }
        return dstIndex - start;
    }

    /**
     * {@inheritDoc}
     *

     */
    private static byte[] createBuffer(int dataLen, byte[] dataBytes, String params) throws UnsupportedEncodingException {
        final byte[] paramBytes = params.getBytes(SmbConstants.DEFAULT_OEM_ENCODING);
        byte[] buffer = new byte[10 + paramBytes.length + dataLen];
        // copy params
        System.arraycopy(paramBytes, 0, buffer, 10, paramBytes.length);

                final int dataLen = SMBUtil.readInt2(buffer, ncpos + 2);

                // Validate context data length
                if (dataLen < 0 || dataLen > 1024) { // 1KB max per context
                    throw new SMBProtocolDecodingException(
                            "Invalid negotiate context data length: " + dataLen + " at position " + i + " (must be 0-1024)");
                }

#### 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.