* form}), the memory requirement is 8*N bytes for the copy of the dataset plus an overhead which is
 * independent of N (but depends on the quantiles being computed). When calling {@link
 * ScaleAndIndex#computeInPlace computeInPlace} (in {@linkplain ScaleAndIndexes#computeInPlace
 * either form}), only the overhead is required. The number of object allocations is independent of
 * N in both cases.
 *

### Multi destination replication

The replication design for multiple sites works in a similar manner as described above for two site scenario. However there are some
important exceptions.

/**
 * A high-performance utility class for encrypting and decrypting data using cached {@link Cipher} instances.
 * <p>
 * This class provides efficient encryption/decryption by pooling and reusing cipher instances,
 * reducing the overhead of repeated cipher initialization. It supports both string-based keys
 * and {@link Key} objects, with configurable algorithms and character encodings.
 * </p>
 * <p>
 * <strong>Key Features:</strong>
 * </p>
 * <ul>

        issue found, _BigtableXYZDataset (of type DatasetV2) does not implement
        the _as_variant_tensor() of DatasetV1, when moving contrib.bigtable to
        tensorflow_io. Converting into DatasetV2 removes the overheads to
        maintain V1 while we are moving into TF 2.0.
    *   Add dataset ops to the graph (or create kernels in Eager execution)
        during the python Dataset object creation instead doing it during

   * non-static, and is initialized/removed frequently - this causes churn in the Thread specific
   * hashmaps. Using a static ThreadLocal to avoid that overhead would mean that different
   * ExecutionSequencer objects interfere with each other, which would be undesirable, in addition
   * to increasing the memory footprint of every thread that interacted with it. In order to release

  ) : Sink {
    /**
     * Buffer of outgoing data. This batches writes of small writes into this sink as larges frames
     * written to the outgoing connection. Batching saves the (small) framing overhead.
     */
    private val sendBuffer = Buffer()

    /** Trailers to send at the end of the stream. */
    var trailers: Headers? = null

    var closed: Boolean = false

    test to timeout but it could be useful when you need to execute a single
    test or more in general your filtered/selected tests have a very low
    execution time and the sharding
    [could create an overhead on the test execution](https://github.com/bazelbuild/bazel/issues/2113#issuecomment-264054799).

2.  Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.

    ```bash

   * - quartiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace;
   * - scale with index and with indexes taking int-varargs, and with all overloads of compute
   *   taking a double-collection and with computeInPlace;
   * - scale with indexes taking integer-collection with compute taking a double-collection and with
   *   computeInPlace;

   * - quartiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace;
   * - scale with index and with indexes taking int-varargs, and with all overloads of compute
   *   taking a double-collection and with computeInPlace;
   * - scale with indexes taking integer-collection with compute taking a double-collection and with
   *   computeInPlace;

   * non-static, and is initialized/removed frequently - this causes churn in the Thread specific
   * hashmaps. Using a static ThreadLocal to avoid that overhead would mean that different
   * ExecutionSequencer objects interfere with each other, which would be undesirable, in addition
   * to increasing the memory footprint of every thread that interacted with it. In order to release