* - 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;

	if err != nil {
		writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)
		return
	}

	// We have to adjust the size of encrypted parts since encrypted parts
	// are slightly larger due to encryption overhead.
	// Further, we have to adjust the ETags of parts when using SSE-S3.
	// Due to AWS S3, SSE-S3 encrypted parts return the plaintext ETag
	// being the content MD5 of that particular part. This is not the

	if r.ContentLength <= 0 {
		writeErrorResponse(ctx, w, errorCodes.ToAPIErr(ErrMissingContentLength), r.URL)
		return
	}

	// The max. XML contains 100000 object names (each at most 1024 bytes long) + XML overhead
	const maxBodySize = 2 * 100000 * 1024

	if r.ContentLength > maxBodySize {
		writeErrorResponse(ctx, w, errorCodes.ToAPIErr(ErrEntityTooLarge), r.URL)
		return
	}

    fun addInterceptor(interceptor: Interceptor) =
      apply {
        interceptors += interceptor
      }

    @JvmName("-addInterceptor") // Prefix with '-' to prevent ambiguous overloads from Java.
    inline fun addInterceptor(crossinline block: (chain: Interceptor.Chain) -> Response) =
      addInterceptor(Interceptor { chain -> block(chain) })

    /**

        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

}

// Returns the compressed offset which should be skipped.
// If encrypted offsets are adjusted for encrypted block headers/trailers.
// Since de-compression is after decryption encryption overhead is only added to compressedOffset.
func getCompressedOffsets(oi ObjectInfo, offset int64, decrypt func([]byte) ([]byte, error)) (compressedOffset int64, partSkip int64, firstPart int, decryptSkip int64, seqNum uint32) {

	case size == 0:
		buffer = make([]byte, 1) // Allocate at least a byte to reach EOF
	case size == -1:
		if size := data.ActualSize(); size > 0 && size < fi.Erasure.BlockSize {
			// Account for padding and forced compression overhead and encryption.
			buffer = make([]byte, data.ActualSize()+256+32+32, data.ActualSize()*2+512)
		} else {
			buffer = globalBytePoolCap.Load().Get()
			defer globalBytePoolCap.Load().Put(buffer)
		}

	return ARNPrefix + kmsID
}

// DecryptETags decryptes the ETag of all ObjectInfos using the KMS.
//
// It adjusts the size of all encrypted objects since encrypted
// objects are slightly larger due to encryption overhead.
// Further, it decrypts all single-part SSE-S3 encrypted objects
// and formats ETags of SSE-C / SSE-KMS encrypted objects to
// be AWS S3 compliant.
//

<p>
it can happen that <code>g</code> prints <code>2</code> and then <code>0</code>.
</p>

<p>
This fact invalidates a few common idioms.
</p>

<p>
Double-checked locking is an attempt to avoid the overhead of synchronization.
For example, the <code>twoprint</code> program might be
incorrectly written as:
</p>

<pre>
var a string
var done bool

func setup() {
	a = "hello, world"
	done = true