// in tests only.)
TF_CAPI_EXPORT int TF_PickUnusedPortOrDie(void);

// Fast path method that makes constructing a single scalar tensor require less
// overhead and copies.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandleFromScalar(
    TF_DataType data_type, void* data, size_t len, TF_Status* status);

// Specify the server_def that enables collective ops.

   * serializable.
   *
   * <p><b>Java 8+ users:</b> Use {@code thisComparator.thenComparing(secondaryComparator)} instead.
   * Depending on what {@code secondaryComparator} is, one of the other overloads of {@code
   * thenComparing} may be even more useful.
   */
  public <U extends T> Ordering<U> compound(Comparator<? super U> secondaryComparator) {

But if you are certain that the content that you are returning is **serializable with JSON**, you can pass it directly to the response class and avoid the extra overhead that FastAPI would have by passing your return content through the `jsonable_encoder` before passing it to the response class.

{* ../../docs_src/custom_response/tutorial001b_py39.py hl[2,7] *}

/// info

The *Secure Channel* splits the object content into chunks of a fixed size of `65536` bytes. The last chunk may be smaller to avoid adding additional overhead and is treated specially to prevent truncation attacks. The nonce value is 96 bits long and generated randomly per object / multi-part part. The *Secure Channel* supports plaintexts up to `65536 * 2^32 = 256 TiB`.

#### Randomness

// 2. all type parameters must all be reifeid or values (TypeOf, KClass or Class)
// 3. when overloading, prefer TypeOf over Class
// 4. in case the policy forbids your overloads, discuss
private
fun kotlinExtensionFunctionsFor(type: ApiType, sinceSupplier: (String) -> String?): Sequence<KotlinExtensionFunction> =
    candidatesForExtensionFrom(type)
        .sortedWithTypeOfTakingFunctionsFirst()

 * 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.

        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