*     be unique to the built instance of the ThreadFactory and will be assigned sequentially. For
   *     example, {@code "rpc-pool-%d"} will generate thread names like {@code "rpc-pool-0"}, {@code
   *     "rpc-pool-1"}, {@code "rpc-pool-2"}, etc.
   * @return this for the builder pattern
   */
  @CanIgnoreReturnValue
  public ThreadFactoryBuilder setNameFormat(String nameFormat) {

	"cmd/internal/obj/wasm"
	"cmd/internal/obj/x86"
	"fmt"
	"strings"
)

// Pseudo-registers whose names are the constant name without the leading R.
const (
	RFP = -(iota + 1)
	RSB
	RSP
	RPC
)

// Arch wraps the link architecture object with more architecture-specific information.
type Arch struct {
	*obj.LinkArch
	// Map of instruction names to enumeration.
	Instructions map[string]obj.As

   *
   * <p>Usage example:
   *
   * {@snippet :
   * // Falling back to a zero counter in case an exception happens when processing the RPC to fetch
   * // counters.
   * ListenableFuture<Integer> faultTolerantFuture =
   *     fetchCounters().catching(FetchException.class, x -> 0, directExecutor());
   * }
   *

        "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service",
        "//tensorflow/core/distributed_runtime/rpc:rpc_rendezvous_mgr",
        "//tensorflow/core/distributed_runtime/rpc/eager:grpc_eager_client",
        "@com_google_absl//absl/memory",
        "@com_google_absl//absl/strings:str_format",
        "@xla//xla/tsl/c:tsl_status_internal",
    ],
    alwayslink = 1,
)

filegroup(

   * value. Otherwise, a call that passes one {@code loader} may return the result of another call
   * with a differently behaving {@code loader}. For example, a call that requests a short timeout
   * for an RPC may wait for a similar call that requests a long timeout, or a call by an
   * unprivileged user may return a resource accessible only to a privileged user making a similar

        "//tensorflow/core/platform:types",
        "//tensorflow/core/profiler/internal:print_model_analysis",
        "//tensorflow/core/profiler/lib:traceme",
        "//tensorflow/core/profiler/rpc/client:profiler_client_impl",
        "//tensorflow/core/profiler/rpc:profiler_server_impl",
        "//tensorflow/core/util:managed_stack_trace",
        "//tensorflow/core:all_kernels",

   *
   * <p>Usage example:
   *
   * {@snippet :
   * ListenableFuture<Integer> fetchCounterFuture = ...;
   *
   * // Falling back to a zero counter in case an exception happens when
   * // processing the RPC to fetch counters.
   * ListenableFuture<Integer> faultTolerantFuture = Futures.catching(
   *     fetchCounterFuture, FetchException.class, x -> 0, directExecutor());
   * }
   *

- Added New RuntimeService streaming RPCs (`StreamPodSandboxes`, `StreamContainers`, `StreamContainerStats`, `StreamPodSandboxStats`, `StreamPodSandboxMetrics`) and New ImageService streaming RPC (`StreamImages`). ([#136987](https://github.com/kubernetes/kubernetes/pull/136987), [@bitoku](https://github.com/bitoku)) [SIG Cluster Lifecycle, Node and Testing]

   *             cancelled the {@code Future}.
   *       </ol>
   *       Given all these possibilities, it is frequently possible for listeners to execute in UI
   *       threads, RPC network threads, or other latency-sensitive threads. In those cases, slow
   *       listeners can harm responsiveness, slow the system as a whole, or worse. (See also the
   *       note about locking below.)

        `RaggedTensor` outputs from the layer.

*   distribute.experimental.rpc package:

    *   distribute.experimental.rpc package introduces APIs to create a GRPC
        based server to register tf.function methods and a GRPC client to invoke
        remote registered methods. RPC APIs are intended for multi-client setups
        i.e. server and clients are started in separate binaries independently.