* other {@code Future}, be {@linkplain #cancel cancelled}.
 *
 * <p>{@code SettableFuture} is the recommended {@code ListenableFuture} implementation when your
 * task cannot be implemented with {@link ListeningExecutorService}, the various {@link Futures}
 * utility methods, or {@link ListenableFutureTask}. Those APIs have less opportunity for developer

 * other {@code Future}, be {@linkplain #cancel cancelled}.
 *
 * <p>{@code SettableFuture} is the recommended {@code ListenableFuture} implementation when your
 * task cannot be implemented with {@link ListeningExecutorService}, the various {@link Futures}
 * utility methods, or {@link ListenableFutureTask}. Those APIs have less opportunity for developer

  private val key: String

  /** Non-null for client web sockets. These can be canceled. */
  internal var call: Call? = null

  /** This task processes the outgoing queues. Call [runWriter] to after enqueueing. */
  private var writerTask: Task? = null

  /** Null until this web socket is connected. Only accessed by the reader thread. */
  private var reader: WebSocketReader? = null

         * It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
         * All you need is a good internet connection and you are all set.
      * Try to build TF from sources by changing CPU optimization flags.
   
   *Please let us know if this helps.*
   
windows_comment: >

    text(
        "profiler",
        defaultProfiler,
        display = ParameterDisplay.PROMPT,
        allowEmpty = false,
        description = "Command line option for the performance test task to enable profiling. For example `async-profiler`, `async-profiler-heap`, `async-profiler-all` or `jfr`. Use `none` for benchmarking only."
    )
}

object AdHocPerformanceScenarioLinux : AdHocPerformanceScenario(Os.LINUX)

import javax.annotation.CheckForNull;

/**
 * {@link Error} variant of {@link java.util.concurrent.ExecutionException}. As with {@code
 * ExecutionException}, the error's {@linkplain #getCause() cause} comes from a failed task,
 * possibly run in another thread. That cause should itself be an {@code Error}; if not, use {@code
 * ExecutionException} or {@link UncheckedExecutionException}. This allows the client code to

HSPLokhttp3/internal/concurrent/TaskQueue;->schedule$default(Lokhttp3/internal/concurrent/TaskQueue;Lokhttp3/internal/concurrent/Task;JI)V
HSPLokhttp3/internal/concurrent/TaskQueue;->schedule(Lokhttp3/internal/concurrent/Task;J)V
HSPLokhttp3/internal/concurrent/TaskQueue;->scheduleAndDecide$okhttp(Lokhttp3/internal/concurrent/Task;JZ)Z
HSPLokhttp3/internal/concurrent/TaskQueue;->shutdown()V

        [`tf.keras.layers.MultiHeadAttention`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MultiHeadAttention).
        *   Implicit masks for `query`, `key` and `value` inputs will
            automatically be used to compute a correct attention mask for the
            layer. These padding masks will be combined with any
            `attention_mask` passed in directly when calling the layer. This can
            be used with

import javax.annotation.CheckForNull;

/**
 * {@link Error} variant of {@link java.util.concurrent.ExecutionException}. As with {@code
 * ExecutionException}, the error's {@linkplain #getCause() cause} comes from a failed task,
 * possibly run in another thread. That cause should itself be an {@code Error}; if not, use {@code
 * ExecutionException} or {@link UncheckedExecutionException}. This allows the client code to

    //                                                                           =========
    protected static final String[] DEFAULT_CLIENT_INVOKE_NAMES = { "Page", "Action", "Controller", "ControllerImpl", "Task", "Test" };

    protected static final String[] DEFAULT_BYPASS_INVOKE_NAMES =
            { "Service", "ServiceImpl", "Facade", "FacadeImpl", "Logic", "LogicImpl" };

    @Override
    public DBDef assistCurrentDBDef() {