*/
  private inner class TaskFakerBlockingQueue<T>(
    val delegate: BlockingQueue<T>,
  ) : AbstractQueue<T>(), BlockingQueue<T> {
    override val size: Int = delegate.size

    private var editCount = 0

    override fun poll(): T = delegate.poll()

    override fun poll(
      timeout: Long,
      unit: TimeUnit,
    ): T? {

    lock.withLock {
      return busyQueues + readyQueues
    }
  }

  fun cancelAll() {
    lock.assertHeld()
    for (i in busyQueues.size - 1 downTo 0) {
      busyQueues[i].cancelAllAndDecide()
    }
    for (i in readyQueues.size - 1 downTo 0) {
      val queue = readyQueues[i]
      queue.cancelAllAndDecide()
      if (queue.futureTasks.isEmpty()) {
        readyQueues.removeAt(i)
      }

| `resident_memory_bytes`       | `gauge`   | Resident memory size in bytes                                                                                  | `server` |
| `virtual_memory_bytes`        | `gauge`   | Virtual memory size in bytes                                                                                   | `server` |

build:nvcc_clang --@local_config_cuda//:cuda_compiler=nvcc


# Debug config
build:dbg -c dbg
# Only include debug info for files under tensorflow/, excluding kernels, to
# reduce the size of the debug info in the binary. This is because if the debug
# sections in the ELF binary are too large, errors can occur. See
# https://github.com/tensorflow/tensorflow/issues/48919.

*   `tensorflow.experimental.tensorrt`:

    *   `conversion_params` is now deprecated inside `TrtGraphConverterV2` in
        favor of direct arguments: `max_workspace_size_bytes`, `precision_mode`,
        `minimum_segment_size`, `maximum_cached_engines`, `use_calibration` and
        `allow_build_at_runtime`.
    *   Added a new parameter called `save_gpu_specific_engines` to the

LINUX_AGENT_BINARIES:=./cni/cmd/istio-cni \
  ./cni/cmd/install-cni \
  $(AGENT_BINARIES)

BINARIES:=$(STANDARD_BINARIES) $(AGENT_BINARIES) $(LINUX_AGENT_BINARIES)

# List of binaries that have their size tested
RELEASE_SIZE_TEST_BINARIES:=pilot-discovery pilot-agent istioctl envoy ztunnel client server

# agent: enables agent-specific files. Usually this is used to trim dependencies where they would be hard to trim through standard refactoring

- In the "File already exists" dialogue, choose "Yes" to overwrite
- In the "Open Project" dialogue, choose "Delete Existing Project and Import"
- Revert the Git changes to files in the `.idea` folder

NOTE: Due to the project size, the very first import can take a while and IntelliJ might become unresponsive for several seconds during this period.

### Server Memory

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.