build:avx_win --copt=/arch:AVX

# Use Clang-cl compiler on Windows
build:win_clang --copt=/clang:-Weverything
build:win_clang --extra_toolchains=@local_config_cc//:cc-toolchain-x64_windows-clang-cl
build:win_clang --extra_execution_platforms=//tensorflow/tools/toolchains/win:x64_windows-clang-cl
build:win_clang --host_platform=//tensorflow/tools/toolchains/win:x64_windows-clang-cl
build:win_clang --compiler=clang-cl

  return val


def set_clang_cuda_compiler_path(environ_cp):
  """Set CLANG_CUDA_COMPILER_PATH."""
  default_clang_path = '/usr/lib/llvm-17/bin/clang'
  if not os.path.exists(default_clang_path):
    default_clang_path = '/usr/lib/llvm-16/bin/clang'
    if not os.path.exists(default_clang_path):
      default_clang_path = which('clang') or ''

  clang_cuda_compiler_path = prompt_loop_or_load_from_env(

					if firstErr == nil {
						firstErr = err
					}
					// Cancel remaining...
					cancel()
					mu.Unlock()
					return
				}
			}(resultIndex, erObj)
		}
	}
	updateCloser := make(chan chan struct{})
	go func() {
		updateTicker := time.NewTicker(30 * time.Second)
		defer updateTicker.Stop()
		var lastUpdate time.Time

		// We need to merge since we will get the same buckets from each pool.

			"-ferror-limit=0",
			// Apple clang version 1.7 (tags/Apple/clang-77) (based on LLVM 2.9svn)
			// doesn't have -Wno-unneeded-internal-declaration, so we need yet another
			// flag to disable the warning. Yes, really good diagnostics, clang.
			"-Wno-unknown-warning-option",
			"-Wno-unneeded-internal-declaration",
			"-Wno-unused-function",
			"-Qunused-arguments",
			// Clang embeds prototypes for some builtin functions,

			t.Fatalf("Unable to change permission to temporary directory %v. %v", permDeniedDir, err)
		}

		xlStorageNew, err := newLocalXLStorage(permDeniedDir)
		if err != nil {
			t.Fatalf("Unable to initialize xlStorage, %s", err)
		}

		// change backend permissions for MakeVol error.
		if err = os.Chmod(permDeniedDir, 0o400); err != nil {

		ctx:          ctx,
		objLayer:     o,
		jobCh:        make(chan *BatchJobRequest, 10000),
		workerKillCh: make(chan struct{}, workers),
		jobCancelers: make(map[string]context.CancelFunc),
	}
	jpool.ResizeWorkers(workers)
	jpool.resume()
	return jpool
}

func (j *BatchJobPool) resume() {
	results := make(chan ObjectInfo, 100)
	ctx, cancel := context.WithCancel(j.ctx)
	defer cancel()

   * at a time, but since read operations and cache loading computations can proceed concurrently,
   * this still yields higher concurrency than full synchronization.
   *
   * <p>Defaults to 4. <b>Note:</b>The default may change in the future. If you care about this
   * value, you should always choose it explicitly.
   *
   * <p>The current implementation uses the concurrency level to create a fixed number of hashtable

	if isGroup {
		v, ok := cache.iamGroupPolicyMap.Load(name)
		return v, ok
	}
	return cache.iamUserPolicyMap.Load(name)
}

// GroupNotificationHandler - updates in-memory cache on notification of
// change (e.g. peer notification for object storage and etcd watch
// notification).
func (store *IAMStoreSys) GroupNotificationHandler(ctx context.Context, group string) error {
	cache := store.lock()
	defer store.unlock()

/// as well. Here is a short summary of what changes are allowed:
///   * adding a new method at the end of a table is allowed at any time;
///   * any other change to these tables is only allowed on a major TensorFlow
///     version change (e.g., from 2.x to 3.0). This is provided as an escape
///     hatch to allow cleaning up these tables. Since any of these changes

# support through e.g. `--config=cuda` (or `--config=cuda_clang` in OSS).
alias(
    name = "is_cuda_enabled",
    actual = if_oss(
        "@local_config_cuda//:is_cuda_enabled",
        "@local_config_cuda//cuda:using_clang",
    ),
)

# Config setting that is satisfied when CUDA device code should be compiled
# with clang. It does not imply that CUDA support has been enabled.
alias(