}
    }.start();

    getStartedSignal.await();

    // computation is in progress; asMap shouldn't have changed
    assertThat(map).hasSize(1);
    assertThat(map.containsKey(getKey)).isFalse();
    assertThat(map.get(refreshKey)).isSameInstanceAs(refreshKey);

    // let computation complete
    letGetFinishSignal.countDown();
    getFinishedSignal.await();
    checkNothingLogged();

      }
    }.start();

    getStartedSignal.await();

    // computation is in progress; asMap shouldn't have changed
    assertThat(map).hasSize(1);
    assertThat(map.containsKey(getKey)).isFalse();
    assertThat(map.get(refreshKey)).isSameInstanceAs(refreshKey);

    // let computation complete
    letGetFinishSignal.countDown();
    getFinishedSignal.await();
    checkNothingLogged();

   * computation is {@linkplain java.util.concurrent.Future#isDone() complete} or, if the
   * computation is already complete, immediately.
   *
   * <p>The callback is run on {@code executor}. There is no guaranteed ordering of execution of
   * callbacks, but any callback added through this method is guaranteed to be called once the
   * computation is complete.
   *

   * segments, each governed by its own write lock. The segment lock is taken once for each explicit
   * write, and twice for each cache loading computation (once prior to loading the new value, and
   * once after loading completes). Much internal cache management is performed at the segment
   * granularity. For example, access queues and write queues are kept per segment when they are

   * segments, each governed by its own write lock. The segment lock is taken once for each explicit
   * write, and twice for each cache loading computation (once prior to loading the new value, and
   * once after loading completes). Much internal cache management is performed at the segment
   * granularity. For example, access queues and write queues are kept per segment when they are

// - export functions to set Config fields

// --------------------------------------------------------------------------
// The new graph construction API, still under development.

// Represents a computation graph.  Graphs may be shared between sessions.
// Graphs are thread-safe when used as directed below.
typedef struct TF_Graph TF_Graph;

// Return a new graph object.
TF_CAPI_EXPORT extern TF_Graph* TF_NewGraph(void);

  TF_Operation* plusB = Add(plus2, b, graph, s, "plusB");
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Setup a session and a partial run handle.  The partial run will allow
  // computation of A + 2 + B in two phases (calls to TF_SessionPRun):
  // 1. Feed A and get (A+2)
  // 2. Feed B and get (A+2)+B
  TF_SessionOptions* opts = TF_NewSessionOptions();
  TF_Session* sess = TF_NewSession(graph, opts, s);

// license that can be found in the LICENSE file.

#include "../../../../../runtime/textflag.h"

TEXT asmtest(SB),DUPOK|NOSPLIT,$0
start:
	//
	// Unprivileged ISA
	//

	// 2.4: Integer Computational Instructions

	ADDI	$2047, X5				// 9382f27f
	ADDI	$-2048, X5				// 93820280
	ADDI	$2048, X5				// 9382024093820240
	ADDI	$-2049, X5				// 938202c09382f2bf
	ADDI	$4094, X5				// 9382f27f9382f27f

# Set lld as the linker.
common:cuda_clang --host_linkopt="-fuse-ld=lld"
common:cuda_clang --host_linkopt="-lm"
common:cuda_clang --linkopt="-fuse-ld=lld"
common:cuda_clang --linkopt="-lm"

# Set up compilation CUDA version and paths and use the CUDA Clang toolchain.
common:cuda_clang_official --config=cuda_clang
common:cuda_clang_official --config=cuda_version

# Build with nvcc for CUDA and clang for host

# Description:
# TensorFlow is a computational framework, primarily for use in machine
# learning applications.

load("@rules_cc//cc:cc_import.bzl", "cc_import")

# buildifier: disable=out-of-order-load
# copybara:comment_begin(oss-only)
load("//tensorflow:strict.default.bzl", "py_strict_library")

# copybara:comment_end
# buildifier: enable=out-of-order-load
load("@bazel_skylib//lib:selects.bzl", "selects")