TF_EXPECT_OK(ReadCache(&cache2, "", 0, 1, &out));
  EXPECT_EQ(calls, 1);
  // Advance the clock by a huge amount and verify that the cached block is
  // used to satisfy the read.
  env->SetNowSeconds(365 * 24 * 60 * 60);  // ~1 year, just for fun.
  TF_EXPECT_OK(ReadCache(&cache2, "", 0, 1, &out));
  EXPECT_EQ(calls, 1);
}

TEST(RamFileBlockCacheTest, RemoveFile) {
  int calls = 0;

        << i;
  }
  TF_DeleteStatus(status);
}

TEST(TF_GraphProperties, InputProperties) {
  std::unique_ptr<SingleMachine> cluster(new SingleMachine(5 * 60, 3, 0));
  TF_ASSERT_OK(cluster->Provision());

  TrivialTestGraphInputYielder fake_input(4, 1, 10, false,
                                          cluster->GetDeviceNames());
  GrapplerItem item;

# major release. Example: sm_80 kernels can run on sm_89 GPUs but
# not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs.
build:cuda_clang --repo_env=TF_CUDA_COMPUTE_CAPABILITIES="sm_60,sm_70,sm_80,sm_89,compute_90"

# Set up compilation CUDA version and paths and use the CUDA Clang toolchain.
build:cuda_clang_official --config=cuda_clang
build:cuda_clang_official --action_env=TF_CUDA_VERSION="12"

    for compute_capability in tf_cuda_compute_capabilities.split(','):
      m = re.match('[0-9]+.[0-9]+', compute_capability)
      if not m:
        # We now support sm_35,sm_50,sm_60,compute_70.
        sm_compute_match = re.match('(sm|compute)_?([0-9]+[0-9]+)',
                                    compute_capability)
        if not sm_compute_match:

    is no longer experimental and allows the use of 16-bit floating point
    formats during training, improving performance by up to 3x on GPUs and 60%
    on TPUs. Please see below for additional details.

*   TensorFlow Profiler now supports profiling `MultiWorkerMirroredStrategy` and
    tracing multiple workers using the