ASSERT_EQ(errors::OK, s.code()) << s.message();
      ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr ctx_;

 public:

# These flags share the user's gcloud credentials with the container, so that bazel
# inside the container can authenticate. Note: TF's CI does not have any credential
# stored here.

            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
      - name: Clean repository
        shell: bash
        run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
      - name: Checkout repository
        uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1

  // 1) nullptr indicates tensor is compact and row-majored.
  // 2) fill in the strides array as the real case for compact row-major data.
  // Here we choose option 2, since some frameworks didn't handle the strides
  // argument properly.
  dlm_tensor->dl_tensor.strides = stride_arr->data();

  dlm_tensor->dl_tensor.byte_offset =
      0;  // TF doesn't handle the strides and byte_offsets here

# the Python version set in "TFCI_PYTHON_VERSION" and use it as default.
# We enable this in the nightly and release builds because before uploading the
# wheels, we install them in a virtual environment and run some smoke tests on
# it. TFCI Mac VMs only have one Python version installed so we need to install
# the other versions manually.
if [[ "${TFCI_MACOS_PYENV_INSTALL_ENABLE}" == 1 ]]; then

  int value = -1;
  TF_Status status;
  cache1.LookupOrCompute("a", &value, compute_func, &status);
  TF_EXPECT_OK(status.status);
  EXPECT_EQ(value, 0);
  EXPECT_EQ(num_compute_calls, 1);
  // re-read the same value, expect another lookup
  cache1.LookupOrCompute("a", &value, compute_func, &status);
  TF_EXPECT_OK(status.status);
  EXPECT_EQ(value, 1);
  EXPECT_EQ(num_compute_calls, 2);

  // respective reads finish (which happens once they are all concurrently being
  // executed, or 10 seconds have passed).
}

TEST(RamFileBlockCacheTest, CoalesceConcurrentReads) {
  // Concurrent reads to the same file blocks should be de-duplicated.
  const size_t block_size = 16;
  int num_requests = 0;
  Notification notification;
  auto fetcher = [&num_requests, &notification, block_size](

   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

## Some of TensorFlow's code is derived from Caffe, which is subject to the following copyright notice:

COPYRIGHT

All contributions by the University of California:

      with open(api_packages_path) as file:
        return set(file.read().splitlines())

    super().setUp()
    self.api_packages_v2 = _get_api_packages_v2()
    # Some paths in the api_packages_path file cannot be directly imported.
    # These paths may point to attributes or sub-modules within a module's
    # namespace, but they don't correspond to an actual file

  TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle(ctx);
  std::vector<TFE_TensorHandle*> handles_task0;
  if (heavy_load_on_streaming_rpc) {
    // Send 50 tensor copy requests to simulate that there have been some RPC
    // requests been enqueued.
    for (int i = 0; i < 50; ++i) {
      handles_task0.push_back(TestMatrixTensorHandle(ctx));
    }
  }
  const char task1_name[] = "/job:localhost/replica:0/task:1/device:CPU:0";