// for this instance of unlocker, we should let the
	// caller know that lock is not successfully released
	// yet.
	if len(locks)-tolerance == tolerance {
		// In case of split brain scenarios where
		// tolerance is exactly half of the len(*locks)
		// then we need to make sure we have unlocked
		// upto tolerance+1 - especially for RUnlock
		// to ensure that we don't end up with active

#
# This file is autogenerated by pip-compile with Python 3.13
# by the following command:
#
#    bazel run //ci/official/requirements_updater:requirements.update
#
absl-py==2.3.0 \
    --hash=sha256:9824a48b654a306168f63e0d97714665f8490b8d89ec7bf2efc24bf67cf579b3 \
    --hash=sha256:d96fda5c884f1b22178852f30ffa85766d50b99e00775ea626c23304f582fc4f
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \

  TF_DeleteTensor(t);
  EXPECT_TRUE(deallocator_called);
}

TEST(CAPI, LibraryLoadFunctions) {
  // TODO(b/73318067): Fix linking for the GPU test generated by the
  // tf_cuda_cc_test() bazel rule and remove the next line.
  if (!GPUDeviceName().empty()) return;

#if !defined(TENSORFLOW_NO_SHARED_OBJECTS)
  {
    // Load the library.
    TF_Status* status = TF_NewStatus();

#
# This file is autogenerated by pip-compile with Python 3.14
# by the following command:
#
#    bazel run //ci/official/requirements_updater:requirements.update
#
absl-py==2.3.0 \
    --hash=sha256:9824a48b654a306168f63e0d97714665f8490b8d89ec7bf2efc24bf67cf579b3 \
    --hash=sha256:d96fda5c884f1b22178852f30ffa85766d50b99e00775ea626c23304f582fc4f
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \

### Birden Fazla Process'li Container'lar ve Özel Durumlar { #containers-with-multiple-processes-and-special-cases }

Elbette bazı **özel durumlarda** bir container içinde birden fazla **Uvicorn worker process** çalıştırmak isteyebilirsiniz.

Bu durumlarda çalıştırmak istediğiniz worker sayısını `--workers` komut satırı seçeneğiyle ayarlayabilirsiniz:

* Hermetic CUDA support is added.

  Hermetic CUDA uses a specific downloadable version of CUDA instead of the user’s locally installed CUDA. Bazel will download CUDA, CUDNN and NCCL distributions, and then use CUDA libraries and tools as dependencies in various Bazel targets. This enables more reproducible builds for Google ML projects and supported CUDA versions.

    - Includes:
      - bazelbuild/bazel-toolchains@3.3.2
      - bazelbuild/rules_go@v0.22.7 ([#93088](https://github.com/kubernetes/kubernetes/pull/93088), [@justaugustus](https://github.com/justaugustus)) [SIG Release and Testing]
- Update Golang to v1.14.6
  - Update repo-infra to 0.0.8 (to support go1.14.6 and go1.13.14)
    - Includes:
      - bazelbuild/bazel-toolchains@3.4.0

    - Includes:
      - bazelbuild/bazel-toolchains@3.3.2
      - bazelbuild/rules_go@v0.22.7 ([#93088](https://github.com/kubernetes/kubernetes/pull/93088), [@justaugustus](https://github.com/justaugustus)) [SIG Release and Testing]
- Update Golang to v1.14.6
  - Update repo-infra to 0.0.8 (to support go1.14.6 and go1.13.14)
    - Includes:
      - bazelbuild/bazel-toolchains@3.4.0

		minDisks = er.setDriveCount - er.defaultParityCount
	}

	if minDisks == er.setDriveCount/2 {
		// when data and parity are same we must atleast
		// wait for response from 1 extra drive to avoid
		// split-brain.
		minDisks++
	}

	calcQuorum := func(metaArr []FileInfo, errs []error) (FileInfo, []FileInfo, []StorageAPI, time.Time, string, error) {

- Update Golang to v1.13.14
  - Update bazel to 2.2.0
  - Update repo-infra to 0.0.8 (to support go1.14.6 and go1.13.14)
    - Includes:
      - bazelbuild/bazel-toolchains@3.4.0