TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=macos_arm64
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-darwin-arm64"
TFCI_MACOS_BAZEL_TEST_DIR_ENABLE=1
TFCI_MACOS_BAZEL_TEST_DIR_PATH="/Volumes/BuildData/bazel_output"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_BAZEL_TEST_ENABLE=1
TFCI_WHL_SIZE_LIMIT=245M

```bash
tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh
```

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

#### Running unit tests

There are two ways to run TensorFlow unit tests.

1.  Using tools and libraries installed directly on your system.

    Refer to the

streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.

TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=devel ci/official/containers/ml_build"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-x86_64"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_AUDIT_ENABLE=1
TFCI_WHL_AUDIT_PLAT=manylinux_2_27_x86_64
TFCI_WHL_BAZEL_TEST_ENABLE=1
TFCI_WHL_SIZE_LIMIT=260M

    public boolean hasPrimaryKey() {
        return false;
    }

    @Override
    public boolean hasCompoundPrimaryKey() {
        return false;
    }

    @Override
    protected UniqueInfo cpui() {
        return null;
    }

    // ===================================================================================
    //                                                                           Type Name

    public boolean hasPrimaryKey() {
        return false;
    }

    @Override
    public boolean hasCompoundPrimaryKey() {
        return false;
    }

    @Override
    protected UniqueInfo cpui() {
        return null;
    }

    // ===================================================================================
    //                                                                           Type Name

        this.delayMillisForWaitingNewUrl = delayMillisForWaitingNewUrl;
    }

    /**
     * Delays the crawler while waiting for new URLs to be available.
     * This method calibrates CPU load, checks crawler status, applies
     * interval control rules, and then calls the parent implementation.
     * All operations are wrapped in exception handling to ensure robustness
     * in test and production environments.

      }
    };

    public abstract Queue<Integer> create(Comparator<Integer> comparator);
  }

  /**
   * Does a CPU intensive operation on Integer and returns a BigInteger Used to implement an
   * ordering that spends a lot of cpu.
   */
  static class ExpensiveComputation implements Function<Integer, BigInteger> {
    @Override
    public BigInteger apply(Integer from) {

- Reduced server startup time. For IAM encryption with the root credentials, MinIO had
   to use a memory-hard function (Argon2) that (on purpose) consumes a lot of memory and CPU.
   The new KMS-based approach can use a key derivation function that is orders of magnitudes
   cheaper w.r.t. memory and CPU.
- Root credentials can now be changed easily. Before, a two-step process was required to

- Fixed a bug whereby the allocation of reusable CPUs and devices was not being honored when the TopologyManager was enabled ([#93189](https://github.com/kubernetes/kubernetes/pull/93189), [@klueska](https://github.com/klueska)) [SIG Node]