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=300M

```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

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

        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) {

      }
    };

    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) {

    // .backing_device of shape is CPU since the tensor is backed by CPU
    backing_device_name =
        TFE_TensorHandleBackingDeviceName(retvals[0], status.get());
    ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
    ASSERT_TRUE(absl::StrContains(backing_device_name, "CPU:0"))
        << backing_device_name;

    TFE_DeleteOp(shape_op);

mkdir -p ~/.gradle/init.d && cp -v $WORKSPACE/.ci/init.gradle ~/.gradle/init.d
if [ "$(uname -m)" = "arm64" ] || [ "$(uname -m)" = "aarch64" ]; then
   MAX_WORKERS=16
elif [ -f /proc/cpuinfo ]; then
   MAX_WORKERS=`grep '^cpu\scores' /proc/cpuinfo  | uniq | sed 's/\s\+//g' |  cut -d':' -f 2`
else
   if [[ "$OSTYPE" == "darwin"* ]]; then
      MAX_WORKERS=`sysctl -n hw.physicalcpu | sed 's/\s\+//g'`
   else
      echo "Unsupported OS Type: $OSTYPE"

  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  ASSERT_EQ(2, TFE_TensorDebugInfoOnDeviceNumDims(debug_info));
  // Shape is the same for CPU tensors.
  EXPECT_EQ(3, TFE_TensorDebugInfoOnDeviceDim(debug_info, 0));
  EXPECT_EQ(2, TFE_TensorDebugInfoOnDeviceDim(debug_info, 1));

  TFE_DeleteTensorDebugInfo(debug_info);
  TFE_DeleteTensorHandle(h);

          CI_DOCKER_BUILD_EXTRA_PARAMS="--build-arg py_major_minor_version=${{ matrix.pyver }} --build-arg is_nightly=${is_nightly} --build-arg tf_project_name=${tf_project_name}" \