echo 'TF CI scripts require GNU core utilties to be installed. Please make'
  echo 'sure they are present on your system and try again.'
  exit 1
fi

# "TFCI_MACOS_BAZEL_TEST_DIR_PATH" specifies the directory that Bazel should use
# when running tests. Each test will be executed in a separate subdirectory
# inside this directory. TF Mac builds need ~150 GB of disk space to be able to

      if (operation_name == std::string("_EagerConst")) {
        // Non-parallel tensors from _EagerConst/tf.constant are implicitly
        // broadcast, i.e. set as the input to each parallel operation. This
        // allows code like "tf.constant(1.)" or "tf.reduce_sum(..., axis=1)"
        // (where the value starts on the host), without allowing other implicit

  fun logHandler() =
    ConsoleHandler().apply {
      level = Level.FINE
      formatter =
        object : SimpleFormatter() {
          override fun format(record: LogRecord) = String.format("[%1\$tF %1\$tT] %2\$s %n", record.millis, record.message)
        }
    }

  fun enable(
    loggerClass: String,
    handler: Handler = logHandler(),
  ): Closeable {
    val logger = Logger.getLogger(loggerClass)

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
#
# Sets up custom apt sources for our TF images.

# Prevent apt install tzinfo from asking our location (assumes UTC)
export DEBIAN_FRONTEND=noninteractive

# Set up shared custom sources
apt-get update
apt-get install -y gnupg ca-certificates

set -euxo pipefail

# Run this from inside the tensorflow github directory.
# Usage: setup_venv_test.sh venv_and_symlink_name "glob pattern for one wheel file"
# Example: setup_venv_test.sh bazel_pip "/tf/pkg/*.whl"
# 
# This will create a venv with that wheel file installed in it, and a symlink
# in ./venv_and_symlink_name/tensorflow to ./tensorflow. We use this for the
# "pip" tests.

python -m venv /$1
mkdir -p $1

 * @internal
 */
public interface SmbTransportPool {

    /**
     * @param tf
     * @param name
     * @param port
     * @param exclusive
     * @param forceSigning
     * @return a connected transport
     * @throws UnknownHostException
     * @throws IOException
     */
    SmbTransport getSmbTransport ( CIFSContext tf, String name, int port, boolean exclusive, boolean forceSigning )

	}
}

func init() {
	cli.MakeKubeFactory = func(k kube.CLIClient) cmdutil.Factory {
		tf := cmdtesting.NewTestFactory()
		_, _, codec := cmdtesting.NewExternalScheme()
		tf.UnstructuredClient = &fake.RESTClient{
			NegotiatedSerializer: resource.UnstructuredPlusDefaultContentConfig().NegotiatedSerializer,
			Resp: &http.Response{

  # replace tensorflow to tf_nightly in the wheel name
  export TFCI_BUILD_PIP_PACKAGE_ARGS="$(echo $TFCI_BUILD_PIP_PACKAGE_ARGS | sed 's/tensorflow/tf_nightly/')"
fi

# TODO(b/361369076) Remove the following block after TF NumPy 1 is dropped
# Move hermetic requirement lock files for NumPy 1 to the root
if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  cp ./ci/official/requirements_updater/numpy1_requirements/*.txt .
fi

from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user
running the `tf.train.Server`.

## Untrusted inputs during training and prediction

TensorFlow supports a wide range of input data formats. For example it can

# of as a "repository". They can't be valid on Linux because Linux can't do
# anything with a Windows-only toolchain, and bazel errors if trying to build
# that directory.
@test "bazel nobuild passes on all of TF except TF Lite and win toolchains" {
    bazel build --experimental_cc_shared_library --nobuild --keep_going -- //tensorflow/... -//tensorflow/lite/... -//tensorflow/tools/toolchains/win/... -//tensorflow/tools/toolchains/win_1803/...
}