EOF
  exit 1
fi
TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX"

# 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.

# for verifying changes to the TFCI scripts system, and most users won't need
# to interact with it at all.
source "${BASH_SOURCE%/*}/utilities/setup.sh"

echo "==TFCI== env outside of tfrun:"
env
echo "==TFCI== env inside of tfrun:"

  gcloud auth activate-service-account --key-file="${GOOGLE_APPLICATION_CREDENTIALS}"
fi
set -x

# When cross-compiling with RBE, we need to copy the macOS sysroot to be
# inside the TensorFlow root directory. We then define them as a filegroup
# target inside "tensorflow/tools/toolchains/cross_compile/cc" so that Bazel
# can register it as an input to compile/link actions and send it to the remote
# VMs when needed.

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
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"
# 

as a rough example of the steps below.

All the files referenced below are located in the same directory as this README,
unless indicated otherwise.

1) Add the new version to the `VERSIONS` variable inside
   `tensorflow/tools/toolchains/python/python_repo.bzl`. \
   While this isn't necessary for running the updater, it is required for
   actually using the new version with Tensorflow.

Since models are practically programs that TensorFlow executes, using untrusted
models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.

//   * increased security as plugins will not be able to alter function table
//     after loading up. Thus, malicious plugins can't alter functionality to
//     probe for gadgets inside core TensorFlow. We can even protect the area
//     of memory where the copies reside to not allow any more writes to it
//     after all copies are created.
template <typename T>
static std::unique_ptr<const T> CopyToCore(const T* plugin_ops,

from tensorflow.python.tools import module_util as _module_util
from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
_os.environ["TF2_BEHAVIOR"] = "1"
from tensorflow.python import tf2 as _tf2
_tf2.enable()

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

  parser = argparse.ArgumentParser(
      description='Extracts ResultStore links from a build log.\n'
                  'These can be then printed out, and/or output into a '
                  'JUnit-based XML file inside a specified directory.')

  parser.add_argument('build_log',
                      help='Path to a build log.')
  parser.add_argument('--xml-out-path',
                      required=False,

# Workaround for networking (b/112379377) was closed as won't fix for MTU setting.
# Remaining lines handle making the metadata server on the VM accessible inside docker.
RUN Get-NetAdapter | Where-Object Name -like "*Ethernet*" | ForEach-Object { \
        & netsh interface ipv4 set subinterface $_.InterfaceIndex mtu=1460 store=persistent }; \