status->status = handle->Tensor(&tensor);
  if (!status->status.ok()) {
    return nullptr;
  }

  std::vector<int64_t> dev_dims = TensorShapeAsVector(*handle, &status->status);
  if (!status->status.ok()) {
    return nullptr;
  }
  return new TFE_TensorDebugInfo(dev_dims);
}

TF_CAPI_EXPORT extern void TFE_DeleteTensorDebugInfo(
    TFE_TensorDebugInfo* debug_info) {
  delete debug_info;
}

# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn8=8.9.6.50-1+cuda12.2
libcudnn8-dev=8.9.6.50-1+cuda12.2

# This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image.
# For now it ensures that we install at least version 12.3.107 of PTXAS,
# since 12.3.103 has a bug.

set -xe

source ~/.bashrc
VERSION=$1
REQUIREMENTS=$2

add-apt-repository ppa:deadsnakes/ppa
# Install Python packages for this container's version
cat >pythons.txt <<EOF
$VERSION
$VERSION-dev
$VERSION-venv
$VERSION-distutils
EOF
/setup.packages.sh pythons.txt

# Re-link pyconfig.h from aarch64-linux-gnu into the devtoolset directory
# for any Python version present
pushd /usr/include/aarch64-linux-gnu

from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

if "dev" in __version__:   # pylint: disable=undefined-variable
  _logging.warning("""

  TensorFlow's `tf-nightly` package will soon be updated to TensorFlow 2.0.

  Please upgrade your code to TensorFlow 2.0:

#include <vector>

#include "tensorflow/core/platform/types.h"

struct TFE_TensorDebugInfo {
  explicit TFE_TensorDebugInfo(const std::vector<int64_t>& dims)
      : dev_dims(dims) {}

  // Fully-padded, minor-to-major.
  std::vector<int64_t> dev_dims;
};

# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.0.0.dev
tb-nightly ~= 2.17.0.a

# Test dependencies
grpcio >= 1.24.3, < 2.0
portpicker == 1.6.0
scipy == 1.11.3
requests == 2.31.0
packaging==23.2
setuptools==68.2.2

# Writes the list of tests that would be run from BAZEL_TEST_COMMAND to OUTPUT.
# Hides all extra output and always exits with success for now.

OUTPUT=$1
shift

  fi
fi

# Keep the existing "tf" container if it's already present.
# The container is not cleaned up automatically! Remove it with:
# docker rm tf
if ! docker container inspect tf >/dev/null 2>&1 ; then
  # Pass all existing TFCI_ variables into the Docker container
  env_file=$(mktemp)
  env | grep ^TFCI_ > "$env_file"
  docker run $TFCI_DOCKER_ARGS --name tf -w "$TFCI_GIT_DIR" -itd --rm \

    how we can reproduce your findings. For memory corruption triggerable in
    TensorFlow models, please demonstrate an exploit against one of Alphabet's
    models in <https://tfhub.dev/>
  - An explanation of who can exploit this vulnerability, and what they gain
    when doing so. Write an attack scenario that demonstrates how your issue

# See https://github.com/pyenv/pyenv/wiki#suggested-build-environment
build-essential
curl
libbz2-dev
libffi-dev
liblzma-dev
libncurses5-dev
libreadline-dev
libsqlite3-dev
libssl-dev
libxml2-dev
libxmlsec1-dev
llvm
make
openssl
tk-dev
wget
xz-utils
zlib1g-dev
git

# Packages needed to build devtoolset
file
flex
g++
make
patch
rpm2cpio
unar
wget