_current_module.app.flags = flags  # pylint: disable=undefined-variable
setattr(_current_module, "flags", flags)

_major_api_version = 1

# Add module aliases from Keras to TF.
# Some tf endpoints actually lives under Keras.
_current_module.layers = _KerasLazyLoader(
    globals(),
    submodule="__internal__.legacy.layers",
    name="layers",
    mode="v1")
if _tf_uses_legacy_keras:

  # Column 1 is left only, Column 2 is right only, Column 3 is shared lines
  # Select lines unique to actual_licenses, i.e. extra licenses.
  comm -1 -3 $BATS_TEST_TMPDIR/expected_licenses $BATS_TEST_TMPDIR/actual_licenses | grep -v -f $BATS_TEST_TMPDIR/allowed_to_be_extra > $BATS_TEST_TMPDIR/actual_extra_licenses || true
  # Select lines unique to expected_licenses, i.e. missing licenses

# Usage: setup.packages.sh <package_list.txt>
set -e

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

apt-get update
# Remove commented lines and blank lines
apt-get install -y --no-install-recommends $(sed -e '/^\s*#.*$/d' -e '/^\s*$/d' "$1" | sort -u)

RUN groupadd -g 1001 buildslave && useradd -m -u 1001 -g buildslave buildslave
RUN mkdir -p /tf/venv
RUN chown -R buildslave:buildslave /tf
RUN dpkg -i /patchelf/patchelf_0.14.3-1_arm64.deb

# All lines past this point are reset when $CACHEBUSTER is set. We need this
# for Python specifically because we install some nightly packages which are
# likely to change daily.
ARG CACHEBUSTER=0
RUN echo $CACHEBUSTER

# export TFCI=py311,linux_x86,no_docker

# Advanced: Use Remote Build Execution (RBE) (internal developers only)
#
#   RBE dramatically speeds up builds and testing. It also gives you a
#   public URL to share your build results with collaborators. However,
#   it is only available to a limited set of internal TensorFlow developers.
#
#   RBE is incompatible with local caching, so you must remove

    of the contribution must be compared against the cost of maintaining the
    feature.
*   Full new features (e.g., a new op implementing a cutting-edge algorithm)
    typically will live in
    [tensorflow/addons](https://github.com/tensorflow/addons) to get some
    airtime before a decision is made regarding whether they are to be migrated
    to the core.

        [Revisiting ResNets: Improved Training and Scaling Strategies](https://arxiv.org/pdf/2103.07579.pdf)
    *   Added `tf.keras.optimizers.experimental.Optimizer`. The reworked
        optimizer gives more control over different phases of optimizer calls,
        and is easier to customize. We provide Adam, SGD, Adadelta, AdaGrad and
        RMSprop optimizers based on

  # Column 1 is left only, Column 2 is right only, Column 3 is shared lines
  # Select lines unique to actual_licenses, i.e. extra licenses.
  comm -1 -3 $BATS_TEST_TMPDIR/expected_licenses $BATS_TEST_TMPDIR/actual_licenses | grep -v -f $BATS_TEST_TMPDIR/allowed_to_be_extra > $BATS_TEST_TMPDIR/actual_extra_licenses || true
  # Select lines unique to expected_licenses, i.e. missing licenses

of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow...

# 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 { \