}

@test "API compatibility test passes, ensuring no unexpected changes to the TF API" {
    bazel test $TFCI_BAZEL_COMMON_ARGS //tensorflow/tools/api/tests:api_compatibility_test
    echo "You have to re-generate the TF API goldens and have the API changes reviewed."
    echo "Look at the instructions for ':api_compatibility_test -- --update_goldens=True'"
}

teardown_file() {
    bazel shutdown

# 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.

## Wheel Test

This directory is dedicated to tests that require a built TensorFlow wheel
file for testing, such as:

* Ensuring the entire API is importable
* Testing downstream projects against the wheel

Ensure you have Bazel installed and accessible from your command line.

These tests use hermetic Python. They also require a built TensorFlow wheel file
and a requirements_lock file. The requirements_lock file is generated by the

# Prevent double-compilation of some TF code, ref. b/183279666 (internal)
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.

***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository.

### Contributing code

If you have improvements to TensorFlow, send us your pull requests! For those
just getting started, GitHub has a
[how-to](https://help.github.com/articles/using-pull-requests/).

#   Non-Googlers should replace "nightly_upload" or "rbe" with
#   "public_cache,disk_cache".
#   Googlers should replace "nightly_upload" with "public_cache,disk_cache" or
#   "rbe", if you have set up your system to use RBE (see further below).
#
# Here is how to choose your TFCI value:
# 1. A Python version must come first, because other scripts reference it.
#      Ex. py39  -- Python 3.9

in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

These modifications and conversions are handled by a variety of libraries that
have different security properties and provide different levels of confidence
when dealing with untrusted data. Based on the security history of these
libraries we consider that it is safe to work with untrusted inputs for PNG,

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64
# Note: this is not set to "--cpu", because that changes the package name
# to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow"
# despite lacking Nvidia CUDA support.
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=gcr.io/tensorflow-sigs/build-arm64:tf-2-16-multi-python

        `allow_build_at_runtime`.
    *   Added a new parameter called `save_gpu_specific_engines` to the
        `.save()` function inside `TrtGraphConverterV2`. When `False`, the
        `.save()` function won't save any TRT engines that have been built. When
        `True` (default), the original behavior is preserved.

COPY cuda.packages.txt /cuda.packages.txt
RUN /setup.sources.sh && /setup.packages.sh /devel.packages.txt

# Install various tools.
# - bats: bash unit testing framework
#         NOTE: v1.6.0 seems to have a bug that made "git" in setup_file break
# - bazelisk: always use the correct bazel version
# - buildifier: clean bazel build deps
# - buildozer: clean bazel build deps