fi
}

# Note: this is excluded on the full code base, since any submitted code must
# have passed Google's internal style guidelines.
@test "Check buildifier formatting on BUILD files" {
    echo "buildifier formatting is recommended. Here are the suggested fixes:"
    echo "============================="
    grep -e 'BUILD' $BATS_FILE_TMPDIR/changed_files \
        | xargs buildifier -v -mode=diff -diff_command="git diff --no-index"
}

TensorFlow process effectively executes arbitrary code.

The risk of loading untrusted checkpoints depends on the code or graph that you
are working with. When loading untrusted checkpoints, the values of the traced
variables from your model are also going to be untrusted. That means that if
your code interacts with the filesystem, network, etc. and uses checkpointed

-   Nightly jobs (Run nightly on the `nightly` branch)
    -   Uses `wheel.sh`, `libtensorflow.sh`, `code_check_full.sh`
-   Continuous jobs (Run on every GitHub commit)
    -   Uses `pycpp.sh`
-   Presubmit jobs (Run on every GitHub PR)
    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions

            create status using an error code without accessing it.
        *   Use the free functions such as
            `tensorflow::errors::IsInvalidArgument` if needed.
        *   In the last resort, use
            e.g.`static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT)`
            or `static_cast<int>(code)` for comparisons.

    fi
}

# Note: this is excluded on the full code base, since any submitted code must
# have passed Google's internal style guidelines.
@test "Check buildifier formatting on BUILD files" {
    echo "buildifier formatting is recommended. Here are the suggested fixes:"
    echo "============================="
    grep -e 'BUILD' $BATS_FILE_TMPDIR/changed_files \
        | xargs buildifier -v -mode=diff -diff_command="git diff --no-index"
}

    visibility = ["//visibility:public"],
)

config_setting(
    name = "debug",
    values = {
        "compilation_mode": "dbg",
    },
    visibility = ["//visibility:public"],
)

config_setting(
    name = "optimized",
    values = {
        "compilation_mode": "opt",
    },
    visibility = ["//visibility:public"],
)

config_setting(
    name = "arm",

-   type: input id: tfversion attributes: label: Tensorflow Version description:
    placeholder: ex,. tf 2.8 validations: required: true

-   type: dropdown id: Code attributes: label: Custom Code description:
    options: - "Yes" - "No" validations: required: true

-   type: input id: OS attributes: label: OS Platform and Distribution

# Build TensorFlow v2
build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1

# 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.

.DS_Store
.ipynb_checkpoints
node_modules
/.bazelrc.user
/.tf_configure.bazelrc
/xla_configure.bazelrc
/bazel-*
/bazel_pip
/tools/python_bin_path.sh
/tensorflow/tools/git/gen
/pip_test
/_python_build
*.pyc
__pycache__
*.swp
.vscode/
cmake_build/
tensorflow/contrib/cmake/_build/
.idea/**
/build/
[Bb]uild/
/build_output/
/tensorflow/core/util/version_info.cc
/tensorflow/python/framework/fast_tensor_util.cpp

#     cuda_clang:             Use Clang when building CUDA code.
#     avx_linux:              Build with avx instruction set on linux.
#     avx_win:                Build with avx instruction set on windows
#
# Other build options:
#     short_logs:       Only log errors during build, skip warnings.
#     verbose_logs:     Show all compiler warnings during build.
#     monolithic:       Build all TF C++ code into a single shared object.