"--quiet", "--wait", "--nocache", \
        "--add", "Microsoft.VisualStudio.Workload.VCTools", \
        "--add", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", \
        "--add", "Microsoft.VisualStudio.Component.Windows10SDK.19041" -Wait; \
    Start-Process -FilePath C:/TEMP/vs_buildtools.exe -ArgumentList "--installPath", "C:/VS", \
        "--quiet", "--wait", "--nocache", "--includeOptional", \

    fi
}

teardown_file() {
    rm -rf /tf/venv
}

@test "Wheel is manylinux2014 (manylinux_2_17) compliant" {
    python3 -m auditwheel show "$TF_WHEEL" > audit.txt
    grep --quiet -zoP 'is consistent with the following platform tag:\n"manylinux_2_17_(aarch|x86_)64"\.' audit.txt
}

@test "Wheel conforms to upstream size limitations" {

# This env is only valid when RBE is available on the selected platform, since
# it enables a derived --config setting.  If RBE is not available (i.e. there
# is no --config setting), bazel would fail and quit. This script does a quick
# check This script checks for such errors early
if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then
  cat <<EOF
ERROR: RBE was enabled via the 'rbe' env in the 'TFCI' variable.

#   https://github.com/NVIDIA/nvidia-container-toolkit
#
#   Note: if you interrupt a bazel command on docker (ctrl-c), you
#   will need to run `docker exec tf pkill bazel` to quit bazel.
#
#   Note: new files created from the container are owned by "root".
#   You can run e.g. `docker exec tf chown -R $(id -u):$(id -g) build_output`
#   to transfer ownership to your user.
#

// (also known as adjoint function) to compute, given downstream gradients,
// upstream gradients.
//
// TODO(apassos) provide concrete template instantiations for TFE_TensorHandle
// specialization, which is blocked by quite a few things needing to loop back
// into python now.
template <typename Gradient, typename BackwardFunction, typename TapeTensor>
class VSpace {
 public:
  virtual ~VSpace() {}