TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cuda
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ARGS="--gpus all"
TFCI_LIB_SUFFIX="-gpu-linux-x86_64"

    return errors::InvalidArgument("Value specified for `", attr_name, "` has ",
                                   num_dims,
                                   " dimensions which is over the limit of ",
                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {
    for (int d = 0; d < num_dims; ++d) {

    TFE_DeleteContextOptions(opts);
    TFE_Executor* executor = TFE_NewExecutor(
        /*is_async=*/false, /*enable_streaming_enqueue=*/true,
        /*in_flight_nodes_limit=*/0);
    TFE_ContextSetExecutorForThread(ctx, executor);

    TFE_DeleteContext(ctx);
    TFE_DeleteExecutor(executor);
  }

  {
    TFE_ContextOptions* opts = TFE_NewContextOptions();

TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-arm64"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_AUDIT_ENABLE=1
TFCI_WHL_AUDIT_PLAT=manylinux2014_aarch64
TFCI_WHL_BAZEL_TEST_ENABLE=1
TFCI_WHL_SIZE_LIMIT=240M

                                                        TF_Buffer* buf,
                                                        TF_Status* status);

// Some TF ops need a step container to be set to limit the lifetime of some
// resources (mostly TensorArray and Stack, used in while loop gradients in
// graph mode). Calling this on a context tells it to start a step.
TF_CAPI_EXPORT extern void TFE_ContextStartStep(TFE_Context* ctx);

TF_DEVICELIST_METHOD(const char*, TF_DeviceListType, device_type().c_str(),
                     nullptr);
TF_DEVICELIST_METHOD(int64_t, TF_DeviceListMemoryBytes, memory_limit(), -1);
TF_DEVICELIST_METHOD(uint64_t, TF_DeviceListIncarnation, incarnation(), 0);

#undef TF_DEVICELIST_METHOD

}  // end extern "C"

TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-x86_64"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_AUDIT_ENABLE=1
TFCI_WHL_AUDIT_PLAT=manylinux2014_x86_64
TFCI_WHL_BAZEL_TEST_ENABLE=1
TFCI_WHL_SIZE_LIMIT=240M

TFCI_MACOS_INSTALL_BAZELISK_URL="https://github.com/bazelbuild/bazelisk/releases/download/v1.11.0/bazelisk-darwin-amd64"
TFCI_MACOS_TWINE_INSTALL_ENABLE=1
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_BAZEL_TEST_ENABLE=1
TFCI_WHL_SIZE_LIMIT=255M
TFCI_WHL_SIZE_LIMIT_ENABLE=1

# 3.11 is the system python on our images
case $TFCI_PYTHON_VERSION in
3.11)
  TFCI_MACOS_PYENV_INSTALL_ENABLE=0
  TFCI_MACOS_UPGRADE_PYENV_ENABLE=0
  ;;
*)

TFCI_PYCPP_SWAP_TO_BUILD_ENABLE=
TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS=
TFCI_PYTHON_VERSION=
TFCI_WHL_AUDIT_ENABLE=
TFCI_WHL_AUDIT_PLAT=
TFCI_WHL_BAZEL_TEST_ENABLE=
TFCI_WHL_IMPORT_TEST_ENABLE=1
TFCI_WHL_SIZE_LIMIT=

        // parallel device.
        device_(device),
        executor_(
            TFE_NewExecutor(is_async, /*enable_streaming_enqueue=*/true,
                            /*in_flight_nodes_limit=*/in_flight_nodes_limit)),
        op_(nullptr),
        thread_(tensorflow::Env::Default()->StartThread(
            tensorflow::ThreadOptions(), "parallel_device_execute",
            std::bind(&DeviceThread::Run, this))) {}