deps = [
        ":tf_status_headers",
        "@local_tsl//tsl/platform:status",
        "@local_xla//xla/tsl/c:tsl_status_helper",
    ],
)

tf_cc_test(
    name = "tf_status_helper_test",
    srcs = ["tf_status_helper_test.cc"],
    deps = [
        ":tf_status",
        ":tf_status_helper",
        "@local_tsl//tsl/platform:errors",
        "@local_tsl//tsl/platform:status",

    "@llvm_terminfo//:__subpackages__",
    "@llvm_zlib//:__subpackages__",
    "@local_config_cuda//:__subpackages__",
    "@local_config_git//:__subpackages__",
    "@local_config_nccl//:__subpackages__",
    "@local_config_rocm//:__subpackages__",
    "@local_config_tensorrt//:__subpackages__",
    "@local_execution_config_platform//:__subpackages__",
    "@mkl_dnn_acl_compatible//:__subpackages__",

# link to the Bazelisk binary which needs to be downloaded.
if [[ "${TFCI_MACOS_INSTALL_BAZELISK_ENABLE}" == 1 ]]; then
  sudo wget --no-verbose -O "/usr/local/bin/bazel" "${TFCI_MACOS_INSTALL_BAZELISK_URL}"
  chmod +x "/usr/local/bin/bazel"
fi

# "TFCI_MACOS_UPGRADE_PYENV_ENABLE" is used to decide if we need to upgrade the
# Pyenv version. We enable this for macOS x86 builds as the default Pyenv on

class ServerDef;
class WorkerEnv;
class WorkerCacheInterface;

class ImmediateExecutionDistributedManager {
 public:
  virtual ~ImmediateExecutionDistributedManager() {}

  // Set up distributed execution environment on local and remote tasks.
  // When `reset_context` is true, initialize new cluster context state based
  // on cluster configurations provided in `server_def`; otherwise, update

    --@local_config_cuda//:enable_cuda \
    "somepath(//tensorflow/tools/pip_package:wheel, " \
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cuda_driver + "\
    "@local_config_cuda//cuda:cudnn + "\
    "@local_config_cuda//cuda:curand + "\
    "@local_config_cuda//cuda:cusolver + "\

build:unsupported_gpu_linux --config=tensorrt
build:unsupported_gpu_linux --action_env=CUDA_TOOLKIT_PATH="/usr/local/cuda-11.2"
build:unsupported_gpu_linux --action_env=LD_LIBRARY_PATH="/usr/local/cuda:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda-11.1/lib64:/usr/local/tensorrt/lib"
build:unsupported_gpu_linux --action_env=GCC_HOST_COMPILER_PATH="/dt9/usr/bin/gcc"

# buildifier: disable=same-origin-load
load("@rules_python//python:repositories.bzl", "py_repositories")

py_repositories()

## Load HERMETIC_PYTHON_VERSION variable
local_repository(
    name = "local_tensorflow",
    path = "../../..",
)

load(
    "@local_tensorflow//tensorflow/tools/toolchains/python:python_repo.bzl",
    "python_repository",
)

python_repository(name = "python_version_repo")

        "//tensorflow/c:tf_status_internal",
        "//tensorflow/core/platform:env",
        "//tensorflow/core/platform:errors",
        "//tensorflow/core/platform:status",
        "@local_tsl//tsl/platform:errors",
    ],
)

# Compliance test for modules and for interface
tf_cc_test(
    name = "modular_filesystem_test",
    size = "small",
    srcs = ["modular_filesystem_test.cc"],

    // Grad for Softmax Input
    TF_RETURN_IF_ERROR(BroadcastMul(
        ctx, grad_outputs[0], forward_outputs_[1],
        grad_inputs.subspan(0, 1)));  // upstream_grad * local softmax grad

    // Grad for labels is null
    grad_inputs[1] = nullptr;
    return absl::OkStatus();
  }
  ~SparseSoftmaxCrossEntropyWithLogitsGradientFunction() override {}

 private:

TF_CAPI_EXPORT extern bool TFE_ContextCheckAlive(TFE_Context* ctx,
                                                 const char* worker_name,
                                                 TF_Status* status);

// Sync pending nodes in local executors (including the context default executor
// and thread executors) and streaming requests to remote executors, and get the
// combined status.
TF_CAPI_EXPORT extern void TFE_ContextAsyncWait(TFE_Context* ctx,