if ld_version_int is None:
        ld_version_int = convert_version_to_int(ld_version[4])

      # Enable if 'ld' version >= 2.35
      if ld_version_int >= 2035000:
        write_to_bazelrc(
            'build --copt="-DEIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH=1"')

  with_xla_support = environ_cp.get('TF_ENABLE_XLA', None)
  if with_xla_support is not None:
    write_to_bazelrc('build --define=with_xla_support=%s' %

// TODO(b/154564140): Move this to its own header. This requires splitting
// c_api_experimental.h
struct TFE_ContextOptions {
  TF_SessionOptions session_options;
  // true if async execution is enabled.
  bool async = false;
  TFE_ContextDevicePlacementPolicy device_placement_policy{
      TFE_DEVICE_PLACEMENT_SILENT};
  // If true, use TFRT backend
  bool use_tfrt = false;

                                                   unsigned char enable);

// Set XLA's internal BuildXlaOpsPassFlags.tf_xla_enable_lazy_compilation to the
// value of 'enabled'. Also returns the original value of that flag.
//
// Use in tests to allow XLA to fallback to TF classic. This has global effect.
TF_CAPI_EXPORT unsigned char TF_SetXlaEnableLazyCompilation(
    unsigned char enable);

#      Ex. public_cache_push -- Use TF's public cache (read and write, Googlers only)
#      Ex. rbe        -- Use RBE for faster builds (Googlers only; see below)
#      Ex. no_docker  -- Disable docker on enabled platforms
#    See full examples below for more details on these. Some other modifiers are:
#      Ex. versions_upload -- for TF official release versions

void TFE_ContextSetSoftDevicePlacement(TFE_Context* ctx, unsigned char enable,
                                       TF_Status* status) {
  tensorflow::unwrap(ctx)->SetAllowSoftPlacement(enable);
}

void TFE_ContextSetLogDevicePlacement(TFE_Context* ctx, unsigned char enable,
                                      TF_Status* status) {
  tensorflow::unwrap(ctx)->SetLogDevicePlacement(enable);
}

# 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.
       TFCI: $TFCI

)

# This flag enables experimental MLIR support.
config_setting(
    name = "with_mlir_support",
    define_values = {"with_mlir_support": "true"},
    visibility = ["//visibility:public"],
)

# This flag forcibly enables experimental MLIR bridge support.
config_setting(
    name = "enable_mlir_bridge",
    define_values = {"enable_mlir_bridge": "true"},

from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
_os.environ["TF2_BEHAVIOR"] = "1"
from tensorflow.python import tf2 as _tf2
_tf2.enable()

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

# Make sure directory containing top level submodules is in

      pruning_thread_.reset(
          TF_StartThread(&thread_options, "TF_prune_FBC", PruneThread, this));
    }
    TF_VLog(1, "GCS file block cache is %s.\n",
            (IsCacheEnabled() ? "enabled" : "disabled"));
  }

  ~RamFileBlockCache() {
    if (pruning_thread_) {
      stop_pruning_thread_.Notify();
      // Destroying pruning_thread_ will block until Prune() receives the above

  auto gcs_file = static_cast<GCSFile*>(filesystem->plugin_filesystem);
  bool is_cache_enabled;
  {
    absl::MutexLock l(&gcs_file->block_cache_lock);
    is_cache_enabled = gcs_file->file_block_cache->IsCacheEnabled();
  }
  auto read_fn = [gcs_file, is_cache_enabled, bucket, object](
                     const std::string& path, uint64_t offset, size_t n,