// for the first time.
TF_Library* TF_LoadPluggableDeviceLibrary(const char* library_filename,
                                          TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
  status->status = tensorflow::errors::Unimplemented(
      "PluggableDevice plugin functionality is not supported on mobile");
  return nullptr;
#else
  TF_Library* lib_handle = new TF_Library;

    default_visibility = [
        ":internal_visibility_allowlist_package",
        "//tensorflow:__pkg__",
    ],
    licenses = ["notice"],
)

cc_library(
    name = "tf_to_stablehlo",
    srcs = [
        "tf_to_stablehlo.cc",
    ],
    hdrs = [
        "tf_to_stablehlo.h",
    ],
    compatible_with = get_compatible_with_portable(),
    deps = [

  flags.out_metadata_object = "out_helper.o";
  flags.out_header = "out.h";
  flags.entry_point = "entry";
  flags.debug_info_path_begin_marker = "";

  // Note that tfcompile.bzl's tf_library macro sets fast math flags as that is
  // generally the preferred case.
  std::vector<tensorflow::Flag> flag_list;
  AppendMainFlags(&flag_list, &flags);
  xla::AppendDebugOptionsFlags(&flag_list);

        assert newModuleRootManager

        def sdkLibrary = newModuleRootManager.orderEntry.find { it.@name == "scala-sdk-$version" }
        assert sdkLibrary
        assert sdkLibrary.@type == "library"
        assert sdkLibrary.@level == "project"
    }

    private void hasScalaFacet(String imlFileName, String libraryName) {

NUMPY_ANNOTATIONS = {
    "numpy": package_annotation(
        additive_build_content = """\
filegroup(
    name = "includes",
    srcs = glob(["site-packages/numpy/core/include/**/*.h"]),
)
cc_library(
    name = "numpy_headers",
    hdrs = [":includes"],
    strip_include_prefix="site-packages/numpy/core/include/",
)
""",
    ),
}

pip_parse(
    name = "pypi",

)

# TODO(jakeharmon8): Remove these in favor of tsl:grpc
# copybara:comment_begin(oss-only)
cc_library(
    name = "grpc",
    visibility = ["//visibility:public"],
    deps = select({
        "//conditions:default": ["@com_github_grpc_grpc//:grpc"],
    }),
)

cc_library(
    name = "grpc++",
    visibility = ["//visibility:public"],
    deps = select({

    // calls), they are ignored.
    TF_RETURN_IF_ERROR(
        graph->get()->mutable_flib_def()->AddLibrary(temp_flib_def));
  }

  if (flib_def != nullptr) {
    TF_RETURN_IF_ERROR(flib_def->AddLibrary(temp_flib_def));
  }
  return absl::OkStatus();
}

}  // namespace

Status ConvertMlirToGraph(mlir::ModuleOp module,

    // calls), they are ignored.
    TF_RETURN_IF_ERROR(
        graph->get()->mutable_flib_def()->AddLibrary(temp_flib_def));
  }

  if (flib_def != nullptr) {
    TF_RETURN_IF_ERROR(flib_def->AddLibrary(temp_flib_def));
  }
  return absl::OkStatus();
}

}  // namespace

Status ConvertMlirToGraph(mlir::ModuleOp module,

    TF_ASSIGN_OR_RETURN(std::unique_ptr<xla::HloSnapshot> module,
                        computation.Snapshot());
    // Serialize the HloSnapshot deterministically so that all the outputs of a
    // tf_library genrule are deterministic.
    const size_t size = module->ByteSizeLong();
    auto serialized = std::make_unique<char[]>(size);
    TF_RET_CHECK(
        SerializeToBufferDeterministic(*module, serialized.get(), size));

// --------------------------------------------------------------------------
// Load plugins containing custom ops and kernels

// TF_Library holds information about dynamically loaded TensorFlow plugins.
typedef struct TF_Library TF_Library;

// Load the library specified by library_filename and register the ops and
// kernels present in that library.
//