name = gen_op_lib_exec,
        srcs = [src],
        srcs_version = "PY3",
        python_version = "PY3",
        deps = py_deps,
    )

    registered_op = "registered_" + name
    native.genrule(
        name = registered_op,
        srcs = [],
        outs = [name + ".inc.cc"],
        cmd = "$(location %s) --output=$@ --gen_register_op=true" % gen_op_lib_exec,
        tools = [":" + gen_op_lib_exec],

load("//tensorflow:strict.default.bzl", "py_strict_binary")
load("//tensorflow:tensorflow.bzl", "tf_cc_test")
load("//tensorflow:tensorflow.default.bzl", "filegroup", "genrule")
load("//tensorflow/compiler/aot:tfcompile.bzl", "tf_library")
load("//tensorflow/compiler/mlir:glob_lit_test.bzl", "glob_lit_tests")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    default_visibility = ["//visibility:private"],

    out_fuzz_files = [op_name + "_fuzz.cc" for op_name in op_names]
    native.genrule(
        name = name + "_genrule",
        outs = out_fuzz_files,
        srcs = api_def_srcs,
        tools = [":op_fuzz_gen_tool"],
        cmd = ("$(location :op_fuzz_gen_tool) " +

        # because builds may run concurrently with tests, and tests need to be
        # able to assume that they have control of the full GPU.
        out_nodes_file = "out_nodes_" + freeze_name
        native.genrule(
            name = ("gen_" + out_nodes_file),
            srcs = [config],
            outs = [out_nodes_file],
            cmd = ("CUDA_VISIBLE_DEVICES='' " +
                   "$(location " + tfcompile_tool + ")" +

)

# Filter the DEF file to reduce the number of symbols to 64K or less.
# Note that we also write the name of the pyd file into DEF file so that
# the dynamic libraries of custom ops can find it at runtime.
genrule(
    name = "tensorflow_filtered_def_file",
    srcs = [":tensorflow_def_file"],
    outs = ["tensorflow_filtered_def_file.def"],
    cmd = select({
        "//tensorflow:windows": """

# are present on the local host machine but not on the remote execution machine,
# leading to build failures. To resolve the issue, the following line is added
# to make sure all Genrule targets are excuted locally.
build:rbe_cross_compile_base_xla --config=rbe_cross_compile_base
build:rbe_cross_compile_base_xla --strategy=Genrule=standalone

# Build TensorFlow v2
build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1

# Prevent double-compilation of some TF code, ref. b/183279666 (internal)
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.

build --linkopt="-fuse-ld=lld"
build --linkopt="-lm"
build --linkopt="-Wl,--undefined-version"

# Prevent double-compilation of some TF code, ref. b/183279666 (internal)
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code
# > on the host machine. If we don't have --distinct_host_configuration=false,
# > the core TensorFlow code will be built once for the host and once for the
# > target platform.

    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));

)

py_strict_binary(
    name = "gen_quantized_function_library",
    srcs = ["gen_quantized_function_library.py"],
    deps = [
        "@absl_py//absl:app",
        "@absl_py//absl/flags",
    ],
)

genrule(
    name = "quantized_function_library",
    srcs = [
        "passes/quantized_function_library_uniform_quantized.mlir",
        "passes/quantized_function_library.mlir",