)

python_repository(name = "python_version_repo")

load("@python_version_repo//:py_version.bzl", "TF_PYTHON_VERSION")

python_register_toolchains(
    name = "python",
    ignore_root_user_error = True,
    python_version = TF_PYTHON_VERSION,
)

load("@python//:defs.bzl", "interpreter")
load("@rules_python//python:pip.bzl", "package_annotation", "pip_parse")

NUMPY_ANNOTATIONS = {
    "numpy": package_annotation(

        "dynamic_loaded_kernels": "true",
        "framework_shared_object": "true",
    },
    visibility = ["//visibility:public"],
)

config_setting(
    name = "using_rocm_hipcc",
    define_values = {"using_rocm_hipcc": "true"},
)

config_setting(
    name = "override_eigen_strong_inline",
    define_values = {"override_eigen_strong_inline": "true"},
    visibility = ["//visibility:public"],
)

python_register_multi_toolchains(
    name = "python",
    default_version = default_python_version,
    ignore_root_user_error = True,
    python_versions = [
        "3.9",
        "3.10",
        "3.11",
        "3.12",
    ],
    register_coverage_tool = True,
)

        boundaries.

*   XLA compilation:

    *   `tf.function(experimental_compile=True)` has become a stable API,
        renamed `tf.function(jit_compile=True)`.
    *   XLA can now compile MirroredStrategy: the step function passed
        to`strategy.run` can now be annoted with `jit_compile=True`.

*   `tf.distribute`:

    *   Rename `experimental_prefetch_to_device` in `tf.distribute.InputOptions`

build:mkl --define=build_with_mkl=true --define=enable_mkl=true
build:mkl --define=tensorflow_mkldnn_contraction_kernel=0
build:mkl --define=build_with_openmp=true
build:mkl -c opt

# config to build OneDNN backend with a user specified threadpool.
build:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
build:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0

        "@local_xla//xla/tsl/distributed_runtime/coordination:coordination_service_agent",
    ],
    alwayslink = 1,
)

cc_library(
    name = "c_api_experimental_reader",
    testonly = True,
    srcs = [
        "c_api_experimental_reader.cc",
    ],
    hdrs = [
        "c_api_experimental_reader.h",
        "tfe_monitoring_reader_internal.h",
    ],

        "math_grad.h",
        "nn_grad.h",
        "not_differentiable.h",
    ],
    visibility = ["//tensorflow/python:__pkg__"],
)

cc_library(
    name = "grad_test_helper",
    testonly = True,
    srcs = ["grad_test_helper.cc"],
    hdrs = ["grad_test_helper.h"],
    visibility = ["//visibility:private"],
    deps = [
        "//tensorflow/c/eager:gradient_checker",

*   Keep API compatibility in mind when you change code in core TensorFlow,
    e.g., code in
    [tensorflow/core](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core)
    and
    [tensorflow/python](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python).
    TensorFlow has passed version 1.0 and hence cannot make
    non-backward-compatible API changes without a major release. Reviewers of

  # Select lines unique to expected_licenses, i.e. missing licenses
  comm -2 -3 $BATS_TEST_TMPDIR/expected_licenses $BATS_TEST_TMPDIR/actual_licenses | grep -v -f $BATS_TEST_TMPDIR/allowed_to_be_missing > $BATS_TEST_TMPDIR/actual_missing_licenses || true

  if [[ -s $BATS_TEST_TMPDIR/actual_extra_licenses ]]; then

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

python_register_toolchains(
    name = "python",
    ignore_root_user_error = True,
    python_version = TF_PYTHON_VERSION,
)

load("@python//:defs.bzl", "interpreter")
load("@rules_python//python:pip.bzl", "pip_parse")

pip_parse(
    name = "pypi",