"//tensorflow/core/transforms:__subpackages__",
    ],
    deps = [
        ":c_api_macros_hdrs",
        "@local_xla//xla/tsl/c:tsl_status",
        "@local_xla//xla/tsl/c:tsl_status_internal",
        "@local_xla//xla/tsl/platform:status",
    ] + select({
        "//tensorflow:android": [
            "//tensorflow/core:portable_tensorflow_lib_lite",  # TODO(annarev): exclude runtime srcs

        [],
        otherwise = [
            "@local_xla//xla/stream_executor/cuda:all_runtime",
            "@local_xla//xla/stream_executor/cuda:cuda_platform",
            "@local_xla//xla/stream_executor/cuda:cudnn_plugin",
            "@local_xla//xla/stream_executor/cuda:cufft_plugin",
            "@local_xla//xla/stream_executor:cuda_platform",
        ],
    ),
    deps = if_cuda([

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

  cp tensorflow/core/platform/ctstring.h \
    tensorflow/core/platform/ctstring_internal.h \
    ${LIB_PKG}/include/tensorflow/core/platform
  cp third_party/xla/xla/tsl/c/tsl_status.h ${LIB_PKG}/include/xla/tsl/c
  cp third_party/xla/third_party/tsl/tsl/platform/ctstring.h \
     third_party/xla/third_party/tsl/tsl/platform/ctstring_internal.h \
     ${LIB_PKG}/include/tsl/platform
  cd ${LIB_PKG}

#ifdef __cplusplus
extern "C" {
#endif

// When `enable` is true, set
// tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also
// set XLA flag values to prepare for XLA compilation. Otherwise set
// global_jit_level to OFF.
//
// This and the next API are syntax sugar over TF_SetConfig(), and is used by
// clients that cannot read/write the tensorflow.ConfigProto proto.

# For wheel verification, and uploading
auditwheel ~= 6.1.0
twine ~= 6.1.0
id
urllib3
requests

# For XLA
pyyaml

# For JAX
build ~= 1.2.2
# uv is faster than pip for installing Python packages.
uv ~= 0.5.30

# For running wheel verification script

**Linux XLA**                 | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.html)         | TBA

#     --config=dbg --per_file_copt=+tensorflow/core/kernels/identity_op.*@-g
# Since this .bazelrc file is synced between the tensorflow/tensorflow repo and
# the openxla/xla repo, also include debug info for files under xla/.
common:dbg --per_file_copt=+.*,-tensorflow.*,-xla.*@-g0
common:dbg --per_file_copt=+tensorflow/core/kernels.*@-g0
# for now, disable arm_neon. see: https://github.com/tensorflow/tensorflow/issues/33360

  auto* optimizer_options =
      config.mutable_graph_options()->mutable_optimizer_options();
  if (enable) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

    // These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =

        "@com_google_absl//absl/memory",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/time",
        "@local_xla//xla/tsl/c:tsl_status_internal",
        "@local_xla//xla/tsl/distributed_runtime/coordination:coordination_service_agent",
    ],
    alwayslink = 1,
)

cc_library(
    name = "c_api_experimental_reader",
    testonly = True,