// to pin to the physical device.
  //
  // This function is guaranteed to be called only when all of the custom-device
  // inputs are on this device.
  bool (*shall_pin_to_this_device)(const TFE_Op* op, TF_Status* s) = nullptr;
} TFE_CustomDevice;

// Registers a custom device for use with eager execution.
//
// Eager operations may be placed on this device, e.g.  `with

  echo '(search for TFCI_WHL_SIZE_LIMIT to change it)'
  ls -sh *.whl
  exit 2
fi

# Quick install checks
venv=$(mktemp -d)
"python${TFCI_PYTHON_VERSION}" -m venv "$venv"
python="$venv/bin/python3"
"$python" -m pip install *.whl $TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS
if [[ "$TFCI_WHL_IMPORT_TEST_ENABLE" == "1" ]]; then

h5py >= 3.10.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch

        "//tensorflow/core/platform:status",
        "@com_google_absl//absl/container:flat_hash_set",
    ],
)

tf_cuda_cc_test(
    name = "gradients_test",
    size = "small",
    srcs = [
        "gradients_test.cc",
    ],
    args = ["--heap_check="],
    tags = tf_cuda_tests_tags() + ["nomac"],
    deps = [
        ":abstract_context",
        ":abstract_tensor_handle",

    no longer include NCCL in the binary install. TensorFlow usage with multiple
    GPUs and NCCL requires upgrade to
    [NCCL 2.2](https://developer.nvidia.com/nccl). See updated install guides:
    [TensorFlow GPU support](https://www.tensorflow.org/install/gpu) and
    [Build TensorFlow from source](https://www.tensorflow.org/install/source).

    TF_Status status;
    // Let this custom device choose the device to pin this op on if it
    // implements the pinning function.
    if (device_.shall_pin_to_this_device != nullptr) {
      return device_.shall_pin_to_this_device(tensorflow::wrap(op), &status);
    }
    return errors::Unimplemented("No custom device pinning implementation.");
  }

 private:
  TFE_Context* context_;

        "tf_private_symbols.lds",
    ],
)

genrule(
    name = "install_headers",
    srcs = [
        "//tensorflow/c:headers",
        "//tensorflow/c/eager:headers",
        "//tensorflow/cc:headers",
        "//tensorflow/core:headers",
        "@local_xla//xla/stream_executor:stream_executor_install_hdrs",
    ],
    outs = ["include"],
    cmd = """
    mkdir $@

# strongly recommend that you migrate to Clang as your compiler for TensorFlow
# Linux builds. Instructions are available in the official documentation:
# https://www.tensorflow.org/install/source#install_clang_recommended_linux_only
# Another good option is to use our Docker containers to build and test TF:
# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/tf_sig_build_dockerfiles.

            # Items below are for backwards compatibility when not using
            # TF_CUDA_PATHS.
            'CUDA_TOOLKIT_PATH',
            'CUDNN_INSTALL_PATH',
            'NCCL_INSTALL_PATH',
            'NCCL_HDR_PATH',
            'TENSORRT_INSTALL_PATH'
        ]
        # Note: set_action_env_var above already writes to bazelrc.
        for name in cuda_env_names:
          if name in environ_cp:

TFCI_WHL_BAZEL_TEST_ENABLE=1
TFCI_WHL_SIZE_LIMIT=240M
TFCI_WHL_SIZE_LIMIT_ENABLE=1

# 3.11 is the system python on our images
case $TFCI_PYTHON_VERSION in
3.11)
  TFCI_MACOS_PYENV_INSTALL_ENABLE=0
  ;;
*)
  TFCI_MACOS_PYENV_INSTALL_ENABLE=1
  ;;