task_and_device = {task, device};
    }
  }

  return topology;
}

// Determine execution devices when topology and device assignment are defined.
// With a topology device coordinate to task and device mapping, device
// assignment device coordinates can then be mapped to task and device for TPU
// devices. The device assignment array is also validated.
//

LogicalResult GetTpuDeviceAssignment(
    ClusterOp cluster, ReplicateOp replicate, mlir::ModuleOp module,
    absl::StatusOr<TPUDeviceAssignment>& status_or_tpu_device_assignment) {
  mlir::TF::RuntimeDevices devices;
  if (failed(tensorflow::GetDevicesFromOp(module, &devices))) return failure();

  uint32_t num_replicas = replicate.getN();

  auto num_cores_per_replica_attr = cluster->getAttrOfType<mlir::IntegerAttr>(

// RUN: tf-opt %s -split-input-file -tf-xla-broadcast | FileCheck %s

    return
  fi

  local device="${devices[0]}"
  if [ "${#devices[@]}" -ne 1 ]; then
    seen_arrays=(/dev/md/*)
    device=${seen_arrays[0]}
    echo "Setting RAID array with local SSDs on device ${device}"
    if [ ! -e "$device" ]; then
      device="/dev/md/0"
      echo "y" | mdadm --create "${device}" --level=0 --raid-devices=${#devices[@]} "${devices[@]}"
    fi
  fi

        ":__subpackages__",
        "//tensorflow/python:__subpackages__",
    ],
    deps = [
        "@llvm-project//mlir:AffineDialect",
        "@llvm-project//mlir:QuantOps",
        # Link jit lib to link JIT devices required to run
        # xla-legalize-tf-with-tf2xla pass.
        "//tensorflow/compiler/jit",
        "//tensorflow/compiler/mlir/lite:tensorflow_lite",
        "//tensorflow/compiler/mlir/lite:tensorflow_lite_legalize_tf",

std::unique_ptr<OperationPass<mlir::ModuleOp>> CreateCrossHostTransferPass();

// Creates a pass that adds the device attribute to every tf.Const op based on
// the device attribute of the operations that read its result. If the result of
// a tf.Const op is read by operations placed on multiple devices, then the pass
// will replicate the tf.Const op once for each device.
std::unique_ptr<OperationPass<ModuleOp>> CreateConstantOpDeviceAssignmentPass();

		return m, nil
	}
	services, err := kl.serviceLister.List(labels.Everything())
	if err != nil {
		return m, fmt.Errorf("failed to list services when setting up env vars")
	}

	// project the services in namespace ns onto the master services
	for i := range services {
		service := services[i]
		// ignore services where ClusterIP is "None" or empty
		if !v1helper.IsServiceIPSet(service) {

}

def SimpleTFDeviceAssignmentPass : Pass<"tf-simple-device-assignment", "mlir::func::FuncOp"> {
  let summary = "Simple device assignment in TF dialect.";
  let constructor = "TF::CreateSimpleTFDeviceAssignmentPass()";
  let options = [
    Option<"default_device_", "default-device", "std::string", /*default=*/"\"cpu\"",
           "The default device to assign.">
  ];
  let description = [{

def TF_DeviceIndexOp : TF_Op<"DeviceIndex", [Pure, TF_NoConstantFold]> {
  let summary = "Return the index of device the op runs.";

  let description = [{
Given a list of device names, this operation returns the index of the device
this op runs. The length of the list is returned in two cases:
(1) Device does not exist in the given device list.
(2) It is in XLA compilation.
  }];

  let arguments = (ins

        decisions.
*   PluggableDevice
    *   Third-party devices can now connect to TensorFlow as plug-ins through
        [StreamExecutor C API](https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api.md).
        and
        [PluggableDevice](https://github.com/tensorflow/community/blob/master/rfcs/20200624-pluggable-device-for-tensorflow.md)
        interface.