}

// -----

// Tests collecting compilation and execution devices results in an error.

module attributes {tf.versions = {producer = 888 : i32}, tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
  func.func @bad_devices() {
    // expected-error@+1 {{error in fetching TPU compilation/execution devices: no TPU_SYSTEM devices found}}

// For EnqueueTPUEmbedding ops the device ordinal corresponds to the resource
// instance. We also take the `device` attribute into account in order to avoid
// dependencies between ops with the same ordinal on different devices.

// Helper function to get an absolute device string, combining device and
// ordinal attribute values.

  func.func @op_dominate_repro(%writer: tensor<*x!tf_type.resource> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}) -> () {
    %step = "tf.Const"() {device = "", value = dense<0> : tensor<i64>} : () -> tensor<i64>
    %tag = "tf.Const"() {device = "", value = dense<""> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>
    %wmetadata = "tf.Const"() {device = "", value = dense<""> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>

    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

		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) {

  // expected-remark@above {{Sinks: {5}}}
}

// -----

// Tests that we treat different op instances with `TPUEmbeddingSideEffect` as
// independent if they have different devices (even if device ordinals are
// equal).
func.func @embedding_effect_same_ordinal_different_devices(
  // expected-remark@above {{ID: 7}}
  %arg0: tensor<!tf_type.string>) {
  tf_executor.graph {

    }
    return false;
  }
  if (a.device() != b.device()) {
    if (diff) {
      *diff = absl::StrCat(diff_preamble, " mismatch for node ", a.name(),
                           ", expected device '", a.device(), "' got '",
                           b.device());
    }
    return false;
  }
  if (a.input_size() != b.input_size()) {
    if (diff) {

	EM_ST100         Machine = 60  /* STMicroelectronics ST100 processor. */
	EM_TINYJ         Machine = 61  /* Advanced Logic Corp. TinyJ processor. */
	EM_X86_64        Machine = 62  /* Advanced Micro Devices x86-64 */
	EM_PDSP          Machine = 63  /* Sony DSP Processor */
	EM_PDP10         Machine = 64  /* Digital Equipment Corp. PDP-10 */
	EM_PDP11         Machine = 65  /* Digital Equipment Corp. PDP-11 */

}

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 = [{

// After globalMapPath is removed, fd lock by loopback for the device can
// be released safely because no one can consume the device at this point.
// At last, device open status will be checked just in case.
// If all steps are completed, the device is marked as unmounted.
func (og *operationGenerator) GenerateUnmapDeviceFunc(
	deviceToDetach AttachedVolume,