*   Iterator<Long> it = set.iterator(); // Must be in synchronized block
   *   while (it.hasNext()) {
   *     foo(it.next());
   *   }
   * }
   * }</pre>
   *
   * <p>Failure to follow this advice may result in non-deterministic behavior.
   *
   * <p>The returned bimap will be serializable if the specified bimap is serializable.
   *
   * @param bimap the bimap to be wrapped in a synchronized view

   *   Iterator<Long> it = set.iterator(); // Must be in synchronized block
   *   while (it.hasNext()) {
   *     foo(it.next());
   *   }
   * }
   * }</pre>
   *
   * <p>Failure to follow this advice may result in non-deterministic behavior.
   *
   * <p>The returned bimap will be serializable if the specified bimap is serializable.
   *
   * @param bimap the bimap to be wrapped in a synchronized view

      %8 = "tf.Identity"(%arg4) {device = ""} : (tensor<1xi32>) -> tensor<1xi32>
      // CHECK: tf.RandomStandardNormal{{.*}}(%[[SHAPE]])
      %9 = "tf.RandomStandardNormal"(%arg4) {device = "", seed = 87654321 : i64, seed2 = 0 : i64} : (tensor<1xi32>) -> tensor<?xf32>
      %10 = "tf.Pow"(%9, %5) {device = ""} : (tensor<?xf32>, tensor<f32>) -> tensor<?xf32>

}

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 = [{

  %14 = "tf.Cast"(%13) {Truncate = false, device = ""} : (tensor<2x16xf32>) -> tensor<2x16xi32>
  %15 = "tf.AddV2"(%14, %3) {device = ""} : (tensor<2x16xi32>, tensor<i32>) -> tensor<2x16xi32>
  %16 = "tf.Mul"(%15, %8) {device = ""} : (tensor<2x16xi32>, tensor<i32>) -> tensor<2x16xi32>
  %17 = "tf.AddV2"(%16, %6) {device = ""} : (tensor<2x16xi32>, tensor<2x1xi32>) -> tensor<2x16xi32>

// 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,

    // CHECK: %[[COMPILE_OUTPUT:[0-9]*]]:2 = "tf_device.launch"() <{device = "/job:worker/replica:0/task:0/device:CPU:0"}>
    // CHECK-NEXT: "tf._TPUCompileMlir"(%[[A_SHAPE_OUTPUT]])
    // CHECK-SAME: metadata
    // CHECK-SAME: mlir_module
    // CHECK-SAME: func @main
    // CHECK-SAME: tf.B
    // CHECK-NOT: func = @tpu0_func
    // CHECK: "tf_device.launch"() <{device = "/job:worker/replica:0/task:0/device:CPU:0"}>

#Formats the given device ($1) if needed and mounts it at given mount point
# ($2).
function safe-format-and-mount() {
  local device
  local mountpoint
  device="$1"
  mountpoint="$2"

  # Format only if the disk is not already formatted.
  if ! tune2fs -l "${device}" ; then
    echo "Formatting '${device}'"
    mkfs.ext4 -F "${device}"
  fi

  mkdir -p "${mountpoint}"
  echo "Mounting '${device}' at '${mountpoint}'"

  %1 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16x28xf32>} : () -> tensor<16x28xf32>
  %2 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16x16xf32>} : () -> tensor<16x16xf32>
  %3 = "tf.Const"() {device = "", dtype = f32, value = dense<0.000000e+00>: tensor<16xf32>} : () -> tensor<16xf32>

			return nil, fmt.Errorf("cannot find volume %q to pass into container %q", device.Name, container.Name)
		}
		// Get a symbolic link associated to a block device under pod device path
		dirPath, volName := vol.BlockVolumeMapper.GetPodDeviceMapPath()
		symlinkPath := filepath.Join(dirPath, volName)