StabilityLevel: metrics.ALPHA,
		},
		[]string{"name", "type", "operation", "error_type", "rejection_code"})

	webhookFailOpen := metrics.NewCounterVec(
		&metrics.CounterOpts{

    %bounded = "tf.XlaSetBound"(%arg0, %bound) : (tensor<i32>, tensor<i32>) -> tensor<i32>

    // CHECK: return %[[RESULT]]
    func.return %bounded : tensor<i32>
  }

  // CHECK-LABEL: func @greater
  func.func @greater(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>) -> tensor<2xi1> {
    // CHECK-NEXT:  mhlo.compare GT, %arg0, %arg1

  private final class ComplementRanges extends ImmutableList<Range<C>> {
    // True if the "positive" range set is empty or bounded below.
    private final boolean positiveBoundedBelow;

    // True if the "positive" range set is empty or bounded above.
    private final boolean positiveBoundedAbove;

    private final int size;

    ComplementRanges() {

  private final class ComplementRanges extends ImmutableList<Range<C>> {
    // True if the "positive" range set is empty or bounded below.
    private final boolean positiveBoundedBelow;

    // True if the "positive" range set is empty or bounded above.
    private final boolean positiveBoundedAbove;

    private final int size;

    ComplementRanges() {

LogicalResult Tf2XlaRewriter::LegalizeOp() {
  for (Type ty : op_->getOperandTypes()) {
    auto ranked_ty = mlir::dyn_cast<ShapedType>(ty);
    // Only bounded operands are supported in the XLA builders.
    if (!IsBounded(ranked_ty)) {
      return op_->emitRemark()
             << "lowering requires bounded tensor operands " << ranked_ty;
    }
  }

  if (HasSymbolRefAttr(op_)) {

		n.SetBounded(bounded(r, t.NumElem()))
		if base.Flag.LowerM != 0 && n.Bounded() && !ir.IsConst(n.Index, constant.Int) {
			base.Warn("index bounds check elided")
		}
	} else if ir.IsConst(n.X, constant.String) {
		n.SetBounded(bounded(r, int64(len(ir.StringVal(n.X)))))
		if base.Flag.LowerM != 0 && n.Bounded() && !ir.IsConst(n.Index, constant.Int) {
			base.Warn("index bounds check elided")
		}
	}
	return n
}

    auto result_ty = mlir::cast<mlir::RankedTensorType>(type_and_idx.value());

    // If the result type isn't static, then the owner of the result may be a
    // cast op from a more specific bounded type to an unbounded dynamic type.
    // Use the bounded type to get the buffer size.
    mlir::RankedTensorType buffer_ty = result_ty;
    if (!buffer_ty.hasStaticShape()) {
      mlir::Value return_val = return_op->getOperand(idx);

// ID -> pod
var quotaPodMap = make(map[common.QuotaID]types.UID)

// Directory -> pod
var dirPodMap = make(map[string]types.UID)

// Backing device -> applier
// This is *not* cleaned up; its size will be bounded.
var devApplierMap = make(map[string]common.LinuxVolumeQuotaApplier)

// Directory -> applier
var dirApplierMap = make(map[string]common.LinuxVolumeQuotaApplier)
var dirApplierLock sync.RWMutex

// Pod -> refcount

  };

  supported_ops->insert(allowlist_ops.begin(), allowlist_ops.end());
}

// Adds the list of ops that are only supported in the old bridge.
// TODO(b/168036682): Remove bounded dynamism ops now that MLIR bridge supports
// bounded dynamism.
// TODO(b/257574556): Remove the need for this manual list by making use of old
// bridge phase 2 op list.
void AddOldBridgeOnlyOps(MLIRContext* context,

        index, replication_attr_name);
    arg->set_is_same_data_across_replicas(attr != nullptr && attr.getValue());

    // Currently only support first dimension to be bounded dynamic.
    arg->mutable_is_bounded_dynamic_dim()->Add(
        dynamic_arg_idx_set.contains(index));
  }

  return success();
}

// Populates a TPUCompileMetadataProto with result sharding from a