*
     * @param outputType The type to cast the input to
     * @param object The object to be cast (must not be {@code null})
     * @param <O> The type to be cast to
     * @param <I> The type of the object to be vast
     * @return The input object, cast to the output type
     */
    public static <O, I> O cast(Class<O> outputType, I object) {
        try {

     *
     * ```kt
     * if (this is String) {
     *   this.substring() // 'this' receiver is explicit, so no implicit smart-cast here.
     *
     *   smartcast() // 'this' receiver is implicit, therefore there is implicit smart-cast involved.
     * }
     * ```
     */
    public fun KtExpression.getImplicitReceiverSmartCast(): Collection<KaImplicitReceiverSmartCast> =

      >();
}

OpFoldResult StorageCastOp::fold(FoldAdaptor) {
  // Matches x -> [scast -> scast] -> y, replacing the second scast with the
  // value of x if the casts invert each other.
  auto srcScastOp = getArg().getDefiningOp<StorageCastOp>();
  if (!srcScastOp || srcScastOp.getArg().getType() != getType())
    return OpFoldResult();
  return srcScastOp.getArg();
}

      >();
}

OpFoldResult StorageCastOp::fold(FoldAdaptor) {
  // Matches x -> [scast -> scast] -> y, replacing the second scast with the
  // value of x if the casts invert each other.
  auto srcScastOp = getArg().getDefiningOp<StorageCastOp>();
  if (!srcScastOp || srcScastOp.getArg().getType() != getType())
    return OpFoldResult();
  return srcScastOp.getArg();
}

#include "tensorflow/compiler/tf2xla/tf2xla_defs.h"

namespace mlir {
namespace TF {

// TODO(b/229028654) Use definitions from tf2xla_defs.h directly. We currently
// don't do this to avoid explicit casts (implicit conversion from
// `absl::string_view` to `llvm::StringRef` is not supported until C++17).

// Whether soft placement is allowed. If true, the marked node is eligible for
// outside compilation.

llvm::StringRef ProcessIndexPath(mlir::ArrayAttr index_path) {
  if (index_path.size() == 1 && mlir::isa<mlir::StringAttr>(index_path[0])) {
    // TODO(chky): Support cases where index_path is not a single string.
    return mlir::cast<mlir::StringAttr>(index_path[0]).getValue();
  }
  return "";
}

absl::StatusOr<std::pair<tensorflow::DataType, tensorflow::PartialTensorShape>>
ProcessTensorSpec(mlir::TensorType type) {

		),
	}
	c.syncHandler = c.handleVersionUpdate

	crdinformer.Informer().AddEventHandler(cache.ResourceEventHandlerFuncs{
		AddFunc: func(obj interface{}) {
			cast := obj.(*apiextensionsv1.CustomResourceDefinition)
			c.enqueueCRD(cast)
		},
		UpdateFunc: func(oldObj, newObj interface{}) {
			// Enqueue both old and new object to make sure we remove and add appropriate API services.

  for (VariableV2Op variable_v2_op : variable_v2s_to_replace) {
    builder.setInsertionPoint(variable_v2_op);
    ShapedType shaped_type =
        mlir::cast<ShapedType>(variable_v2_op.getResult().getType());
    TensorType tensor_type = mlir::cast<TensorType>(DropRefType(shaped_type));
    StringAttr device_attr =
        variable_v2_op->getAttrOfType<StringAttr>("device");

#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

namespace mlir {
namespace TFL {
namespace tac {
namespace {

// These are just fake costs.
constexpr float kDequantCost = 2.0;
constexpr float kQuantCost = 2.0;
constexpr float kRequantCost = 2.0;

// TODO(renjieliu): Ideally this should consider different kinds of SOCs as
// well.

  func.setVisibility(mlir::func::FuncOp::Visibility::Public);
  auto func_builder = OpBuilder::atBlockBegin(func.addEntryBlock());
  func_builder.create<mlir::func::ReturnOp>(func.getLoc());
  // In cases where there is a session initializer op with empty initializer,
  // replace the session initializer with the new one that points to the session
  // initializer func.