// types match the result types.
  auto yield = cast<YieldOp>(region.front().getTerminator());
  auto updated_results = llvm::to_vector<4>(yield.getOperands());

  // If the yield types do not match the IfRegion result types, add appropriate
  // casts.
  rewriter.setInsertionPoint(yield);
  for (auto it : llvm::zip(op.getResultTypes(), updated_results)) {

// division can result in strange behavior.
//
//      floordiv = cast(floordiv(cast(left), cast(right))))
//
//   %left_cast = cast(%left)
//   %right_cast = cast(%right)
//   %div = div(%left, %left)
//   %floored = floor(%div)
//   %floored_cast = cast(%floored)
//
// Required to manually specify the intermediate types.

*   `tf.nn.embedding_lookup_sparse`

    *   Optimized this function for some cases by fusing internal operations.

*   `tf.saved_model.SaveOptions`

// If there is a change in supporting more types in the TFLite cast op kernel,
// the While loop outline pass should be updated since it inserts cast op(s)
// after the TF -> TFL legalization pass is done.
// LINT.IfChange
def TFL_CastOp : TFL_Op<"cast", [
    Pure,
    SameOperandsAndResultShape]> {
  let summary = "Cast operator";

  let description = [{
    Casts input from input type to output type.
  }];

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");

  ShapedType type;
  InspectResult result = {};
  if (auto cst = dyn_cast<ConstOp>(inst)) {
    attr = cst.getValue();
    type = mlir::cast<ShapedType>(cst.getType());
  } else if (auto cst = dyn_cast<QConstOp>(inst)) {
    attr = cst.getValue();
    type = mlir::cast<ShapedType>(cst.getType());
  } else {
    result.can_compress = false;
    return result;
  }

    // Handle per tensor cases only.
    if (!mlir::isa<quant::UniformQuantizedType>(
            (getElementTypeOrSelf(op.getType())))) {
      return failure();
    }

    // Remove identity reshape with both static result and input shape.
    auto result_type = mlir::cast<ShapedType>(op.getType());
    auto input_type = mlir::cast<ShapedType>(op.getInput().getType());

    // Constant folding

  transient volatile int busy;

  /** Package-private default constructor */
  Striped64() {}

  /** CASes the base field. */
  final boolean casBase(long cmp, long val) {
    return UNSAFE.compareAndSwapLong(this, baseOffset, cmp, val);
  }

  /** CASes the busy field from 0 to 1 to acquire lock. */
  final boolean casBusy() {
    return UNSAFE.compareAndSwapInt(this, busyOffset, 0, 1);
  }