//
//	OpVarDef x
//	base, len, cap = pieces of x[1:]
//	x = {base, len, cap}
//
// then the liveness analysis would decide the previous value of x was
// unnecessary even though it is about to be used by the x[1:] computation.
// Similarly, if the generated code looked like:
//
//	base, len, cap = pieces of x[1:]
//	x = {base, len, cap}
//	OpVarDef x
//
// then the liveness analysis will not preserve the new value of x, because

 * java.util.concurrent.Executor) Futures.catching}.
 *
 * <p>This class implements all methods in {@code ListenableFuture}. Subclasses should provide a way
 * to set the result of the computation through the protected methods {@link #set(Object)}, {@link
 * #setFuture(ListenableFuture)} and {@link #setException(Throwable)}. Subclasses may also override

 * java.util.concurrent.Executor) Futures.catching}.
 *
 * <p>This class implements all methods in {@code ListenableFuture}. Subclasses should provide a way
 * to set the result of the computation through the protected methods {@link #set(Object)}, {@link
 * #setFuture(ListenableFuture)} and {@link #setException(Throwable)}. Subclasses may also override

 *       SMALL_DELAY_MS}, {@code MEDIUM_DELAY_MS}, {@code LONG_DELAY_MS}. The idea here is that a
 *       SHORT is always discriminable from zero time, and always allows enough time for the small
 *       amounts of computation (creating a thread, calling a few methods, etc) needed to reach a
 *       timeout point. Similarly, a SMALL is always discriminable as larger than SHORT and smaller

             'compilation to reduce the compilation time?'),
      'Eigen strong inline overridden.', 'Not overriding eigen strong inline, '
      'some compilations could take more than 20 mins.'):
    # Due to a known MSVC compiler issue
    # https://github.com/tensorflow/tensorflow/issues/10521
    # Overriding eigen strong inline speeds up the compiling of

    DeclareOpInterfaceMethods<RegionBranchOpInterface>,
    SingleBlockImplicitTerminator<"YieldOp">]> {
  let summary = [{Poly call}];

  let description = [{
    Have multiple function bodies for the same computation. This allows a
    program compiler/interpreter to choose one of the available options to
    execute the program based on which one is most suitable for the target
    backend.

  // Returns true if a return type was changed.
  bool InferShapeForXlaCallModule(XlaCallModuleOp op);

  // Infers the shape of _XlaHostComputeMlir based on the host computation
  // module.  Returns true if a return type was changed.
  bool InferShapeForXlaHostComputeMlir(_XlaHostComputeMlirOp op);

  // Infers the shape of function attached to XlaHostCompute.

	rbit := z.mant.bit(r) & 1    // rounding bit; be safe and ensure it's a single bit
	// The sticky bit is only needed for rounding ToNearestEven
	// or when the rounding bit is zero. Avoid computation otherwise.
	if sbit == 0 && (rbit == 0 || z.mode == ToNearestEven) {
		sbit = z.mant.sticky(r)
	}
	sbit &= 1 // be safe and ensure it's a single bit

	// cut off extra words

	}

	gw, gwMap, nsReferences := convertGateways(ctx)
	ctx.GatewayReferences = gwMap
	result.Gateway = gw

	result.VirtualService = convertVirtualService(ctx)

	// Once we have gone through all route computation, we will know how many routes bound to each gateway.
	// Report this in the status.
	for _, dm := range gwMap {
		for _, pri := range dm {
			if pri.ReportAttachedRoutes != nil {
				pri.ReportAttachedRoutes()
			}

        auto backend_config = builder_.CreateString(
            mlir::cast<mlir::vhlo::StringV1Attr>(vhlo_op.getBackendConfig())
                .getValue()
                .str());
        // building the computation info
        auto flex_builder = std::make_unique<flexbuffers::Builder>();
        size_t map_start = flex_builder->StartMap();
        auto attrs = vhlo_op->getAttrs();