for k, v := range nc.zoneStates {
			newState := newZoneStates[k]
			if v == newState {
				continue
			}
			logger.Info("Controller detected that zone is now in new state", "zone", k, "newState", newState)
			nc.setLimiterInZone(k, len(zoneToNodeConditions[k]), newState)
			nc.zoneStates[k] = newState
		}
	}
}

	// other values as these are modified.
	newState := baseState
	if updating {
		newState = blockLocs[b.ID].startState
	}

	for it := newState.Iterator(); !it.Done(); {
		k, d := it.Next()
		thisSlot := d.(*liveSlot)
		x := thisSlot.VarLoc
		x0 := x // initial value in newState

		// Intersect this slot with the slot in all the predecessors

                    .nagUser();
                newState = domainObjectContext.getModel().fromMutableState(p -> resolveExclusivelyIfRequired());
            }
        } else {
            newState = resolveExclusivelyIfRequired();
        }

        return newState;
    }

    private ResolverResults resolveExclusivelyIfRequired() {

      // required by the new converter, from the reshape op's option.
      auto new_shape = op.builtin_options.AsReshapeOptions()->new_shape;
      auto shape_type = tensorflow::GetTypeFromTFTensorShape(
          {static_cast<int64_t>(new_shape.size())}, builder.getIntegerType(32));

      mlir::SmallVector<mlir::Attribute, 4> shape;
      for (auto s : new_shape) {
        shape.push_back(

    auto new_shape_const_attr =
        DenseElementsAttr::get(shape_spec_type, new_shape.getShape());
    rewriter.setInsertionPointAfter(weight_op);
    auto new_shape_const = rewriter.create<TF::ConstOp>(
        weight_op->getLoc(), shape_spec_type, new_shape_const_attr);
    auto reshape_op = rewriter.create<TF::ReshapeOp>(
        weight_op->getLoc(), new_shape, weight_op->getResult(0),
        new_shape_const);

//sys	AdjustTokenPrivileges(token Token, disableAllPrivileges bool, newstate *Tokenprivileges, buflen uint32, prevstate *Tokenprivileges, returnlen *uint32) (err error) = advapi32.AdjustTokenPrivileges
//sys	AdjustTokenGroups(token Token, resetToDefault bool, newstate *Tokengroups, buflen uint32, prevstate *Tokengroups, returnlen *uint32) (err error) = advapi32.AdjustTokenGroups

      rewriter.replaceOp(op, {input});
      return success();
    }

    RankedTensorType shape_type =
        tensorflow::GetTypeFromTFTensorShape({-1}, rewriter.getIntegerType(32));
    auto new_shape = rewriter.create<TF::ShapeOp>(loc, shape_type, input);
    SmallVector<int64_t, 8> output_shape(/*Size=*/1, op.getNumElements());
    for (const auto &dim : dense_elem_attr.getValues<APInt>())