#include <vector>

#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project

namespace mlir {
namespace TFL {

// Check if the given op has stateful operands and return their stateful
// operand indices.
bool IsStatefulOp(Operation* op, std::vector<int>* stateful_operand_indices);

}  // namespace TFL
}  // namespace mlir

// Returns the RankedTensorType for the given operand. TensorFlow constant ops
// may have non-static shape because the shape is not propagated during constant
// folding. If the defining op for the given operand is a constant op, this
// routine uses the constant op's attribute to get the actual shape.
RankedTensorType GetRankedTensorTypeForOperand(Value operand) {
  DenseElementsAttr attr;
  if (matchPattern(operand, m_Constant(&attr))) {

  Value GetNumElements(TF::TensorListReserveOp op, ValueRange operands,
                       PatternRewriter *rewriter) const override {
    Value scalar_zero = CreateI32SplatConst(op.getLoc(), rewriter, {}, 0);
    Type shape_dtype = getElementTypeOrSelf(op.getElementShape().getType());
    Value num_elements = operands[1];
    IntegerAttr attr;
    if (matchPattern(num_elements, m_Constant(&attr))) {

    }

    auto fused_loc = rewriter.getFusedLoc(locations);

    // The fused contraction has the same operands as the original contraction
    // with `bias` from the BiasAddOp appended.
    SmallVector<Value, 4> operands(contraction.operand_begin(),
                                   contraction.operand_end());
    operands.push_back(bias_add.getBias());

    // The fused contraction has the same attributes as the original

        decomposed_partitioned_call_callees) {
  for (OpOperand& operand : op.getOpOperands()) {
    if (getElementTypeOrSelf(operand.get().getType()).isa<TF::ResourceType>()) {
      return op.emitOpError()
             << "found unexpected type " << operand.get().getType()
             << " of operand #" << operand.getOperandNumber()
             << ", resource type operands are expected to have been "

      // original If op.
      if (op_to_inline.hasTrait<OpTrait::IsTerminator>()) {
        updated_results.reserve(op_to_inline.getNumOperands());
        for (Value operand : op_to_inline.getOperands())
          updated_results.push_back(mapper.lookup(operand));
        break;
      }

      // Otherwise, clone the op here.
      rewriter.clone(op_to_inline, mapper);
    }
    rewriter.replaceOp(op, updated_results);

				s.Insert(randomStringMaker.makeString(here.minStringLen, here.maxStringLen))
			}
			return s
		}
		operands := make([]String, 500)
		for i := range operands {
			operands[i] = makeSet()
		}
		randOperand := func() String { return operands[rand.Intn(len(operands))] }

		b.Run(fmt.Sprintf("insert-%v", here.size), func(b *testing.B) {
			b.ReportAllocs()
			for i := 0; i < b.N; i++ {

}
func uint64div(n, d uint64) uint64 {
	// Check for 32 bit operands
	if uint32(n>>32) == 0 && uint32(d>>32) == 0 {
		if uint32(d) == 0 {
			panicdivide()
		}
		return uint64(uint32(n) / uint32(d))
	}
	q, _ := dodiv(n, d)
	return q
}

func uint64mod(n, d uint64) uint64 {
	// Check for 32 bit operands
	if uint32(n>>32) == 0 && uint32(d>>32) == 0 {
		if uint32(d) == 0 {

  bool ForwardThroughTFOperation(Operation *op,
                                 ArrayRef<const StateT *> operands,
                                 ArrayRef<StateT *> results) {
    if (auto cast = dyn_cast<TF::CastOp>(op)) {
      this->join(results[0], *operands[0]);
    } else if (auto while_op = dyn_cast<TF::WhileRegionOp>(op)) {
      for (auto &region : while_op->getRegions()) {

// immutable because they are from quantization-aware training.
//
// The algorithm traverses each op and sets the quantization parameters of its
// operands and results, according to its quantization specification, and then
// adds the operands and results to the worklist. If there are any conflicts
// (for example, there are quantization parameters propagated from the previous