// of our files will have access to `OpHasSameStaticShapes` when including files
// generated from table-gen.
def OpHasSameStaticShapesPred : CPred<"OpHasSameStaticShapes($0.getDefiningOp())">;
def OpHasSameStaticShapes : Constraint<OpHasSameStaticShapesPred, "op must have static same input shapes">;
def OpHasNotSameStaticShapes : Constraint<Neg<OpHasSameStaticShapesPred>, "op must have not static same input shapes">;

  LogicalResult matchAndRewrite(quantfork::DequantizeCastOp op,
                                PatternRewriter& rewriter) const override {
    auto input_op = op.getArg().getDefiningOp();
    if (auto q = llvm::dyn_cast_or_null<quantfork::QuantizeCastOp>(input_op)) {
      if (!q->getAttr(kVolatileOpAttrName)) return failure();

      // If the quantize op is a requantize op, it is being used in other scale

  auto assign_var = dyn_cast<TF::AssignVariableOp>(use.getOwner());
  if (!assign_var) return nullptr;

  if (use.get() != assign_var.getValue()) return nullptr;

  auto* resource_handle_op = assign_var.getResource().getDefiningOp();
  if (resource_handle_op == parallel_execute) return nullptr;

  if (resource_handle_op &&
      resource_handle_op->getBlock() ==
          parallel_execute.getOperation()->getBlock() &&

  for (uint64_t i = 0, e = op->getNumOperands(); i < e; ++i) {
    // Check that the i'th operand is a broadcast.
    auto broadcast = llvm::dyn_cast_or_null<TF::BroadcastToOp>(
        op->getOpOperand(i).get().getDefiningOp());
    if (!broadcast) continue;

    // Check that the operand of the broadcast has fully defined shape.
    auto broadcast_arg_type = mlir::dyn_cast_or_null<RankedTensorType>(

  if (!return_op) return failure();
  if (return_op.getNumOperands() != 1) return failure();

  ReductionOp reduce_op = dyn_cast_or_null<ReductionOp>(
      return_op.getOperands().front().getDefiningOp());
  if (!reduce_op) return failure();
  if (reduce_op.getLhs() != body.getArgument(0) ||
      reduce_op.getRhs() != body.getArgument(1))
    return failure();

  return success();
}

  // An operation is ready to be scheduled if all its operands are ready. An
  // operand is ready if:
  const auto is_ready = [&](Value value, Operation *top) {
    Operation *parent = value.getDefiningOp();
    // - it is a block argument,
    if (parent == nullptr) return true;
    Operation *ancestor = block->findAncestorOpInBlock(*parent);
    // - it is an implicit capture,

    }
    alias_info.output_index = result.getResultNumber();
  }

  for (auto& operand : cluster_func->getOpOperands()) {
    auto read_op = llvm::dyn_cast_or_null<TF::ReadVariableOp>(
        operand.get().getDefiningOp());
    if (!read_op) continue;
    if (!read_op->hasOneUse()) continue;
    auto it = resource_alias_info_map.find(read_op.getResource());
    if (it == resource_alias_info_map.end()) continue;

void VisitOpOperand(GraphOp graph, Value operand,
                    llvm::SmallPtrSetImpl<Operation*>* reachable_ops,
                    llvm::SmallVectorImpl<Operation*>* ops_to_visit) {
  Operation* def = operand.getDefiningOp();
  if (def && def->getParentOp() == graph && reachable_ops->insert(def).second) {
    // Op has not been visited, add to queue to visit later.
    ops_to_visit->push_back(def);
  }
}

  LogicalResult matchAndRewrite(TFL::DequantizeOp dequant_op,
                                PatternRewriter& rewriter) const override {
    // We only fold i32 -> float pattern.
    auto input = dequant_op.getInput().getDefiningOp();
    if (!input) return failure();

    auto input_dequant = llvm::dyn_cast_or_null<TFL::QConstOp>(input);
    if (!input_dequant) return failure();

 private:
  LogicalResult matchAndRewrite(quantfork::QuantizeCastOp q_op,
                                PatternRewriter& rewriter) const override {
    auto preceding_qcast =
        q_op.getArg().getDefiningOp<quantfork::QuantizeCastOp>();
    if (!preceding_qcast) return failure();

    auto new_qcast = rewriter.create<quantfork::QuantizeCastOp>(
        q_op.getLoc(), q_op.getType(), preceding_qcast.getArg());