LogicalResult match(stablehlo::ReshapeOp op) const override {
    return success(IsOpFullyQuantized(op));
  }

  void rewrite(stablehlo::ReshapeOp op,
               PatternRewriter& rewriter) const override {
    rewriter.replaceOpWithNewOp<TFL::ReshapeOp>(
        op, op.getOperand(),
        CreateI32ShapeConstantOp(op.getResult().getType(), op->getLoc(),
                                 rewriter));

LogicalResult ConcatOffsetOp::verify() {
  ConcatOffsetOp op = *this;
  if (op.getN() < 2)
    return op.emitOpError() << "requires N to be at least 2, got " << op.getN();

  if (op.getShape().size() != op.getOffset().size())
    return op.emitOpError()
           << "requires sizes of shapes and offsets to be the same, got sizes "
           << op.getShape().size() << " and " << op.getOffset().size();

  auto ranked_dim =

mlir::LogicalResult ReshapeOp::verify() {
  ReshapeOp op = *this;
  auto error_handler = [&op](const llvm::Twine& message) -> LogicalResult {
    return op.emitOpError() << message;
  };
  TensorType expected_ty;
  if (failed(GetReshapeOutputType(op.getInput(), op.getShape(), error_handler,
                                  expected_ty)))
    return failure();

  auto output_ty = op.getType().dyn_cast<RankedTensorType>();

LogicalResult ShapeNOp::verify() {
  ShapeNOp op = *this;
  const size_t num_tensors = op.getN();

  if (op.getNumOperands() != num_tensors)
    return op.emitOpError() << "requires " << num_tensors << " operand(s), got "
                            << op.getNumOperands() << " operand(s)";

  if (op.getNumResults() != num_tensors)
    return op.emitOpError() << "requires " << num_tensors << " result(s), got "

}

// Returns true if the op creates a TensorList.
bool IsTensorListInitOp(Operation* op) {
  return isa<TensorListReserveOp>(op) || isa<EmptyTensorListOp>(op) ||
         isa<TensorListFromTensorOp>(op);
}

// Returns the `element_shape` operand of the ops that create a TensorList.
Value GetElementShapeOperand(Operation* op) {
  if (auto empty_tl = dyn_cast<EmptyTensorListOp>(op))
    return empty_tl.getElementShape();

    }
    return failure();
  }

  // Walks up the op and ignore all precedding ops of type Tys.
  // Returns the first producer op whose type is not in Tys.
  template <typename... Tys>
  Value recursivelyWalkUp(Value op) const {
    while (llvm::isa_and_nonnull<Tys...>(op.getDefiningOp())) {
      Operation* producer = op.getDefiningOp();
      op = producer->getOperand(/*idx=*/0);
    }

    return op;

		}
	}

	if op != nil {
		ps.writeString(op.Name)
		if ps.llvmStyle && op.Name == "noexcept" {
			ps.writeByte(' ')
		}
	} else if c, ok := u.Op.(*Cast); ok {
		ps.startScope('(')
		ps.print(c.To)
		ps.endScope(')')
	} else {
		ps.print(u.Op)
	}

	if !u.Suffix {
		isDelete := op != nil && (op.Name == "delete " || op.Name == "delete[] ")

(Add64 (Add64 i:(Const64 <t>) z) x) && (z.Op != OpConst64 && x.Op != OpConst64) => (Add64 i (Add64 <t> z x))
(Add32 (Add32 i:(Const32 <t>) z) x) && (z.Op != OpConst32 && x.Op != OpConst32) => (Add32 i (Add32 <t> z x))
(Add16 (Add16 i:(Const16 <t>) z) x) && (z.Op != OpConst16 && x.Op != OpConst16) => (Add16 i (Add16 <t> z x))
(Add8  (Add8  i:(Const8  <t>) z) x) && (z.Op != OpConst8  && x.Op != OpConst8)  => (Add8  i (Add8  <t> z x))

    }

    // CustomTfOp is just a wrapper around a TF op, we export the custom Op
    // not the wrapper, so we fetch the op from the region.
    if (auto custom_op = dyn_cast<mlir::TFL::CustomTfOp>(inst)) {
      // If we have custom op with a region, then use the first op in the
      // region, if it exists, otherwise just use params for custom op.
      if (!custom_op.getBody().empty()) {

		args[2+i] = a.Class - 1
	}

	// Lookup op in optab.
	ops := oprange[p.As&obj.AMask]
	cmp := [len(args)]*[C_NCLASS]bool{}
	for i := range cmp {
		cmp[i] = &xcmp[args[i]]
	}
	for i := range ops {
		op := &ops[i]
		if cmp[0][op.a1] && cmp[1][op.a2] &&
			cmp[2][op.a3] && cmp[3][op.a4] &&
			cmp[4][op.a5] && cmp[5][op.a6] {
			p.Optab = uint16(cap(optab) - cap(ops) + i + 1)
			return op