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 "

    lstm_variant->op_code =
        tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM;
  } else {
    op.emitError("ConvertLstmStatsToQDQs pass only supports LSTMs.");
    return failure();
  }
  lstm_variant->use_projection =
      !mlir::isa<NoneType>(op.getProjectionWeights().getType());
  lstm_variant->use_peephole =
      !mlir::isa<NoneType>(op.getCellToOutputWeights().getType());

		v.Op = OpARM64MULW
		return true
	case OpMul32F:
		v.Op = OpARM64FMULS
		return true
	case OpMul64:
		v.Op = OpARM64MUL
		return true
	case OpMul64F:
		v.Op = OpARM64FMULD
		return true
	case OpMul8:
		v.Op = OpARM64MULW
		return true
	case OpNeg16:
		v.Op = OpARM64NEG
		return true
	case OpNeg32:
		v.Op = OpARM64NEG
		return true
	case OpNeg32F:
		v.Op = OpARM64FNEGS
		return true

      // requantize op to remove the unnecessary requantize op.
      if (const QuantizedType qtype =
              QuantizedType::getQuantizedElementType(q.getArg().getType())) {
        rewriter.setInsertionPoint(op);
        rewriter.replaceOpWithNewOp<quantfork::DequantizeCastOp>(
            op, op.getResult().getType(), q.getArg());
        return success();
      }

      op.replaceAllUsesWith(q.getArg());

  func_.walk([&](quantfork::QuantizeRegionOp op) { all_ops.push_back(op); });
  return all_ops;
}

KernelSpecs::Signature QuantizeContext::GetSignature(
    quantfork::QuantizeRegionOp op) {
  KernelSpecs::Signature signature;
  signature.reserve(op.getInputSpecs().size() + op.getOutputSpecs().size());
  for (int i = 0; i < op.getNumOperands(); ++i) {
    DeviceTarget::AppendToSignature(GetOperandParams(op, i), &signature);
  }

  op->walk([&](Operation *op) {
    if (op->getDialect()->getNamespace() != "vhlo") return;

    // Convert operands
    rewriter.modifyOpInPlace(op, [&]() {
      rewriter.setInsertionPoint(op);
      WrapOperandsInUnrealizedCastAndConvert(op, converter, rewriter);

      // Convert op types
      for (auto value : op->getResults()) {
        rewriter.setInsertionPointAfter(value.getDefiningOp());

    worklist.pop();
    auto result = u.walk([&](SymbolUserOpInterface op) {
      if (llvm::isa<T, Types...>(op)) {
        if (!predicate || predicate(op)) {
          hits.push_back(op);
        } else {
          first_misses.push_back(op);
          return WalkResult::advance();
        }
      }
      llvm::SmallVector<func::FuncOp> callees;
      if (GetCallees(op, symtab, callees).failed()) {
        return WalkResult::interrupt();

	v := a[0]
	if v.Op == shiftOp {
		v = v.Args[0]
	}
	var extOp Op
	if orOp == OpOr64 && (v.Op == OpZeroExt8to64 || v.Op == OpZeroExt16to64 || v.Op == OpZeroExt32to64) ||
		orOp == OpOr32 && (v.Op == OpZeroExt8to32 || v.Op == OpZeroExt16to32) ||
		orOp == OpOr16 && v.Op == OpZeroExt8to16 {
		extOp = v.Op
		v = v.Args[0]
	} else {
		return false
	}
	if v.Op != OpLoad {
		return false
	}

//===----------------------------------------------------------------------===//
// DivNoNan and MulNonNan op patterns.
//===----------------------------------------------------------------------===//

class BinaryNoNanPat<Op FromOp, Op ToOp>
  : Pat<(FromOp $l, $r),
        (TF_SelectV2Op (TF_EqualOp $r,
                                   (TF_ConstOp:$zero (GetScalarOfType<0> $r)),

      auto loader,
      tensorflow::XlaCallModuleLoader::Create(
          context, static_cast<int>(op.getVersion()), op.getModule().str(),
          std::move(disabled_checks), std::move(platforms),
          /*num_invocation_args=*/op.getArgs().size(),
          op.getHasTokenInputOutput()));
  return std::move(*loader).module();
}

// Renames functions in the stablehlo module to avoid naming conflicts with