}
    auto arg_type = cast<func::FuncOp>(op).getArgument(arg_index).getType();
    return VerifyBoundInputArgType(op, arg_type, symbol_op);
  }
  if (named_attr.getName() == kTfSavedModelIndexPathAttr) {
    return VerifyIndexPath(op, named_attr);
  }

  return op->emitError() << "unknown tf_saved_model dialect arg attribute '"
                         << named_attr.getName().getValue() << "'";
}

          op->getLoc(), op.getOperand().getType(), adaptor.getOperand());
      rewriter.replaceOpWithNewOp<mlir::stablehlo::BitcastConvertOp>(
          op, op.getResult().getType(), output);
    } else {
      // Both input/output are f32. Convert to no-op.
      rewriter.replaceOp(op, adaptor.getOperand());
    }
    return success();
  }
};
}  // namespace

#define GEN_PASS_DEF_CONVERTFUNCTOBFLOAT16PASS

namespace mlir {
namespace TFL {
namespace tac {

// Returns true if 'op' is non const op. Returns false otherwise or if
// 'op' is null.
inline bool IsNonConstOp(Operation* op) {
  if (!op) return false;
  if (llvm::isa<arith::ConstantOp, mlir::func::ConstantOp>(op)) return false;
  if (op->hasTrait<OpTrait::ConstantLike>()) return false;
  if (llvm::isa<TFL::ConstOp, TFL::QConstOp>(op)) return false;
  return true;
}

  // `op`, and bypass all result transposes.
  Location loc = op->getLoc();

  // Move constant op defining result permutation to the beginning of the block.
  permutation_op.getOperation()->moveBefore(&op->getBlock()->front());

  // Bypass Transpose nodes for all results.
  for (OpResult result : op->getResults()) {
    result.setType(

    if (argumentOp && op->getName() == argumentOp->getName()) {
      // Replace the outer operation output with the inner operation.
      return op->getOperand(0);
    }
  } else if (op->getOperand(0) == op->getOperand(1)) {
    return op->getOperand(0);
  }

  return {};
}

inline LogicalResult verifyIsInvolution(Operation* op) {
  // TODO(b/246518997): Add back check for no side effects on operation.

	// result: x
	for {
		x := v_0
		if x.Op != OpARM64Equal {
			break
		}
		v.copyOf(x)
		return true
	}
	// match: (MOVBUreg x:(NotEqual _))
	// result: x
	for {
		x := v_0
		if x.Op != OpARM64NotEqual {
			break
		}
		v.copyOf(x)
		return true
	}
	// match: (MOVBUreg x:(LessThan _))
	// result: x
	for {
		x := v_0
		if x.Op != OpARM64LessThan {
			break
		}

// Checks if an op is inside a lifted function.
// If the given op pointer is a nullptr, returns false.
bool IsInLiftedFunc(Operation* op);

// Checks if the op is inside a StableHLO op with region.
// If the given op pointer is a nullptr, returns false.
bool IsInStableHloOpRegion(Operation* op);

// Checks if a given einsum op is supported for XlaDotV2 quantization.

  // quantization. For dynamic range quantizable ops, it refers to the op
  // specification for checking the support. For custom ops, it checks the
  // provided map.
  bool hasInt8QuantizableOperandAt(Operation* op, int operand_index) const {
    if (visited_nonquantizable_ops_->contains(op)) {
      return false;
    }

    if (auto custom_op = llvm::dyn_cast_or_null<CustomOp>(op)) {
      std::string op_name = custom_op.getCustomCode().str();

        return true;
      };

  return RewriteOp(op, rewriter, get_broadcasted_shape);
}

template <typename Op>
LogicalResult ConvertResultsBroadcastableShapeOp::RewriteEqOp(
    Operation* op, PatternRewriter& rewriter) const {
  auto eq_op = llvm::dyn_cast_or_null<Op>(op);
  if (eq_op && eq_op.getIncompatibleShapeError())
    return RewriteOp(op, rewriter, OpTrait::util::getBroadcastedShape);
  return failure();

    tensorflow::tpu::TPUCompileMetadataProto* metadata) {
  auto input_shardings =
      op->getAttrOfType<ArrayAttr>(tensorflow::kInputShardingAttr);
  if (!input_shardings)
    return op.emitOpError(
        CreateMissingAttributeMsg(tensorflow::kInputShardingAttr));

  if (input_shardings.size() != op.getNumOperands())
    return op.emitOpError(
        llvm::formatv(kBadArrayAttrLengthMsg, tensorflow::kInputShardingAttr,