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
		}

  // 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();

    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,

			switch inst.Op {
			case FDIV:
				inst.Op = FDIVR
			case FDIVR:
				inst.Op = FDIV
			case FSUB:
				inst.Op = FSUBR
			case FSUBR:
				inst.Op = FSUB
			case FDIVP:
				inst.Op = FDIVRP
			case FDIVRP:
				inst.Op = FDIVP
			case FSUBP:
				inst.Op = FSUBRP
			case FSUBRP:
				inst.Op = FSUBP
			}
		}

	case MOVNTSD:

  if (op.getDialect() != tf_dialect) return true;
  // Assert has a legalization that later removes it so we don't want to outside
  // compile it ever for performance reasons.
  if (llvm::isa<mlir::TF::AssertOp>(op)) return true;

  if (HasStringOperand(op)) return false;
  if (HasStringResult(op)) return false;
  if (MatchesPattern(op, supported_ops)) return true;

  auto abstractOp = op.getRegisteredInfo();

// conversion, use the first input to get inputs rank. Other inputs need not be
// ranked.
// Defining op for `axis` is TensorFlow constant op in the pattern as during
// the conversion, original Concat op operands still refers to the old ops even
// if HLO constant op is introduced as an replacement for the TensorFlow
// Constant op.
def : Pat<(TF_ConcatV2Op $inputs, (ConstantLikeMatcher OneElementAttr:$axis)),

  block.walk([&](Operation *op) {
    if (op->getName().getStringRef() != "tfg.NextIteration") return;
    mlir::OpBuilder builder(op);

    llvm::SmallVector<Value, 2> new_operands;
    FilterOutBlockArgControlDep(op->getOperands().drop_front(), new_operands);

    auto source_op = builder.create<tf_executor::NextIterationSourceOp>(
        op->getLoc(), op->getOperand(0).getType());

              builder.create<ConstOp>(op->getLoc(), output_type, attribute);
        } else {
          attr_cst = builder.create<mlir::arith::ConstantOp>(
              op->getLoc(), cast<TypedAttr>(attribute));
        }
        new_operands.push_back(attr_cst);
      }
    }

    // Create the TFR call op
    auto new_op = builder.create<CallOp>(
        op->getLoc(), compose_func_type.getResults(),

  // op involves a region, the op is only quantizable when the region only
  // consists of ops whose quantization parameters can be propagated from
  // outside.
  bool IsNestedRegionQuantizable(Operation* op) const {
    for (Region& region : op->getRegions()) {
      for (Operation& op : region.getOps()) {
        if (!IsOpQuantizableInNestedRegion(op)) {
          return false;
        }
      }
    }