OperationState new_state(candidate_op->getLoc(),
                               candidate_op->getName().getStringRef(), inputs,
                               output_types, candidate_op->getAttrs());
      for (int i = 0; i < candidate_op->getNumRegions(); ++i) {
        new_state.addRegion();
      }
      Operation* quantized_op = rewriter.create(new_state);
      if (candidate_op->getNumRegions() != 0) {

    }

    // Save the new state value to the resource.
    pack_args.push_back(key);
    Value new_state = rewriter.create<PackOp>(loc, res_type, pack_args);
    rewriter.create<AssignVariableOp>(loc, rng_op.getResource(), new_state);

    // Pad the original state as necessary to fill the output shape.
    int pad = tensorflow::RNG_MAX_COUNTER_SIZE - counter_size;

      OperationState new_state(quantizing_op->getLoc(),
                               quantizing_op->getName().getStringRef(), inputs,
                               output_types, quantizing_op->getAttrs());
      for (int i = 0; i < quantizing_op->getNumRegions(); ++i) {
        new_state.addRegion();
      }
      Operation* quantized_op = rewriter.create(new_state);
      if (quantizing_op->getNumRegions() != 0) {

      OperationState new_state(quantizing_op->getLoc(),
                               quantizing_op->getName().getStringRef(), inputs,
                               output_types, quantizing_op->getAttrs());
      for (int i = 0; i < quantizing_op->getNumRegions(); ++i) {
        new_state.addRegion();
      }
      Operation* quantized_op = rewriter.create(new_state);
      if (quantizing_op->getNumRegions() != 0) {

      OperationState new_state(op_with_region->getLoc(),
                               op_with_region->getName().getStringRef(), inputs,
                               output_types, op_with_region->getAttrs());
      for (int i = 0; i < op_with_region->getNumRegions(); ++i) {
        new_state.addRegion();
      }
      Operation* quantized_op = rewriter.create(new_state);
      for (const auto& [index, region] :

  Location loc = call_op.getLoc();
  rewriter.setInsertionPointAfter(call_op);
  std::string tf_op_name = GetTFOpName(call_op.getCallee());
  OperationState new_state(loc, tf_op_name, inputs, output_types, attr_list);
  Operation* new_op = rewriter.create(new_state);
  if (materialize_derived_attrs_) {
    for (const auto& attr : derived_attrs) {
      // Add or update the derived attribute with the value. Skip the fixed

  auto shape = mlir::cast<ShapedType>(value.getType());
  if (shape.getRank() != 1) {
    SmallVector<int64_t> new_shape;
    new_shape.push_back(shape.getNumElements());
    value = builder.create<TF::ReshapeOp>(
        loc, value, Create1DConstValue(builder, loc, new_shape));
  }
  return ConstantFoldOpIfPossible(value.getDefiningOp()).front();
}

	termios, err := unix.IoctlGetTermios(fd, ioctlReadTermios)
	if err != nil {
		return nil, err
	}

	newState := *termios
	newState.Lflag &^= unix.ECHO
	newState.Lflag |= unix.ICANON | unix.ISIG
	newState.Iflag |= unix.ICRNL
	if err := unix.IoctlSetTermios(fd, ioctlWriteTermios, &newState); err != nil {
		return nil, err
	}

	defer unix.IoctlSetTermios(fd, ioctlWriteTermios, termios)

        def state = compilationState(fileStates)

        then:
        def newState = serialized(state)
        newState.fileStates.size() == 2

        def emptyCompileState = newState.getState(fileEmpty)
        emptyCompileState.hash == TestHashCodes.hashCodeFrom(0x12345678)
        emptyCompileState.edges.empty

        def otherCompileState = newState.getState(fileTwo)

        LockState newState = lockState.completeUpdate();
        lockStateAccess.writeState(lockFileAccess, newState);
        return newState;
    }

    public LockState markDirty(LockState lockState) throws IOException {
        LockState newState = lockState.beforeUpdate();
        lockStateAccess.writeState(lockFileAccess, newState);
        return newState;
    }