}

// computeInitContainerActions sets the actions on the given changes that need
// to be taken for the init containers. This includes actions to initialize the
// init containers and actions to keep restartable init containers running.
// computeInitContainerActions returns true if pod has been initialized.
//
// The actions include:
// - Start the first init container that has not been started.

                if (transformerContextBuilder != null) {
                    ModelTransformerContext context = transformerContextBuilder.initialize(request, problems);
                    rawModel = this.transformer.transform(context, rawModel, pomFile);
                }
            } catch (ModelTransformerException e) {

    protected volatile boolean isBind = false;

    protected FessConfig fessConfig;

    @PostConstruct
    public void init() {
        if (logger.isDebugEnabled()) {
            logger.debug("Initialize {}", this.getClass().getSimpleName());
        }
        fessConfig = ComponentUtil.getFessConfig();
    }

	// "-unusedresult",
}

func runTest(ctx context.Context, cmd *base.Command, args []string) {
	pkgArgs, testArgs = testFlags(args)
	modload.InitWorkfile() // The test command does custom flag processing; initialize workspaces after that.

	if cfg.DebugTrace != "" {
		var close func() error
		var err error
		ctx, close, err = trace.Start(ctx, cfg.DebugTrace)
		if err != nil {

            try {
                if (request.getTransformerContextBuilder() != null) {
                    TransformerContext context =
                            request.getTransformerContextBuilder().initialize(request, problems);
                    transformer.transform(pomFile.toPath(), context, rawModel);
                }
            } catch (TransformerException e) {
                problems.add(

  if (rewrite_subgraph_fn) {
    std::vector<OutputTensor> arg_source_tensors(args_by_src_.size());
    for (const auto& arg : args_by_src_) {
      arg_source_tensors.at(arg.second) = arg.first;
    }
    // Initialize the input and output permutations to the identity.
    std::vector<int> input_permutation(args_by_src_.size());
    std::iota(input_permutation.begin(), input_permutation.end(), 0);

        ...

    assert(old_return->getNumOperands() == op_->getNumResults());
    auto new_return_operands = llvm::to_vector<4>(old_return->getOperands());
    new_return_operands.resize(num_new_results_);

    // initialize return values for written resources to be the hoisted reads.
    for (Value resource : written_resources_) {
      const ResourceInfo& info = resources_[resource];

	proxier.syncRunner.Loop(wait.NeverStop)
}

func (proxier *Proxier) setInitialized(value bool) {
	var initialized int32
	if value {
		initialized = 1
	}
	atomic.StoreInt32(&proxier.initialized, initialized)
}

func (proxier *Proxier) isInitialized() bool {
	return atomic.LoadInt32(&proxier.initialized) > 0
}

// OnServiceAdd is called whenever creation of new service object is observed.

  // of list elements to initialize the output tensor list with.
  virtual Value GetNumElements(OpT op, ValueRange operands,
                               PatternRewriter *rewriter) const = 0;

  // Rewrites the original op into `tf.fill`. The result tensor shape is
  // [num_element, element_shape]. All the values in the result tensor will be
  // initialized to 0.
  LogicalResult matchAndRewrite(