PatternRewriter& rewriter) const override {
    auto preceding_qcast =
        q_op.getArg().getDefiningOp<quantfork::QuantizeCastOp>();
    if (!preceding_qcast) return failure();

    auto new_qcast = rewriter.create<quantfork::QuantizeCastOp>(
        q_op.getLoc(), q_op.getType(), preceding_qcast.getArg());
    new_qcast->setAttr(kVolatileOpAttrName, rewriter.getUnitAttr());
    q_op->replaceAllUsesWith(new_qcast);

  // quant.qcast op in case it overrides the information from training FakeQuant
  // ops.
  func.walk([&](quantfork::QuantizeCastOp q) {
    auto input_op = q.getArg().getDefiningOp();
    if (auto stats = dyn_cast_or_null<quantfork::StatisticsOp>(input_op)) {
      q.setOperand(stats.getArg());
      if (stats.use_empty()) stats.erase();
    }
  });

  // Step 1: forward pass: propagate any value scales which are not produces

	if err != nil {
		return nil, err
	}
	if filter != nil && !filter.Contains(uid) {
		return nil, nil
	}

	netns, err := proc.Open(netnsName)
	if err != nil {
		return nil, err
	}
	fd, err := GetFd(netns)
	if err != nil {
		netns.Close()
		return nil, err
	}
	netnsObserved[inode] = struct{}{}
	log.Debugf("found pod to netns: %s %d", uid, inode)

	return &PodNetnsEntry{
		uid:     uid,

                                PatternRewriter &rewriter) const override {
    auto rng_op = cast<RngReadAndSkipOp>(op);

    DenseIntElementsAttr alg_constant;
    if (!matchPattern(rng_op.getAlg(), m_Constant(&alg_constant))) {
      return rewriter.notifyMatchFailure(
          op, "unable to determine algorithm statically");
    }

    if (alg_constant.getNumElements() != 1) {

    @InputFile
    abstract RegularFileProperty getToolingApiShadedJar()

    @Input
    abstract Property<String> getProjectVersion()

    @Inject
    protected abstract FileSystemOperations getFs()

    @OutputDirectory
    File projectDirectory

    @TaskAction
    void generateBuildshipProject() {

        File jarFile = toolingApiShadedJar.get().asFile

                @Input
                abstract Property<String> getContent() // set to empty string to delete directory

                @Inject
                abstract FileSystemOperations getFs()

                DirProducer() {
                    content.convention("content")
                }

                @TaskAction
                def go() {
                    def dir = output.get().asFile

        return failure();
      }
      output_types = {new_output_type};
    } else {
      output_types = {output_type.clone(elem_type.getStorageType())};
    }

    SmallVector<Value> args = {q_op.getArg(), scale, zero_point};
    FlatSymbolRefAttr func_name =
        FlatSymbolRefAttr::get(rewriter.getStringAttr(kQuantizeFuncName));

    auto quantize_call = rewriter.create<TF::PartitionedCallOp>(

// Called to initialize a new m (including the bootstrap m).
// Called on the new thread, can not allocate memory.
func minit() {
	getg().m.procid = uint64(getthrid())
	minitSignals()
}

// Called from dropm to undo the effect of an minit.
//
//go:nosplit
func unminit() {
	unminitSignals()
	getg().m.procid = 0
}

// Called from exitm, but not from drop, to undo the effect of thread-owned

              .getOutputDimensions()
              .getDefiningOp<shape::ShapeOfOp>();
      // Check if the shape come from the original conv op.
      if (!shape_of_op ||
          shape_of_op.getArg().getDefiningOp<mhlo::ConvolutionOp>() !=
              conv_op) {
        return failure();
      }
      Value new_shape_of = rewriter.create<shape::ShapeOfOp>(

// Called to initialize a new m (including the bootstrap m).
// Called on the new thread, cannot allocate memory.
func minit() {
	getg().m.procid = uint64(lwp_gettid())
	minitSignals()
}

// Called from dropm to undo the effect of an minit.
//
//go:nosplit
func unminit() {
	unminitSignals()
	getg().m.procid = 0
}

// Called from exitm, but not from drop, to undo the effect of thread-owned