}

// AddImageMetaFlags adds the --image-repository flag to the given flagset
func AddImageMetaFlags(fs *pflag.FlagSet, imageRepository *string) {
	fs.StringVar(imageRepository, ImageRepository, *imageRepository, "Choose a container registry to pull control plane images from")
}

// AddFeatureGatesStringFlag adds the --feature-gates flag to the given flagset
func AddFeatureGatesStringFlag(fs *pflag.FlagSet, featureGatesString *string) {

[[reserved_alias_name]]
== Use of a reserved alias name

This error indicates that you chose an alias which is a reserved name.
Typically this happens if you choose an alias which starts with `versions`, `bundles`, or `plugins` or contains `extensions`, `convention`, `class`, as it may clash with generated accessors.

To fix this problem, you must choose a different alias.

[[undefined_version_reference]]
== Undefined version reference

    registry.insert<TF::TensorFlowDialect>();
  }

  void runOnOperation() override;

 private:
  Option<OpSet> target_opset_{
      *this, "target-opset", llvm::cl::init(OpSet::TF),
      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),

        } else {
            formatter.node("The consumer was configured to find " + describer.describeAttributeSet(failure.getRequestedAttributes().asMap()) + ". However we cannot choose between the following variants of " + failure.getRequestedName());
        }
        formatter.startChildren();
        for (AssessedCandidate assessedCandidate : failure.getCandidates()) {

          <!-- We add the link ourselves, both so that we can choose Java 9 over the version that -source suggests and so that we can solve the JSR305 problem described below. -->
          <detectJavaApiLink>false</detectJavaApiLink>
          <offlineLinks>

  Option<QuantMethod> quantization_method_{
      *this, "quantization-method",
      llvm::cl::init(tensorflow::quantization::QuantizationMethod::
                         METHOD_STATIC_RANGE_INT8),
      llvm::cl::desc("Choose quantization method."),
      llvm::cl::values(
          clEnumValN(tensorflow::quantization::QuantizationMethod::
                         METHOD_STATIC_RANGE_INT8,

          <!-- We add the link ourselves, both so that we can choose Java 9 over the version that -source suggests and so that we can solve the JSR305 problem described below. -->
          <detectJavaApiLink>false</detectJavaApiLink>
          <offlineLinks>

}

// Dispatcher dispatches evaluates an admission request against the currently
// active hooks returned by the source.
type Dispatcher[H Hook] interface {
	// Dispatch a request to the policies. Dispatcher may choose not to
	// call a hook, either because the rules of the hook does not match, or
	// the namespaceSelector or the objectSelector of the hook does not
	// match. A non-nil error means the request is rejected.

    private static final NON_ASCII_NAME = [
        "ascii-only": "ascii",
        "space": " ",
        "zwnj": "\u200c",
        "chinese": "敏捷的棕色狐狸跳过了懒狗",
        "cyrillic": "здравствуйте",
        "hungarian": "Árvíztűrő tükörfúrógép",
    ].values().join("-")

    private static final DEFAULT_ENCODINGS = [
        "UTF-8",
        "ISO-8859-1",

      auto entrypoint = entrypoints.begin()->second;
      Builder builder(entrypoint);
      entrypoint.setName(builder.getStringAttr("main"));
      return;
    }

    // In case we have more than 1 entry points, choose the one with
    // 'tf.entry_function' attribute set.
    llvm::SmallVector<func::FuncOp, 4> candidate_funcs;
    for (auto& entrypoint : entrypoints) {
      if (entrypoint.second->hasAttr("tf.entry_function")) {