)

type Configer interface {
	Config(t *testing.T, variant string) string
}

type vsArgs struct {
	Namespace string
	Match     string
	Matches   []string
	GwMatches []types.NamespacedName
	Dest      string
	Port      int
	PortMatch int
	Time      string
}

func (args vsArgs) Config(t *testing.T, variant string) string {
	if args.Time == "" {
		args.Time = TimeBase

        group2libv11.artifactFile.text = "lib"
        def group2lib2v10 = mavenRepo.module("group2", "lib2", "1.0").adhocVariants().variant('runtime', [color: 'blue']).withModuleMetadata().publish()
        group2lib2v10.artifactFile.text = "lib2"

        Returns:
          A map of: output key -> output result.
        """
        scale = [1.0] * self.out_channel_size
        offset = [0.5] * self.out_channel_size
        mean, variance = scale, offset
        out = nn_ops.depthwise_conv2d_native(
            input_tensor,
            self.filters,
            strides=[1, 2, 2, 1],
            dilations=[1, 1, 1, 1],

		{name: "LoweredAtomicExchange32", argLength: 3, reg: gpxchg, resultNotInArgs: true, faultOnNilArg0: true, hasSideEffects: true, unsafePoint: true},

		// atomic exchange variant.
		// store arg1 to arg0. arg2=mem. returns <old content of *arg0, memory>. auxint must be zero.
		// SWPALD	Rarg1, (Rarg0), Rout

                                PatternRewriter &rewriter) const override {
    auto addn_op = cast<AddNOp>(op);

    // TODO(hinsu): Support variant with TensorList type. tf.AddV2 doesn't
    // support variant type so variant types require special handling.
    if (mlir::isa<VariantType>(getElementTypeOrSelf(addn_op.getType())))
      return failure();

	Scope int

	// Addr is the type of address used to manipulate a socket. Addr must be
	// one of:
	//  - *TIPCSocketAddr: "id" variant in the C addr union
	//  - *TIPCServiceRange: "nameseq" variant in the C addr union
	//  - *TIPCServiceName: "name" variant in the C addr union
	//
	// If nil, EINVAL will be returned when the structure is used.
	Addr TIPCAddr

	raw RawSockaddrTIPC
}

            std::vector<int>* output_permutation, NodeDef* node) {
        // Optimize the subgraph.
        // Do not constant fold nodes that output DT_VARIANT type tensors.
        // XLA does not support Const nodes of Variant type since it needs
        // to know the original ops to be able to compile them to the relevant
        // XLA form.
        // TODO(srbs): This filter is a little conservative. E.g. a subgraph of
        // the form:

// Adds operations to compute the partial derivatives of sum of `y`s w.r.t `x`s,
// i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...
// This is a variant of TF_AddGradients that allows to caller to pass a custom
// name prefix to the operations added to a graph to compute the gradients.
//
// `dx` are used as initial gradients (which represent the symbolic partial

//
// `X` may contain symbolic predicates and the operations corresponding to these
// symbolic predicates are either in frame `loop` or outside it.  The symbols
// that are inside frame `loop` are loop variant (i.e. can have different
// liveness in each loop iteration) and the symbols that are outside frame
// `loop` are loop invariant (i.e. have the same liveness across all
// iterations).

     *
     * @return true if a deprecation was emitted
     */
    private boolean maybePreventMutation(MutationType type, String typeDescription) {
        // If an external party has seen the public state (variant metadata) of our configuration,
        // we forbid any mutation that mutates the public state. The resolution strategy does
        // not mutate the public state of the configuration, so we allow it.