}
            ],
            "description": "default is a default value for undefined object fields. Defaulting is a beta feature under the CustomResourceDefaulting feature gate. Defaulting requires spec.preserveUnknownFields to be false."
          },
          "definitions": {
            "additionalProperties": {
              "allOf": [
                {

	options             Options
	optsFunc            func(*Options)
	signingKey          *jose.JSONWebKey
	pubKeys             []*jose.JSONWebKey
	claims              string
	want                *user.DefaultInfo
	wantSkip            bool
	wantErr             string
	wantInitErr         string
	wantHealthErrPrefix string
	claimToResponseMap  map[string]string
	openIDConfig        string
	fetchKeysFromRemote bool

* AWS: Handle kube-down case where the LaunchConfig is dangling ([#30816](https://github.com/kubernetes/kubernetes/pull/30816), [@zmerlynn](https://github.com/zmerlynn))
* kubectl will no longer do client-side defaulting on create and replace. ([#30250](https://github.com/kubernetes/kubernetes/pull/30250), [@krousey](https://github.com/krousey))

			}
		}
		if len(tc.metricsTarget) > 0 {
			obj.Items[0].Spec.Metrics = append(obj.Items[0].Spec.Metrics, tc.metricsTarget...)
		}

		if len(obj.Items[0].Spec.Metrics) == 0 {
			// manually add in the defaulting logic
			obj.Items[0].Spec.Metrics = []autoscalingv2.MetricSpec{
				{
					Type: autoscalingv2.ResourceMetricSourceType,
					Resource: &autoscalingv2.ResourceMetricSource{
						Name: v1.ResourceCPU,
					},

		t, il.Exprs.goString(indent+2, "Exprs: "))
}

// DefaultArg holds a default argument for a local name.
type DefaultArg struct {
	Num int
	Arg AST
}

func (da *DefaultArg) print(ps *printState) {
	if !ps.llvmStyle {
		fmt.Fprintf(&ps.buf, "{default arg#%d}::", da.Num+1)
	}
	ps.print(da.Arg)
}

func (da *DefaultArg) Traverse(fn func(AST) bool) {
	if fn(da) {
		da.Arg.Traverse(fn)

        activation_fn,
        strides,
        dilations,
    )
    saved_model_save.save(model, self._input_saved_model_path)

    # Generate model input data.
    rng = np.random.default_rng(seed=1234)
    static_input_shape = [dim if dim is not None else 2 for dim in input_shape]
    input_data = ops.convert_to_tensor(
        rng.uniform(low=0.0, high=1.0, size=static_input_shape).astype(