### `-tf-tpu-update-embedding-enqueue-op-inputs`

_Updates inputs to TPU embedding enqueue ops depending on whether graph is in training mode or in evaluation mode._

Updates inputs to TPU embedding enqueue ops depending on whether graph
is in training mode or in evaluation mode.
### `-tf-tpu-validate-inputs`

_Validates inputs to the TPU TF/XLA bridge_

component-model-maven.png...

// Unpacks the given flatbuffer model.
//
// This helper is useful as UnPackTo requires the input to not have any existing
// state so directly calling UnPackTo could lead to memory leaks if the model
// already had some state. Instead, the returned object from here can be used to
// overwrite existing model.
ModelT UnPackFlatBufferModel(const Model& flatbuffer_model) {
  ModelT model;
  flatbuffer_model.UnPackTo(&model);

				}
			},
		},
	}

	proxy := &model.Proxy{
		Type:         model.SidecarProxy,
		Metadata:     &model.NodeMetadata{},
		IstioVersion: &model.IstioVersion{Major: 1, Minor: 5},
	}
	push := model.NewPushContext()
	for _, test := range tests {
		t.Run(test.name, func(t *testing.T) {
			cb := NewClusterBuilder(proxy, &model.PushRequest{Push: push}, model.DisabledCache{})
			opts := &buildClusterOpts{

						}),
				},
			},
		},
	}
	push := model.NewPushContext()
	push.JwtKeyResolver = model.NewJwksResolver(
		model.JwtPubKeyEvictionDuration, model.JwtPubKeyRefreshInterval,
		model.JwtPubKeyRefreshIntervalOnFailure, 10*time.Millisecond)

	defer push.JwtKeyResolver.Close()

	push.ServiceIndex.HostnameAndNamespace[host.Name("jwt-token-issuer.mesh")] = map[string]*model.Service{}

func (configgen *ConfigGeneratorImpl) BuildListeners(node *model.Proxy,
	push *model.PushContext,
) []*listener.Listener {
	builder := NewListenerBuilder(node, push)

	switch node.Type {
	case model.SidecarProxy:
		builder = configgen.buildSidecarListeners(builder)
	case model.Waypoint:
		builder = configgen.buildWaypointListeners(builder)
	case model.Router:
		builder = configgen.buildGatewayListeners(builder)
	}

        out = math_ops.matmul(out, random_tensor_gen_fn((3, 4)))
        return {'output': out}

    model = TwoMatmulModel()
    input_shape = (1, 2)

    save.save(
        model,
        self._input_saved_model_path,
        signatures=model.matmul.get_concrete_function(
            tensor_spec.TensorSpec(
                shape=input_shape, dtype=dtypes.float32, name='input_tensor'

		pol := c.(*clientsecurityv1beta1.PeerAuthentication)
		pol.Spec.Mtls = &auth.PeerAuthentication_MutualTLS{
			Mode: auth.PeerAuthentication_MutualTLS_STRICT,
		}
	})
	assert.Equal(t,
		s.lookup(s.addrXdsName("127.0.0.1"))[0].Address.GetWorkload().AuthorizationPolicies,
		[]string{fmt.Sprintf("ns1/%s", model.GetAmbientPolicyConfigName(model.ConfigKey{
			Kind:      kind.PeerAuthentication,
			Name:      selectorPolicyName,
			Namespace: "ns1",

	"istio.io/istio/pkg/util/protomarshal"
)

func flattenInstances(il ...[]*model.ServiceInstance) []*model.ServiceInstance {
	ret := []*model.ServiceInstance{}
	for _, i := range il {
		ret = append(ret, i...)
	}
	return ret
}

func makeInstances(proxy *model.Proxy, svc *model.Service, servicePort int, targetPort int) []*model.ServiceInstance {
	ret := []*model.ServiceInstance{}
	for _, p := range svc.Ports {
		if p.Port != servicePort {

		Resolution:     model.DNSLB,
		Attributes: model.ServiceAttributes{
			ServiceRegistry: provider.Kubernetes,
			Namespace:       namespace,
			ExportTo:        sets.New(v),
		},
	}
	if ip == wildcardIPv4 {
		service.Resolution = model.Passthrough
	}

	Ports := make([]*model.Port, 0)

	for _, p := range ports {
		Ports = append(Ports, &model.Port{
			Name:     fmt.Sprintf("http-%d", p),