"Number of quantized layers with quantized outputs: %d/%d",
        total_quantized_func_count - float_output_func_count,
        total_quantized_func_count));
    lines.push_back(absl::StrFormat("Number of quantize layers added: %d",
                                    quantize_func_count));
    lines.push_back(absl::StrFormat("Number of dequantize layers added: %d",

	}
}

// ExecObjectLayerAPINilTest - Sets the object layer to `nil`, and calls the registered object layer API endpoint,
// and assert the error response. The purpose is to validate the API handlers response when the object layer is uninitialized.

		HTTPStatusCode: http.StatusBadRequest,
	},
}

// toAPIErrorCode - Converts embedded errors. Convenience
// function written to handle all cases where we have known types of
// errors returned by underlying layers.
func toAPIErrorCode(ctx context.Context, err error) (apiErr APIErrorCode) {
	if err == nil {
		return ErrNone
	}

	// Errors that are generated by net.Conn and any context errors must be handled here.

			{nil, true, watch.Deleted},
		},
		pred: storage.SelectionPredicate{
			Label: labels.SelectorFromSet(labels.Set{"select": "true"}),
			Field: fields.Everything(),
			GetAttrs: func(obj runtime.Object) (labels.Set, fields.Set, error) {
				pod := obj.(*example.Pod)
				return labels.Set(pod.Labels), nil, nil
			},
		},
	}}
	for _, tt := range tests {
		t.Run(tt.name, func(t *testing.T) {

This pass implements automated pipelining for TPU embeddings defined using
the TF2 Embedding API. This is designed for applications that have an
embedding lookup on the SparseCore, followed by one or more dense layers on
TensorCores, optionally followed by a backward pass (training update) with
more ops on the SparseCore. Ops are broken up into:
  1. SC forward pass
  2. TC forward/backward pass
  3. SC backward pass

			markDelete = false
		}
		if opts.VersionPurgeStatus() == Complete {
			markDelete = false
		}
		// now, since VersionPurgeStatus() is already set, we can let the
		// lower layers decide this. This fixes a regression that was introduced
		// in PR #14555 where !VersionPurgeStatus.Empty() is automatically
		// considered as Delete marker true to avoid listing such objects by

			pods: []*v1.Pod{
				st.MakePod().Name("p1").Node("node1").Labels(podLabelA).Obj(),
			},
			nodes: []*v1.Node{
				{ObjectMeta: metav1.ObjectMeta{Name: "node1", Labels: labelRgChina}},
				{ObjectMeta: metav1.ObjectMeta{Name: "node2", Labels: labelRgChinaAzAz1}},
				{ObjectMeta: metav1.ObjectMeta{Name: "node3", Labels: labelRgIndia}},
			},
			wantFilterStatuses: []*framework.Status{
				nil,
				nil,

		return config.Config{
			Meta: config.Meta{
				GroupVersionKind:  gvk.AuthorizationPolicy,
				Name:              obj.Name,
				Namespace:         obj.Namespace,
				Labels:            obj.Labels,
				Annotations:       obj.Annotations,
				ResourceVersion:   obj.ResourceVersion,
				CreationTimestamp: obj.CreationTimestamp.Time,
				OwnerReferences:   obj.OwnerReferences,

	// an existing revision.
	modTemplate := ss1.Spec.Template.DeepCopy()
	modTemplate.Labels["foo"] = "not_bar"
	ss1Rev2, err := NewControllerRevision(ss1, parentKind, ss1.Spec.Template.Labels, rawTemplate(modTemplate), 2, ss1.Status.CollisionCount)
	ss1Rev2.Name = ss1Rev1.Name
	ss1Rev2.Labels[ControllerRevisionHashLabel] = ss1Rev1.Labels[ControllerRevisionHashLabel]
	if err != nil {
		t.Fatal(err)
	}

					Labels: labels,
				},
			},
		})
		makeIstioObject(t, store, config.Config{
			Meta: config.Meta{
				Name:             "workload",
				Namespace:        namespace,
				GroupVersionKind: gvk.WorkloadEntry,
				Domain:           "cluster.local",
			},
			Spec: &networking.WorkloadEntry{
				Address: "2.3.4.5",
				Labels:  labels,
				Ports: map[string]uint32{