//   purposes and doesn't affect logic. module_name - What the input module name
//   is for debugging help.
//
// Output: Modifies the input module in place with clustered operations.
//   status - Whether the transformation to cluster the input MLIR module was
//   successful.
tensorflow::Status RunFunctionTf2xlaClusteringBridge(
    mlir::ModuleOp module, bool is_supported_by_replicated_brige,

Clustered nodes: 2178
Unclustered nodes: 446
Number of clusters: 1

unclustered size 446
 AssignAddVariableOp 2
 Const 2
 DivNoNan 1
 Identity 1
 NoOp 1
 ReadVariableOp 2
 VarHandleOp 435
 _Retval 2
cluster 0 size 2178
 Add 17
 AddN 72
 ArgMax 1
 AssignAddVariableOp 1
 AssignSubVariableOp 106
 BiasAdd 1
 BiasAddGrad 1
 Cast 3
 Const 357
 Conv2D 53
 Conv2DBackpropFilter 53
 Conv2DBackpropInput 52

Clustered nodes: 2725
Unclustered nodes: 606
Number of clusters: 2

unclustered size 606
 AssignAddVariableOp 1
 Const 108
 DivNoNan 1
 Identity 2
 Merge 53
 PlaceholderWithDefault 1
 ReadVariableOp 2
 Switch 1
 _Arg 435
 _Retval 2
cluster 0 size 1910
 Add 16
 AddN 71
 ArgMax 1
 AssignAddVariableOp 1
 BiasAdd 1
 BiasAddGrad 1
 Cast 115
 Const 407
 Conv2D 53
 Conv2DBackpropFilter 53

// ```
//
// Assuming `tf.OpA` and `tf.OpC` are clustered together, the region will be
// inserted right after `tf.OpC`. The live-out `%0`, however, is used by
// `tf.OpB`, which won't dominate the region. This function reorders all users
// of the cluster op to be placed after the cluster op itself so that SSA
// dominance is preserved after cluster op creation.
void ReorderOpResultUses(mlir::Operation* cluster);

}  // namespace mlir::TF

      TF_RET_CHECK(absl::SimpleAtoi(*maybe_cluster, &cluster));
      clustered_nodes++;
    }
    clusters[cluster][n->type_string()]++;
    cluster_size[cluster]++;
  }

  string result =
      absl::StrCat("Clustered nodes: ", clustered_nodes,
                   "\nUnclustered nodes: ", cluster_size[kNoCluster],
                   "\nNumber of clusters: ", clusters.size() - 1, "\n\n");

// file name suggests, these are graphdefs that have been dumped right before
// the mark_for_compilation pass.  There should be one
// before_mark_for_compilation_<N>.pbtxt for every TF graph that was
// auto-clustered, out of which usually only one is the "main" graph that's
// running training/inference.
//
// Copy the pbtxt for that "main" graph to tensorflow/compiler/jit/tests/

Clustered nodes: 2385
Unclustered nodes: 4221
Number of clusters: 30

unclustered size 4221
 Add 17
 AddN 1
 All 1
 ApplyAdam 38
 Assert 7
 Assign 47
 AssignAdd 2
 AssignSub 2
 BroadcastGradientArgs 44
 Cast 38
 ConcatV2 3
 Const 875
 ControlTrigger 5
 Enter 874
 Equal 4
 Exit 69
 ExpandDims 9
 Fill 5
 FloorMod 1
 GreaterEqual 7
 Identity 113
 IsVariableInitialized 1
 IteratorGetNext 1

	"istio.io/istio/pilot/pkg/features"
	"istio.io/istio/pkg/cluster"
	"istio.io/istio/pkg/config/mesh"
	"istio.io/istio/pkg/kube"
	"istio.io/istio/pkg/kube/kclient"
	filter "istio.io/istio/pkg/kube/namespace"
	"istio.io/istio/pkg/log"
)

// Cluster defines cluster struct
type Cluster struct {
	// ID of the cluster.
	ID cluster.ID
	// Client for accessing the cluster.
	Client kube.Client

	kubeConfigSha [sha256.Size]byte

}

// CHECK: func private @[[CLUSTER]]
// CHECK-SAME: () -> tensor<?xi32>
// CHECK: %[[A_OUTPUT:[0-9]*]] = "tf.A"()
// CHECK: return %[[A_OUTPUT]]

// -----

// Tests cluster attributes are copied over to cluster_func.

// CHECK-LABEL: func @cluster_attrs
func.func @cluster_attrs() -> tensor<?xi32> {
  %0 = "tf_device.cluster"() ({
    %1 = "tf.A"() : () -> tensor<?xi32>

    istio -> ztunnel
    extras[label="istio/extras.yaml"]
    istio -> extras
    tags[label="istio/tags.yaml"]
    istio -> tags
    fontsize="32"
    label = "ArgoCD Cluster";
  }

  subgraph cluster_1 {
    node [style=filled,color=white]
    subgraph cluster_istio {
        fontsize="24"
        label = "NS istio-system"
        istiod18[label="Istio 1.18.5 Control"]