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

  attributes {tf.entry_function = {control_outputs = "", inputs = "", outputs = "dense_2/Add:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %0 = "tf.VarHandleOp"() {_class = ["loc:@dense/kernel"], allowed_devices = [], container = "", device = "/job:worker/replica:0/task:1/device:CPU:0", shared_name = "var1"} : () -> tensor<!tf_type.resource<tensor<100x50xf32>>>

  SqrtGrad \
  Log1p

${generate} \
  --category=nn \
  SparseSoftmaxCrossEntropyWithLogits \
  ReluGrad \
  Relu \
  BiasAdd \
  BiasAddGrad

${generate} \
  --category=resource_variable \
  VarHandleOp \
  ReadVariableOp \
  AssignVariableOp \
  DestroyResourceOp

${generate} \
  --category=io \
  RestoreV2 \

// CHECK-NEXT: [[o_chain:%.*]], [[o:%.*]] = tfrt_fallback_async.executeop.seq([[in_chain]]) key(0) cost({{.*}}) device("/job:localhost/replica:0/task:0/device:CPU:0") "tf.VarHandleOp"()
// CHECK-NEXT: [[o_chain_0:%.*]], [[o1:%.*]] = tfrt_fallback_async.executeop.seq([[in_chain]]) key(1) cost({{.*}}) device("/job:localhost/replica:0/task:0/device:CPU:0") "tf.ReadVariableOp"([[o]]) {dtype = f32} : 1

  func.return %0 : tensor<10xf32>
}

func.func @main(%arg0: tensor<7xf32>) -> tensor<10xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "output:0"}} {
  %0 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "", shared_name = "variable"} : () -> tensor<!tf_type.resource<tensor<f32>>>
  %1 = "tf.ReadVariableOp"(%0) {device = "/device:CPU:0"} : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>

  func.return %1 : tensor<1x3xf32>
}

func.func @main(%arg0: tensor<1x3xf32>) -> tensor<1x3xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "output:0"}} {
  %0 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "", shared_name = "variable"} : () -> tensor<!tf_type.resource<tensor<1x3xf32>>>

}

// CHECK-LABEL: func @main
func.func @main(%arg0: tensor<1x3xf32>) -> tensor<*xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "batch/BatchFunction:0"}} {
  %0 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "", shared_name = "variable"} : () -> tensor<!tf_type.resource<tensor<1x3xf32>>>
  // CHECK: tfrt_fallback_async.batch_function device("/device:CPU:0") @batched_function

  func.func @main_func() -> (tensor<*xf32> {tf_saved_model.index_path = ["a"]}) attributes {tf_saved_model.exported_names = ["main_func"]} {
    %0 = tf_executor.graph {
      %outputs_0, %control_0 = tf_executor.island wraps "tf.VarHandleOp"() {container = "", shared_name = ""} : () -> tensor<!tf_type.resource<tensor<501000x128xf32>>>

  }, {
    tf_device.return %arg0 : tensor<i1>
  // CHECK:      }) : () -> (tensor<i1>, tensor<i1>)
  }) : () -> (tensor<i1>, tensor<i1>)
  // CHECK-NEXT: [[VAR:%.+]] = "tf.VarHandleOp"
  %1 = "tf.VarHandleOp"() {container = "", shape = #tf_type.shape<>, shared_name = "x"} : () -> tensor<!tf_type.resource<tensor<i1>>>
  // CHECK-NEXT: "tf.AssignVariableOp"([[VAR]], [[PARALLEL_EXECUTE]]#0)

// inlined.

func.func private @simple_callee_var() -> tensor<2xi32>  {
  %cst = "tf.Const"() { value = dense<2> : tensor<2xi32> } : () -> tensor<2xi32>
  %0 = "tf.VarHandleOp"() {container = "c", shared_name = "v"} : () -> tensor<!tf_type.resource<tensor<2xi32>>>
  "tf.AssignVariableOp"(%0, %cst) {device = ""} : (tensor<!tf_type.resource<tensor<2xi32>>>, tensor<2xi32>) -> ()