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

    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "rets_0"
  op: "_Retval"
  input: "identity"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "rets_1"
  op: "_Retval"
  input: "statefulpartitionedcall"
  attr {
    key: "T"
    value {

    TF_RETURN_IF_ERROR(status);
    graph->AddEdge(node, 0, main_node, i);
  }

  // Similarly with return values, create dummy _Retval nodes fed by `node`.
  for (int64_t i = 0, end = result_types.size(); i < end; ++i) {
    Node* node;
    string retval_name = absl::StrCat("_retval", i);
    Status status = NodeBuilder(retval_name, FunctionLibraryDefinition::kRetOp)
                        .Input(main_node, i)

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
 Conv2DBackpropInput 52
 Equal 1

    }
  }
  experimental_debug_info {
  }
}
node {
  name: "main"
  op: "_Retval"
  input: "StatefulIf"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "main1"
  op: "_Retval"
  input: "StatelessIf"
  attr {
    key: "T"
    value {
      type: DT_FLOAT

// CHECK-NEXT:   name: "Add{{_.*_1}}"
// CHECK-NEXT:   op: "Add"
// CHECK-NEXT:   input: "input0"
// CHECK-NEXT:   input: "input1"
// CHECK:      node {
// CHECK-NEXT:   name: "Add"
// CHECK-NEXT:   op: "_Retval"
// CHECK-NEXT:   input: "Add{{_.*_1}}"
// CHECK-NEXT:   attr {
// CHECK-NEXT:     key: "T"
// CHECK-NEXT:     value {
// CHECK-NEXT:       type: DT_INT32
// CHECK-NEXT:     }
// CHECK-NEXT:   }

// CHECK-NEXT: op: "Identity"
// CHECK-NEXT: input: "bar"
// CHECK: name: "[[BAR_ID_1:.*]]"
// CHECK-NEXT: op: "Identity"
// CHECK-NEXT: input: "[[BAR_ID_0]]"
// CHECK: name: "Add"
// CHECK-NEXT: op: "_Retval"

      %control_23 = "tf_executor.island"() ({
        "tf._Retval"(%output) {T = f32, device = "/job:localhost/replica:0/task:0/device:CPU:0", index = 0 : i64} : (tensor<*xf32>) -> ()
        "tf_executor.yield"() : () -> ()
      }) : () -> !tf_executor.control

          dim {
            size: 1
          }
        }
      }
    }
  }
}
# Return value to ensure not pruned if we want to run passes post.
node {
  name: "rets_0"
  op: "_Retval"
  input: "RaggedToTensor"
  attr {
    key: "T"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}

// CHECK-NEXT:     value {
// CHECK-NEXT:       list {
// CHECK-NEXT:         shape {
// CHECK-NEXT:           dim {
// CHECK-NEXT:             size: 10
// CHECK:        name: "output0"
// CHECK-NEXT:   op: "_Retval"
// CHECK-NEXT:   input: "input0"
// CHECK-NEXT:   attr {
// CHECK-NEXT:     key: "T"
// CHECK-NEXT:     value {
// CHECK-NEXT:       type: DT_INT32
// CHECK-NEXT:     }
// CHECK-NEXT:   }
// CHECK-NEXT:   attr {