attributes {tf.entry_function = {inputs = "input0,input1", outputs = "Add"}} {
  %graph = tf_executor.graph {
    %2:2 = tf_executor.island wraps "tf.Add"(%arg0, %arg1) {T = "tfdtype$DT_INT32", device = ""} : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32> loc("Add")
    tf_executor.fetch %2 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK:      node {

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main() {
  tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", name = "Constant", value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %outputs_0, %control_1 = tf_executor.island wraps "tf.LegacyCall"(%outputs) {_tpu_replicate = "cluster", device = "", f = @foo0} : (tensor<i32>) -> tensor<i32>
    tf_executor.fetch
  }

// CHECK-NEXT:         }
// CHECK-NEXT:       }
// CHECK:          }
// CHECK-NEXT:   }
func.func @bar() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", name = "tf.Const", value = dense<1> : tensor<i32>} : () -> tensor<i32>
    %1:2 = tf_executor.island wraps "tf.Empty"(%0#0) {dtype = "tfdtype$DT_FLOAT", name = "tf.Empty"} : (tensor<i32>) -> tensor<*xf32>
    tf_executor.fetch

  attributes  {tf.entry_function = {inputs = "args_0,args_1", outputs = "rets_0,rets_1"}} {
    %0:2 = tf_executor.graph {
      %1:3 = tf_executor.island wraps "tf.IdentityN"(%arg0, %arg1) {T = ["tfdtype$DT_FLOAT", "tfdtype$DT_INT32"], device = "", name = "identity"} : (tensor<*xf32>, tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<2x4x6x8xi32>)
      tf_executor.fetch %1#0, %1#1 : tensor<*xf32>, tensor<2x4x6x8xi32>
    }

// CHECK-NEXT:         tensor_shape {
// CHECK-NEXT:         }
// CHECK-NEXT:       }
// CHECK-NEXT:     }
// CHECK-NEXT:   }
// CHECK:      }
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32> loc("predicate")

// CHECK:      node {
// CHECK-NEXT:   name: "Case"
// CHECK-NEXT:   op: "Case"
// CHECK-NEXT:   input: "predicate"
// CHECK:        attr {

  // ALWAYS-DECOMPOSE-NOT:  "tf.AssignAddVariableOp"
  "tf.AssignAddVariableOp"(%0, %1) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2x8xi32>>>, tensor<i32>) -> ()
  // CHECK: tf_device.cluster
  "tf_device.cluster"() ({
    // CHECK-NOT: "tf.AssignAddVariableOp"
    "tf.AssignAddVariableOp"(%0, %1) {dtype = "tfdtype$DT_INT32"} : (tensor<*x!tf_type.resource<tensor<2x8xi32>>>, tensor<i32>) -> ()
    tf_device.return
  }) : () -> ()

  %0 = "tf.Shape"(%arg0) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<f32>) -> tensor<0xi32>

  // Result shape need not be static. Folding harness uses TensorFlow constant
  // in that case.
  // CHECK-DAG: "tf.Const"() <{value = dense<[1, 32, 32, 16]> : tensor<4xi32>}> : () -> tensor<?xi32>
  %1 = "tf.Shape"(%arg1) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<1x32x32x16xf32>) -> tensor<?xi32>

^bb0(%arg0: tensor<1x32x32x16xf32>, %arg1: tensor<*xf32>):
  %0 = "tf.Shape"(%arg0) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<1x32x32x16xf32>) -> tensor<4xi32>
  %1 = "tf.Shape"(%arg1) {T = "tfdtype$DT_FLOAT", output = "tfdtype$DT_INT32"} : (tensor<*xf32>) -> tensor<?xi32>
  func.return %0, %1 : tensor<4xi32>, tensor<?xi32>
}

// -----

  %cst_1 = arith.constant dense<-1> : tensor<i32>
  %0 = "tf.TensorListFromTensor"(%arg0, %cst) : (tensor<2x3xf32>, tensor<1xi32>) -> tensor<!tf_type.variant<tensor<3xf32>>>
  %1:2 = "tf.While"(%cst_0, %0) {T = ["tfdtype$DT_INT32", "tfdtype$DT_VARIANT"], body = @tensorlistWhileBody, cond = @tensorlistWhileCond, is_stateless = false} : (tensor<i32>, tensor<!tf_type.variant<tensor<3xf32>>>) -> (tensor<i32>, tensor<!tf_type.variant<tensor<*xf32>>>)

    REGISTER_OP("Exit")
       .Input("data: T")
       .Output("output: T")
       .Attr("T: type")

    For example:
     %1:2 = tf_executor.Exit %0#0 : tensor<*xi32> {T: "tfdtype$DT_INT32"}

    Note: Additional result corresponds to the control output.
  }];

  let arguments = (ins
    AnyType:$data,
    Variadic<TfeControlType>:$controlInputs
  );

  let results = (outs