return %arg2 : tensor<?xi32>
  }

  // CHECK-LABEL: func @main
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {
    // CHECK:      %[[RESULT:.*]] = "tf.XlaCallModule"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME:   Sout = [#tf_type.shape<?>]
    // CHECK-SAME:   dim_args_spec = []
    // CHECK-NOT:    _entry_function

    // CHECK:     return %[[RESULT]]
    func.return %0 : tensor<10xi32>
  }

  // CHECK-LABEL: func @foo
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @foo(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {

    // CHECK: return %[[RESULT]]
    func.return %0 : tensor<10xi32>
  }

  // CHECK-LABEL: func private @_tf_func
  func.func private @_tf_func(%arg0: tensor<?xi32>, %arg1: tensor<*xi32>) {
    // CHECK: tf.StreamResults

    // StreamResults is a pseudo op in this test.

module {
func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> attributes {tf.entry_function = {inputs = "arg0", outputs = "tfl.custom1"}} {
  %0 = "tfl.custom"(%arg0, %arg0) {custom_code = "stablehlo.subtract", custom_option = #tfl<const_bytes : "0x00000100002401">} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}
}

// CHECK:       module {

//RUN: tf_tfl_translate --enable-stablehlo-conversion --input-mlir %s -o /tmp/temp.stablehlo; [ -f /tmp/temp.stablehlo ]


module {
func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> {
  %0 = "tf.Div"(%arg0, %arg0) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}

func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32>
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>
}

// Inline a function that contains only tfl ops.
func.func @func_with_tfl_ops(%arg0 : tensor<2xi32>) -> tensor<2xi32> {
  %0 = "tfl.sub"(%arg0, %arg0) {fused_activation_function = "RELU6"} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  %1 = "tfl.add"(%0, %arg0) {fused_activation_function = "RELU6"} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %1: tensor<2xi32>
}

// CHECK-LABEL: func @inline_with_arg

        %5:2 = "tf.C"(%4) : (tensor<2xi32>) -> (tensor<2xi32>, tensor<2xi32>)
        tf_device.return %5#0, %5#1 : tensor<2xi32>, tensor<2xi32>
      }) {num_cores_per_replica = 1, topology =  "", device_assignment =  []} : () -> (tensor<2xi32>, tensor<2xi32>)
      tf_device.return %2#1 : tensor<2xi32>
    }
    func.return %1 : tensor<2xi32>
  }

func.func private @simple_callee() -> tensor<2xi32>  {
  %cst = "tf.Const"() { value = dense<2> : tensor<2xi32> } : () -> tensor<2xi32>
  func.return %cst : tensor<2xi32>
}

func.func private @simple_callee_with_noinline() -> tensor<2xi32> attributes {tf._noinline = true} {
  %cst = "tf.Const"() { value = dense<2> : tensor<2xi32> } : () -> tensor<2xi32>
  func.return %cst : tensor<2xi32>
}

// Test that simple TF operations can be inlined.

  // CHECK-DAG: %[[INDICES_3:.*]] = "tf.Mul"(%[[INDICES_2]], %arg0) : (tensor<5xi32>, tensor<5xi32>) -> tensor<5xi32>
  // CHECK-DAG: %[[INDICES_4:.*]] = "tf.TensorScatterAdd"(%[[cst_3]], %0, %[[UPDATES]]) : (tensor<5xi32>, tensor<5x1xi32>, tensor<5xi32>) -> tensor<5xi32>
  // CHECK-DAG: %[[INDICES_5:.*]] = "tf.AddV2"(%[[INDICES_3]], %[[INDICES_4]]) : (tensor<5xi32>, tensor<5xi32>) -> tensor<5xi32>