// CHECK-SAME: tf._input_shapes = [#tf_type.shape<0>]
  func.func private @f_callee(%arg0: tensor<0xf32>, %arg1: tensor<*x!tf_type.resource>) -> tensor<0xf32> attributes {tf._input_shapes = [#tf_type.shape<0>, #tf_type.shape<>]} {
    %0 = "tf.ReadVariableOp"(%arg1) : (tensor<*x!tf_type.resource>) -> tensor<0xf32>
    %1 = "tf.AddV2"(%arg0, %0) : (tensor<0xf32>, tensor<0xf32>) -> tensor<0xf32>
    func.return %1 : tensor<0xf32>

           metadata = ""} : (tensor<*x!tf_type.variant>) -> tensor<!tf_type.variant>
  %5 = "tf.BatchDatasetV2"(%4, %0, %1) {device = "",
           output_shapes = [#tf_type.shape<>],
           output_types = [i32],
           parallel_copy = false,
           metadata = ""} : (tensor<!tf_type.variant>, tensor<i64>, tensor<i1>) -> tensor<!tf_type.variant>
  func.return %5 : tensor<!tf_type.variant>
}

  "tf.TPUCompileSucceededAssert"(%compilation_status) {device = "/CPU:0"} : (tensor<!tf_type.string>) -> ()

    "tf.OpB"(%1) : (tensor<f32>) -> ()
  }
  func.return
}

// -----

// Check that hoisting preserves read order.

// CHECK-LABEL: func @order_preserved

        tf_device.return %compilation_status, %program#0, %program#1 : tensor<!tf_type.string>, tensor<3x!tf_type.string>, tensor<3x!tf_type.string>
      }) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : () -> (tensor<!tf_type.string>, tensor<3x!tf_type.string>, tensor<3x!tf_type.string>)
      "tf_device.launch"() ( {

// -----

func.func @testIfThen(%arg0: tensor<!tf_type.variant>) -> tensor<!tf_type.variant> {
  func.return %arg0 : tensor<!tf_type.variant>
}
func.func @testIfElse(%arg0: tensor<!tf_type.variant>) -> tensor<!tf_type.variant> {
  func.return %arg0 : tensor<!tf_type.variant>
}

// CHECK-LABEL: func @testIfCasts(%arg0: tensor<i1>, %arg1: tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>

  // CHECK: "tf.ReadVariableOp"
  %val0 = "tf.VariableV2"() {_class = ["loc:@v"], container = "", device = "", shape = #tf_type.shape<96>, shared_name = ""} : () -> tensor<96x!tf_type.f32ref>
  %val1 = "tf.Identity"(%val0) : (tensor<96x!tf_type.f32ref>) -> tensor<96xf32>
  %val2 = "tf.Identity"(%val0) : (tensor<96x!tf_type.f32ref>) -> tensor<96xf32>
  func.return
}

// -----

// Test case: No follow-up ReadVariable case.

  %0 = "tf.HashTableV2"() {container = "", device = "", key_dtype = !tf_type.string, shared_name = "x", use_node_name_sharing = false, value_dtype = i64} : () -> tensor<!tf_type.resource>
  %1 = "tf.LookupTableSizeV2"(%0) {device = ""} : (tensor<!tf_type.resource>) -> tensor<i64>
  %2 = "tf.LookupTableFindV2"(%0, %arg, %default) {device = "/CPU:0"} : (tensor<!tf_type.resource>, tensor<?x!tf_type.string>, tensor<i64>) -> tensor<*xi64>

  %output_0 = "tf.TensorArrayGatherV3"(%handle_1, %indices, %res_0#3) {device = "/job:localhost/replica:0/task:0/device:CPU:0", element_shape = #tf_type.shape<?x512>} : (tensor<2x!tf_type.resource<tensor<?x512xf32>>>,...

        tf_saved_model.exported_names = ["predict"]
    }
  {
    %0 = "tf.Const"() {dtype = !tf_type.string, value = dense<[]> : tensor<0x!tf_type.string>} : () -> tensor<0x!tf_type.string>
    func.return %0 : tensor<0x!tf_type.string>
  }