}) {sym_name = "__inference_wrapped_function_4260250_0", sym_visibility = "private", tf._input_shapes = [#tf_type.shape<>], tf.signature.is_stateful, function_type = (tensor<i32>) -> ()} : () -> ()
  "func.func"() ({
  ^bb0(%arg0: tensor<i32>):
    "func.return"(%arg0) : (tensor<i32>) -> ()

func.func private @whitespace_tokenizer_rank1(%arg0: tensor<1x!tf_type.string> {tf._user_specified_name = "input"}) -> (tensor<?x!tf_type.string>, tensor<?xi64>) attributes {tf._input_shapes = [#tf_type.shape<1>], tf._implements = #tf_type.func<@"tftext:WhitespaceTokenizer", {}>, tf.signature.is_stateful} {
  %0 = "tf.Const"() {value = dense<[0, 1]> : tensor<2xi64>} : () -> tensor<2xi64>

func.func private @batched_function(%arg0: tensor<1x3xf32> {tf._user_specified_name = "0"}, %arg1: tensor<*x!tf_type.resource>) -> tensor<1x3xf32> attributes {tf._input_shapes = [#tf_type.shape<1x3>, #tf_type.shape<*>], tf.signature.is_stateful} {
  %0 = "tf.ReadVariableOp"(%arg1) {device = "/device:CPU:0"} : (tensor<*x!tf_type.resource>) -> tensor<1x3xf32>

tensor<?x8x8xf32>, %arg1: tensor<8x10xf32>, %arg2: tensor<8x10xf32>, %arg3: tensor<8x40xf32>, %arg4: tensor<10x40xf32>, %arg5: tensor<40xf32>) -> (tensor<8x10xf32>, tensor<?x8x10xf32>, tensor<8x10xf32>, tensor<8x10xf32>, tensor<f32>) attributes {tf._input_shapes = ["tfshape$dim { size: -1 } dim { size: 8 } dim { size: 8 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$unknown_rank: true", "tfshape$unknown_rank: false", "tfshape$unknown_rank: false"],...

  if (input_shapes_attr.size() != func_op.getNumArguments()) {
    return func_op->emitError(
               "Number of arguments and 'tf._input_shapes' "
               "attribute size do not match. ")
           << "Num args: " << func_op.getNumArguments()
           << ", tf._input_shapes size: " << input_shapes_attr.size();
  }

  return success();
}

// Validates ModuleOp. Returns `failure` if the module op is invalid.

func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} {
  %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
  %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>

  %cst = "tf.Const"() <{value = dense<1> : tensor<i32>}> {device = ""} : () -> tensor<i32>

func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} {
  %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
  %bias = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>

// Test hoist var handle op and read variable op in the batch function.

// CHECK-LABEL: func private @batched_function
func.func private @batched_function(%arg0: tensor<1x3xf32>) -> tensor<1x3xf32>
  attributes {tf._input_shapes = [#tf_type.shape<1x3>, #tf_type.shape<*>], tf.signature.is_stateful} {
  // CHECK-NOT: tf.VarHandleOp
  // CHECK-NOT: tf.ReadVariableOp
  // CHECK:  "tf._TfrtGetResource"()

  func.func private @cond_false_80(%arg0: tensor<1x4xf32> {tf._user_specified_name = "x"}) -> (tensor<i1>, tensor<1x3xf32>) attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x4>], tf._original_func_name = "cond_false_8"} {
    %cst = "tf.Const"() <{value = dense<true> : tensor<i1>}> {device = ""} : () -> tensor<i1>