func.func @init1() attributes {tf_saved_model.exported_names = ["init1"]} {
  %0 = "tf.Const"() {value = dense<42> : tensor<1xi64>} : () -> tensor<1xi64>
  return
}
func.func @init2() attributes {tf_saved_model.exported_names = ["init2"]} {
  %0 = "tf.Const"() {value = dense<43> : tensor<1xi64>} : () -> tensor<1xi64>
  return
}
func.func @init3() attributes {tf_saved_model.exported_names = ["init3"]} {

    func.return %1 : tensor<100x50xf32>
  }

  func.func @Init() attributes {tf_saved_model.exported_names = ["Init"], tf_saved_model.initializer_type = "restore_op"} {
    func.return
  }

  // CHECK-LABEL: func @Init
  // CHECK-SAME: tf_saved_model.exported_names = ["Init"]
  // CHECK-SAME: tf_saved_model.initializer_type = "restore_op"
  // CHECK: %[[VAR:.*]] = "tf.VarHandleOp"()

  "tf_saved_model.global_tensor"() {
    tf_saved_model.exported_names = ["some_const"],
    sym_name = "some_constant",
    type = tensor<1x64xf32>,
    value = dense<42.0> : tensor<1x64xf32>
  } : () -> ()

  // Representation for variables: mutable global tensor.
  // CHECK: tf_saved_model.global_tensor
  "tf_saved_model.global_tensor"() {
    is_mutable,
    tf_saved_model.exported_names = ["some_var", "some.other.name"],

// CHECK-SAME: tf_saved_model.exported_names = ["add"]

// CHECK: func @sub(
// CHECK-SAME: {tf_saved_model.index_path = ["input2"]}
// CHECK-SAME: {tf_saved_model.index_path = ["input1"]}
// CHECK-SAME: {tf_saved_model.index_path = ["result"]}
// CHECK-SAME: tf.entry_function = {inputs = "input2:0,input1:0", outputs = "result:0"}
// CHECK-SAME: tf_saved_model.exported_names = ["sub"]

  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", tf_saved_model.exported_names = ["v"], type = tensor<f32>, value = dense<42.> : tensor<f32> } : () -> ()

  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v}) -> (tensor<f32> {tf_saved_model.index_path = []})
  attributes {tf_saved_model.exported_names = ["f"]} {

  # Check index paths for results.
  #
  # CHECK:      func {{@[a-zA-Z_0-9]+}}() -> (
  # CHECK-SAME:   tensor<1xf32> {tf_saved_model.index_path = []})
  # CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["f0000_single_return"]
  @tf.function(input_signature=[])
  def f0000_single_return(self):
    return tf.constant(1.0, shape=[1])

  # Check index paths for results with multiple return values.

  func.func @read(%arg0: tensor<!tf_type.resource<tensor<10xf32>>> {tf_saved_model.bound_input = @v})
  -> (tensor<10xf32> {tf_saved_model.index_path = []})
  attributes {tf_saved_model.exported_names = ["read"]} {
    // CHECK: %[[v:.*]] = ml_program.global_load @vars.v : tensor<10xf32>
    // CHECK: %[[bar:.*]] = ml_program.global_load @vars.bar : tensor<1xf32>
    // CHECK: %[[sum:.*]] = "tf.Add"(%[[v]], %[[bar]])

  // CHECK: func @f()
  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v})
  attributes {tf_saved_model.exported_names = ["f"]} {
    %val = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
    // CHECK: "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}>
    func.return
  }
}

// -----

// CHECK: "tf_saved_model.session_initializer"()
// CHECK-SAME: {{.*initializers = \[@init_func_restore_op\].*}}

  func.func @init_func_restore_op() -> () attributes {
      tf_saved_model.exported_names = ["tf_saved_model.session_initializer_restore_op"],
      tf_saved_model.initializer_type = "restore_op"} {
    %cst_0 = "tf.Const"() {value = dense<1.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>

  // CHECK: return [[tuple]]#0, [[tuple]]#1
  // CHECK-SAME: tensor<i8>
  // CHECK-SAME: tensor<i16>
  // CHECK: func @b
  // CHECK: func @c
  // CHECK: func private @f
  // CHECK-NOT: exported_names
  // CHECK: tf.Const
  // CHECK: tf.Const
  // CHECK: return
  func.func @f(%a: i32         {tf_saved_model.index_path = [0]},
               %b: f32         {tf_saved_model.index_path = [0]},