node [fontname = "Sans"];
   edge [fontname = "Sans"];

   "apiElements" [shape="box", label=<<B>apiElements</B>(C)>, color="pink", bgcolor="pink", style="filled"]
   "apiElements" -> "api"
   "apiElements" -> "compileOnlyApi"

   "api" [shape="box", label=<<B>api</B>>, color="chartreuse3", bgcolor="chartreuse3", style="filled"]
   "compileOnlyApi" [shape="box", label=<<B>compileOnlyApi</B>>, color="chartreuse3", bgcolor="chartreuse3", style="filled"]

// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 0 ],
// CHECK-NEXT:       name: "input_to_input_intermediate",
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 0 ],
// CHECK-NEXT:       name: "input_to_forget_intermediate",
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 0 ],
// CHECK-NEXT:       name: "input_to_cell_intermediate",

      %arg4: tensor<!tf_type.resource<tensor<128xf32>>>
    ) -> (tensor<i64>, tensor<i32>) {
    // CHECK:      %[[ANON_ITER:[0-9]*]] = "tf.AnonymousIteratorV3"
    // CHECK-SAME: output_shapes = [#tf_type.shape<32>, #tf_type.shape<64>]
    // CHECK-SAME: output_types = [f32, f64]
    // CHECK-NEXT: "tf.MakeIterator"(%[[ARG_0]], %[[ANON_ITER]])
    // CHECK-NEXT: %[[COND:.*]] = "tf.Const"

      return;
    }
    if (!TensorShape{e.tensor.shape()}.IsSameSize(
            TensorShape{first.tensor.shape()})) {
      status = errors::InvalidArgument(
          "Initializer list components should all have the same shape");
      return;
    }
  }

  // Form the new shape.
  TensorShape shape{static_cast<int64_t>(v.size())};

# of the feed.
#
# CHECK-LABEL: func @main
# CHECK-SAME:  (%[[ARG_0:.*]]: tensor<f32>, %[[ARG_1:.*]]: tensor<f32>) -> (tensor<f32>, tensor<f32>, tensor<*xf32>)
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "z:1,z:2"
# CHECK-SAME:  outputs = "z:2,z:1,a:0"
# CHECK:           %{{.*}}, %[[ASSIGN_ADD_CTRL:.*]] = tf_executor.island wraps "tf.AssignAdd"

  // CHECK-SAME: ([[serialized]]
  // CHECK-NOT: device
  // CHECK-SAME: Tdense = [f32, f32]
  // CHECK-SAME: dense_shapes = [#corert.shape<>, #corert.shape<>]
  // CHECK-SAME: num_sparse = 2 : i64
  // CHECK-SAME: ragged_split_types = []
  // CHECK-SAME: ragged_value_types = []
  // CHECK-SAME: sparse_types = [!corert.string, i64]

# Verify that we match correctly the input / output when they are scalar.

# CHECK-LABEL: func @main
# CHECK-SAME:  (%{{[a-z0-9]+}}: tensor<f32> {tf.device = "/device:CPU:0"})
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "out:1,out"

    // CHECK-SAME:   platforms = []
    // CHECK-SAME:   version = 5
    // CHECK-SAME:   _entry_function = @main_0
    // CHECK-SAME:   _stablehlo_module_attrs = {}

    %0 = "tf.XlaCallModule"(%arg0, %arg1) {Sout = [#tf_type.shape<?>], dim_args_spec = [], _entry_function = @main_0, module = "", platforms = [], version = 5 : i64} : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32>
    // CHECK: return %[[RESULT]]

      f._tf_data_function = true,
      output_shapes = [#tf_type.shape<64>, #tf_type.shape<69>, #tf_type.shape<73>, #tf_type.shape<12>, #tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<>, #tf_type.shape<64>, #tf_type.shape<64>, #tf_type.shape<>, #tf_type.shape<>],
      output_types = [i32, i32, i32, i64, i32, i32, f32, i64, i32, i32, i32, f32],
      preserve_cardinality = false,

  # CHECK-SAME:   tensor<1xf32> {tf_saved_model.index_path = [0]},
  # CHECK-SAME:   tensor<2xf32> {tf_saved_model.index_path = [1]})
  # CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["f0001_multiple_results_no_punctuation"]
  @tf.function(input_signature=[])
  def f0001_multiple_results_no_punctuation(self):
    return tf.constant(1.0, shape=[1]), tf.constant(1.0, shape=[2])