[](const Scope& s, const std::vector<Output>& inputs,
         std::vector<Output>* outputs) {
        // Use AddN, rather than Add, because the gradient function doesn't
        // depend on the input shapes, and thus we do not need to store
        // intermediate values in a stack.
        outputs->push_back(ops::AddN(s, {inputs[0], 1}));
        return s.status();
      });
  CreateBackprop();

  Run<int>({1}, {1});

Clustered nodes: 2385
Unclustered nodes: 4221
Number of clusters: 30

unclustered size 4221
 Add 17
 AddN 1
 All 1
 ApplyAdam 38
 Assert 7
 Assign 47
 AssignAdd 2
 AssignSub 2
 BroadcastGradientArgs 44
 Cast 38
 ConcatV2 3
 Const 875
 ControlTrigger 5
 Enter 874
 Equal 4
 Exit 69
 ExpandDims 9
 Fill 5
 FloorMod 1
 GreaterEqual 7
 Identity 113
 IsVariableInitialized 1
 IteratorGetNext 1

unclustered size 606
 AssignAddVariableOp 1
 Const 108
 DivNoNan 1
 Identity 2
 Merge 53
 PlaceholderWithDefault 1
 ReadVariableOp 2
 Switch 1
 _Arg 435
 _Retval 2
cluster 0 size 1910
 Add 16
 AddN 71
 ArgMax 1
 AssignAddVariableOp 1
 BiasAdd 1
 BiasAddGrad 1
 Cast 115
 Const 407
 Conv2D 53
 Conv2DBackpropFilter 53
 Conv2DBackpropInput 52
 Equal 1
 FusedBatchNormGradV2 53
 FusedBatchNormV2 53

Unclustered nodes: 446
Number of clusters: 1

unclustered size 446
 AssignAddVariableOp 2
 Const 2
 DivNoNan 1
 Identity 1
 NoOp 1
 ReadVariableOp 2
 VarHandleOp 435
 _Retval 2
cluster 0 size 2178
 Add 17
 AddN 72
 ArgMax 1
 AssignAddVariableOp 1
 AssignSubVariableOp 106
 BiasAdd 1
 BiasAddGrad 1
 Cast 3
 Const 357
 Conv2D 53
 Conv2DBackpropFilter 53
 Conv2DBackpropInput 52
 DivNoNan 1
 Equal 1

  which corresponds to the input and attributes in OpDef of the TF operation.
  # TODO(fengliuai): more documents here.

  Example:
    @composite.Composite('AddN')
    def _compose_add_n(inputs, N):
      if N == 1:
        ....
  """

  # TODO(fengliuai): support input_binding and output_binding so the arguments
  # are not positional.
  def __init__(self,

          concat_dim=i, values=[reversed_left_padding, left_offset])
      from_right_padding = tf.raw_ops.Concat(
          concat_dim=i, values=[right_offset, reversed_right_padding])
    input_ = tf.raw_ops.AddN(
        inputs=[from_left_padding, core, from_right_padding])

  return input_


@tf.RegisterGradient('NewMirrorPadGrad')
def _mirror_pad_grad_grad(op, grad):
  mode = op.get_attr('mode')

TF_CAPI_EXPORT extern bool TF_IsRefInput(TF_OpKernelContext* ctx, int i,
                                         TF_Status* status);

#ifndef IS_MOBILE_PLATFORM
// Expose higher level AddN operation for Pluggable vendors to implement
// in the plugin for Variant data types. The API takes in the context and a
// callback provided by pluggable vendor to do a Binary Add operation on the

}

// -----

// CHECK-LABEL: variantAddN
func.func @variantAddN(%arg0: tensor<!tf_type.variant<tensor<*xi32>>>, %arg1: tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<*xi32>>> {
  %1 = "tf.AddN"(%arg0, %arg1) : (tensor<!tf_type.variant<tensor<*xi32>>>, tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<*xi32>>>