// CHECK-NEXT: }

      tf_executor.fetch %outputs_10#0 : tensor<?x2xi64>
    }
    func.return %0#0 : tensor<?x2xi64>
    // CHECK:      name: "ParseExample/ParseExampleV2"
    // CHECK-NEXT: op: "_Retval"
    // CHECK-NEXT: input: "ParseExample"

  }

      count: 4
    }
  }
  unclustered_op_histogram {
    op: "NoOp"
    count: 2
  }
  unclustered_op_histogram {
    op: "_Arg"
    count: 1
  }
  unclustered_op_histogram {
    op: "_Retval"
    count: 1
  }
}
)",
      &expected_auto_clustering_activity);
  EXPECT_EQ(listener()->auto_clustering_activity().DebugString(),
            expected_auto_clustering_activity.DebugString());

  auto a = ops::_Arg(scope.WithOpName("A"), DT_INT32, 0);
  auto b = ops::_Arg(scope.WithOpName("B"), DT_INT32, 1);
  auto c = ops::Add(scope.WithOpName("C"), a, b);
  auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
  TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
  return graph;
}

StatusOr<FunctionDef> SampleFuntionAddXY(const std::string& name) {
  TF_ASSIGN_OR_RETURN(auto graph, SampleGraphAddXY());

      StrEq("input_tensor:0"));

  // Match the `_Retval` node that corresponds to the return value of @main.
  const auto retval_node_itr =
      llvm::find_if(exported_model->graph_def().node(),
                    [](const NodeDef& node) { return node.op() == "_Retval"; });
  ASSERT_NE(retval_node_itr, exported_model->graph_def().node().end());

// following steps:
//
// 1. Add a XLA computation key placeholder node (it will be used as input for
//    XlaRecvAtHost and XlaSendFromHost);
// 2. Replace all _Arg nodes with one single XlaRecvAtHost node;
// 3. Replace all _Retval nodes with one single XlaSendFromHost node;
// 4. Mark all nodes except key placeholder with attr `xla_cluster_attr_name`
//    and `outside_compilation_attr_name`;

std::string GetComposeFuncName(StringRef tf_op_name) {
  std::string compose_func_name;
  for (int i = 0; i < tf_op_name.size(); ++i) {
    if (tf_op_name[i] == '_') {
      // The field name must not contain "_"s. "_Arg" and "_RetVal" are special
      // op names and we can return empty string to skip the decomposition.
      return {};
    }
    if (tf_op_name[i] == '.') {
      compose_func_name.push_back('_');

    return false;
  }

  // _Arg nodes in a top-level function represent feeds and _Retval nodes in a
  // top-level function represent fetches.
  if (stack_depth == 1 &&
      (node.type_string() == "_Arg" || node.type_string() == "_Retval")) {
    absl::string_view uncompilable_reason = "top level _Arg or _Retval";
    MaybeMarkUncompilableNode(uncompilable_reason, *stack_trace,

    if (mul) {
      auto c = ops::Mul(scope.WithOpName("C"), a, b);
      auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
      TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
    } else {
      auto c = ops::Add(scope.WithOpName("C"), a, b);
      auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
      TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
    }

 Sum 2
 Switch 247
 TensorArrayGatherV3 8
 TensorArrayGradV3 17
 TensorArrayReadV3 9
 TensorArrayScatterV3 8
 TensorArraySizeV3 4
 TensorArrayV3 8
 TensorArrayWriteV3 9
 Unique 2
 VariableV2 164
 _Retval 5
cluster 0 size 440
 Abs 40
 AddN 1
 Any 41
 Cast 40
 ConcatV2 2
 Const 95
 ExpandDims 2
 IsInf 1
 IsNan 40
 L2Loss 40
 LogicalOr 1
 Max 41
 Minimum 1
 Mul 82
 Pack 3

  auto a = ops::_Arg(scope.WithOpName("A"), DT_INT32, 0);
  auto b = ops::_Arg(scope.WithOpName("B"), DT_INT32, 1);
  auto c = ops::Add(scope.WithOpName("C"), a, b);
  auto d = ops::_Retval(scope.WithOpName("D"), c, 0);
  TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));
  return graph;
}

absl::StatusOr<FunctionDef> SampleFuntionAddXY(const std::string& name) {