node {
  name: "arg0"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "arg1"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "_arg1_attr0"

    // "ret0" = "arg1"
    // "ret1" = "arg0"
    tensorflow::Scope s = tensorflow::Scope::NewRootScope();
    Output arg0 = ops::_Arg(s.WithOpName("arg0"), DT_RESOURCE, 0);
    Output arg1 = ops::_Arg(s.WithOpName("arg1"), DT_RESOURCE, 1);
    Output arg2 = ops::_Arg(s.WithOpName("arg2"), DT_INT32, 2);
    auto ret0 = ops::_Retval(s.WithOpName("ret0"), arg1, 0);
    auto ret1 = ops::_Retval(s.WithOpName("ret1"), arg0, 1);

    tf_executor.fetch %2 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK:      node {
// CHECK-NEXT:   name: "input0"
// CHECK-NEXT:   op: "_Arg"
// CHECK:      node {
// CHECK-NEXT:   name: "input1"
// CHECK-NEXT:   op: "_Arg"
// CHECK:      node {
// CHECK-NEXT:   name: "Add{{_.*_1}}"
// CHECK-NEXT:   op: "Add"
// CHECK-NEXT:   input: "input0"
// CHECK-NEXT:   input: "input1"
// CHECK:      node {

node {
  name: "args_0"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "args_1"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {

    tf_executor.fetch %4#0 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK: name: "foo"
// CHECK-NEXT: op: "_Arg"
// CHECK: name: "bar"
// CHECK-NEXT: op: "_Arg"
// CHECK: name: "[[BAR_ID_0:.*]]"
// CHECK-NEXT: op: "Identity"
// CHECK-NEXT: input: "bar"
// CHECK: name: "[[BAR_ID_1:.*]]"
// CHECK-NEXT: op: "Identity"
// CHECK-NEXT: input: "[[BAR_ID_0]]"

  auto arg0 = ops::_Arg(scope.WithOpName("a_0_arg"), DT_INT32, 0);
  auto arg1 = ops::_Arg(scope.WithOpName("b_0_arg"), DT_FLOAT, 1);
  auto arg2 = ops::_Arg(scope.WithOpName("c_0_arg"), DT_INT32, 2);
  auto arg3 = ops::_Arg(scope.WithOpName("d_0_arg"), DT_FLOAT, 3);

  auto arg4 = ops::_Arg(scope.WithOpName("u_0_arg"), DT_RESOURCE, 4);
  auto arg5 = ops::_Arg(scope.WithOpName("v_0_arg"), DT_RESOURCE, 5);

  // TODO(b/74182462): We implement this by creating a new dummy Graph including
  // _Arg nodes, and let CompileGraph walk it. This could be optimized.
  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));

  // First create the actual node we care about computing.
  TF_ASSIGN_OR_RETURN(Node * main_node, graph->AddNode(node_def));

  // Create dummy _Arg nodes. Link these to `node` and also via a control

}

TEST(CreateSaverDefTest, CreateValidSaverDef) {
  // Needs to have a _Arg node with an attribute "tf_saved_model.index_path" =
  // ["__tf_file_prefix"].
  GraphDef graph_def;
  ASSERT_TRUE(TextFormat::ParseFromString(
      R"pb(node {
             name: "foo",
             op: "_Arg",
             attr {
               key: "tf_saved_model.index_path",

Clustered nodes: 2725
Unclustered nodes: 606
Number of clusters: 2

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

namespace {

StatusOr<std::unique_ptr<Graph>> SampleGraphAddXY() {
  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
  Scope scope = Scope::NewRootScope().ExitOnError();
  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()));