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()));

node {
  name: "arg"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "retval"
  op: "_Retval"
  input: "arg"

    }
    func.return %0#0, %0#1 : tensor<2x2x1x2xi32>, tensor<2x2x1x2xf32>
  }
}

// CHECK:        name: "serving_default_x"
// CHECK-NEXT:   op: "_Arg"

// CHECK:        name: "tf.MaxPoolWithArgmax"
// CHECK-NEXT:   op: "MaxPoolWithArgmax"
// CHECK-NEXT:   input: "serving_default_x"


// CHECK:        name: "PartitionedCall"
// CHECK-NEXT:   op: "_Retval"

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

node {
  name: "input0"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "args_0"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "spc1"

}

// Builds XlaRecvAtHost node, and replaces all _Arg nodes with it.
absl::StatusOr<Node*> ReplaceArgNodesWithRecvAtHostNode(
    Graph* g, const string& oc_cluster_name,
    std::vector<DataType>* recv_at_host_dtypes, Node* key_placeholder) {
  // TODO(b/77601805): use out nodes for source node, instead of traversing all
  // nodes.
  std::vector<Node*> arg_nodes = GatherNodesWithType(*g, "_Arg");

// lifted out of If/While/function node. Attribute value will always be boolean
// value "true".
extern const char kXlaIsLiftedArgAttrName[];

// Attribute indicating that this node is a Placeholder node for an _Arg node
// lifted out of If/While/function node. Attribute value will be a string, which
// is the outside compilation cluster name sending the lifted arg node to host.
extern const char kXlaLiftedArgOutsideCompilationAttrName[];

    int GetResultIndexForEdge(const Edge* edge) const;

    // Creates an _Arg node for the src node of edge, and add its index to
    // args_by_src_, if none exists yet. Also adds its index to args_by_dst_,
    // and adds the edge within the subgraph from the _Arg node to the image of
    // the dst node.
    Status RecordArg(const Edge* edge,

// TODO(phawkins): this code does not infer accurate shapes for cyclic graphs.
// `arg_shapes`: user given map from the `index` to shapes of this
// node, where `index` is the `index` attribute of `_Arg` op or `_index`
// attribute of `Placeholder` op.
Status InferShapes(Graph* graph, const std::map<int, InferredShape>& arg_shapes,
                   const tensorflow::FunctionLibraryDefinition* fnlib_def,

  graph.SetConstructionContext(ConstructionContext::kEagerRuntime);
  tensorflow::set_tf2_execution(true);

  ConfigProto config = ConfigProto();
  Scope root = Scope::NewRootScope().ExitOnError();

  Output a = ops::_Arg(root.WithOpName("A"), DT_RESOURCE, 0);
  std::vector<NodeBuilder::NodeOut> inputs({NodeBuilder::NodeOut(a.node())});

  Node* call;
  NameAttrList f_name_attr;
  f_name_attr.set_name(fd.signature().name());
  TF_ASSERT_OK(