CollectionDef

 CollectionDef should cover most collections.
 To add a user-defined collection, do one of the following:
 1. For simple data types, such as string, int, float:
      tf.add_to_collection("your_collection_name", your_simple_value)
    strings will be stored as bytes_list.
 2. For Protobuf types, there are three ways to add them:
    1) tf.add_to_collection("your_collection_name",
         your_proto.SerializeToString())
       collection_def {
         key: "user_defined_bytes_collection"
         value {
           bytes_list {
             value: "queue_name: \"test_queue\"\n"
           }
         }
       }
  or
    2) tf.add_to_collection("your_collection_name", str(your_proto))
       collection_def {
         key: "user_defined_string_collection"
         value {
          bytes_list {
             value: "\n\ntest_queue"
           }
         }
       }
  or
    3) any_buf = any_pb2.Any()
       tf.add_to_collection("your_collection_name",
         any_buf.Pack(your_proto))
       collection_def {
         key: "user_defined_any_collection"
         value {
           any_list {
             value {
               type_url: "type.googleapis.com/tensorflow.QueueRunnerDef"
               value: "\n\ntest_queue"
             }
           }
         }
       }
 3. For Python objects, implement to_proto() and from_proto(), and register
    them in the following manner:
    ops.register_proto_function("your_collection_name",
                                proto_type,
                                to_proto=YourPythonObject.to_proto,
                                from_proto=YourPythonObject.from_proto)
    These functions will be invoked to serialize and de-serialize the
    collection. For example,
    ops.register_proto_function(ops.GraphKeys.GLOBAL_VARIABLES,
                                proto_type=variable_pb2.VariableDef,
                                to_proto=Variable.to_proto,
                                from_proto=Variable.from_proto)