class_docstr: The Recurrent Neural Network Grammar (RNNG) parser from
  Dyer et al.: https://arxiv.org/abs/1602.07776 and
  Gupta et al.: https://arxiv.org/abs/1810.07942d.
  RNNG is a neural constituency parsing algorithm that
  explicitly models compositional structure of a sentence.
  It is able to learn about hierarchical relationship among the words and
  phrases in a given sentence thereby learning the underlying tree structure.
  The paper proposes generative as well as discriminative approaches.
  In PyText we have implemented the discriminative approach for modeling
  intent slot models.
  It is a top-down shift-reduce parser than can output
  trees with non-terminals (intent and slot labels) and terminals (tokens)
code: def save_modules(self, *args, **kwargs):
      pass
  
imports: from enum import Enum
  from typing import Dict, List, Optional, Tuple
  import numpy
  import pytext.utils.cuda
  import torch
  import torch.nn
  import torch.nn.functional
  from pytext.common.constants import Stage
  from pytext.config import ConfigBase
  from pytext.config.component import ComponentType
  from pytext.data import CommonMetadata
  from pytext.data.tensorizers import AnnotationNumberizer, Tensorizer, TokenTensorizer
  from pytext.data.utils import pad_and_tensorize
  from pytext.models import BaseModel, Model
  from pytext.models.embeddings import EmbeddingList
  from pytext.models.embeddings.word_embedding import WordEmbedding
  from pytext.models.module import create_module
  from pytext.models.representations.bilstm import BiLSTM
  from pytext.models.semantic_parsers.rnng.rnng_data_structures import CompositionalNN, CompositionalSummationNN, Element, ParserState
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 9 for calling the RNNGParserBase obj's underlying member method code with example usage: obj.save_modules() without return types