docstr: Extract only the top constituent from a state's constituent
  sequence, even though it has multiple addition pieces of
  information
code: def get_top_constituent(self, constituents):
      """
      Extract only the top constituent from a state's constituent
      sequence, even though it has multiple addition pieces of
      information
      """
      # TreeStack value -> LSTMTreeStack value -> Constituent value
      constituent_node = constituents.value.value
      return constituent_node.value
  
imports: from collections import namedtuple
  from enum import Enum
  import logging
  import math
  import random
  import torch
  import torch.nn
  from torch.nn.utils.rnn import pack_padded_sequence
  from stanza.models.common.bert_embedding import extract_bert_embeddings
  from stanza.models.common.maxout_linear import MaxoutLinear
  from stanza.models.common.utils import unsort
  from stanza.models.common.vocab import PAD_ID, UNK_ID
  from stanza.models.constituency.base_model import BaseModel
  from stanza.models.constituency.label_attention import LabelAttentionModule
  from stanza.models.constituency.lstm_tree_stack import LSTMTreeStack
  from stanza.models.constituency.parse_transitions import TransitionScheme
  from stanza.models.constituency.parse_tree import Tree
  from stanza.models.constituency.partitioned_transformer import PartitionedTransformerModule
  from stanza.models.constituency.positional_encoding import ConcatSinusoidalEncoding
  from stanza.models.constituency.transformer_tree_stack import TransformerTreeStack
  from stanza.models.constituency.tree_stack import TreeStack
  from stanza.models.constituency.utils import build_nonlinearity, initialize_linear
label_desc: Use image node_id 23 for calling the LSTMModel obj's underlying member method code with example usage: obj.get_top_constituent(constituents) and returns: constituent_node