code: def transform_word_to_constituent(self, state):
      word_node = state.get_word(state.word_position)
      word = word_node.value
      if (
          self.constituency_composition
          == ConstituencyComposition.TREE_LSTM
      ):
          return Constituent(
              word,
              word_node.hx.view(
                  self.num_tree_lstm_layers, self.hidden_size
              ),
              self.word_zeros.view(
                  self.num_tree_lstm_layers, self.hidden_size
              ),
          )
      elif (
          self.constituency_composition
          == ConstituencyComposition.TREE_LSTM_CX
      ):
          # the UNK tag will be trained thanks to occasionally dropping out tags
          tag = word.label
          tree_hx = word_node.hx.view(
              self.num_tree_lstm_layers, self.hidden_size
          )
          tag_tensor = self.tag_tensors[self.tag_map.get(tag, UNK_ID)]
          tree_cx = self.constituent_reduce_embedding(tag_tensor)
          tree_cx = tree_cx.view(
              self.num_tree_lstm_layers, self.hidden_size
          )
          return Constituent(word, tree_hx, tree_cx * tree_hx)
      else:
          return Constituent(
              word, word_node.hx[: self.hidden_size].unsqueeze(0), None
          )
  
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 19 for calling the LSTMModel obj's underlying member method code with example usage: obj.transform_word_to_constituent(state) and returns: Constituent, Constituent, Constituent