code: def push_constituents(self, constituent_stacks, constituents):
      # Another possibility here would be to use output[0, i, :]
      # from the constituency lstm for the value of the new node.
      # This might theoretically make the new constituent include
      # information from neighboring constituents.  However, this
      # lowers the scores of various models.
      # For example, an experiment on ja_alt built this way,
      # averaged over 5 trials, had the following loss in accuracy:
      # 150 epochs: 0.8971 to 0.8953
      # 200 epochs: 0.8985 to 0.8964
      current_nodes = [stack.value for stack in constituent_stacks]
  
      constituent_input = torch.stack(
          [x.tree_hx[-1:] for x in constituents], axis=1
      )
      # constituent_input = constituent_input.unsqueeze(0)
      # the constituents are already Constituent(tree, tree_hx, tree_cx)
      return self.constituent_stack.push_states(
          constituent_stacks, constituents, constituent_input
      )
  
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 22 for calling the LSTMModel obj's underlying member method code with example usage: obj.push_constituents(constituent_stacks, constituents) and returns: self