docstr: Return logits for a prediction of what transition to make next
  
  We've basically done all the work analyzing the state as
  part of applying the transitions, so this method is very simple
  
  return shape: (num_states, num_transitions)
code: def forward(self, states):
      """
      Return logits for a prediction of what transition to make next
  
      We've basically done all the work analyzing the state as
      part of applying the transitions, so this method is very simple
  
      return shape: (num_states, num_transitions)
      """
      word_hx = torch.stack(
          [state.get_word(state.word_position).hx for state in states]
      )
      transition_hx = torch.stack(
          [
              self.transition_stack.output(state.transitions)
              for state in states
          ]
      )
      # this .output() is the output of the constituent stack, not the
      # constituent itself
      # this way, we can, as an option, NOT include the constituents to the left
      # when building the current vector for a constituent
      # and the vector used for inference will still incorporate the entire LSTM
      constituent_hx = torch.stack(
          [
              self.constituent_stack.output(state.constituents)
              for state in states
          ]
      )
  
      hx = torch.cat((word_hx, transition_hx, constituent_hx), axis=1)
      for idx, output_layer in enumerate(self.output_layers):
          hx = self.predict_dropout(hx)
          if not self.maxout_k and idx < len(self.output_layers) - 1:
              hx = self.nonlinearity(hx)
          hx = output_layer(hx)
      return hx
  
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 26 for calling the LSTMModel obj's underlying member method code with example usage: obj.forward(states) and returns: hx