docstr: Generate and return predictions, along with the transitions those predictions represent
  
  If is_legal is set to True, will only return legal transitions.
  This means returning None if there are no legal transitions.
  Hopefully the constraints prevent that from happening
code: def predict(self, states, is_legal=True):
      """
      Generate and return predictions, along with the transitions those predictions represent
  
      If is_legal is set to True, will only return legal transitions.
      This means returning None if there are no legal transitions.
      Hopefully the constraints prevent that from happening
      """
      predictions = self.forward(states)
      pred_max = torch.argmax(predictions, dim=1)
      scores = torch.take_along_dim(
          predictions, pred_max.unsqueeze(1), dim=1
      )
      pred_max = pred_max.detach().cpu()
  
      pred_trans = [
          self.transitions[pred_max[idx]] for idx in range(len(states))
      ]
      if is_legal:
          for idx, (state, trans) in enumerate(zip(states, pred_trans)):
              if not trans.is_legal(state, self):
                  _, indices = predictions[idx, :].sort(descending=True)
                  for index in indices:
                      if self.transitions[index].is_legal(state, self):
                          pred_trans[idx] = self.transitions[index]
                          scores[idx] = predictions[idx, index]
                          break
                  else:  # yeah, else on a for loop, deal with it
                      pred_trans[idx] = None
                      scores[idx] = None
  
      return predictions, pred_trans, scores.squeeze(1)
  
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 27 for calling the LSTMModel obj's underlying member method code with example usage: obj.predict(states, is_legal) and returns: predictions,  pred_trans,  scores