docstr: For each State, return the next item in the gold_sequence
code: def predict_gold(self, states):
      """
      For each State, return the next item in the gold_sequence
      """
      predictions = self.forward(states)
      transitions = [
          y.gold_sequence[y.num_transitions()] for y in states
      ]
      indices = torch.tensor(
          [self.transition_map[t] for t in transitions],
          device=predictions.device,
      )
      scores = torch.take_along_dim(
          predictions, indices.unsqueeze(1), dim=1
      )
      return predictions, transitions, 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 29 for calling the LSTMModel obj's underlying member method code with example usage: obj.predict_gold(states) and returns: predictions,  transitions,  scores