docstr: Get a dictionary for saving the model
code: def get_params(self, skip_modules=True):
      """
      Get a dictionary for saving the model
      """
      model_state = self.state_dict()
      # skip saving modules like pretrained embeddings, because they are large and will be saved in a separate file
      if skip_modules:
          skipped = [
              k for k in model_state.keys() if self.is_unsaved_module(k)
          ]
          for k in skipped:
              del model_state[k]
      params = {
          "model": model_state,
          "model_type": "LSTM",
          "config": self.args,
          "transitions": self.transitions,
          "constituents": self.constituents,
          "tags": self.tags,
          "words": self.delta_words,
          "rare_words": self.rare_words,
          "root_labels": self.root_labels,
          "constituent_opens": self.constituent_opens,
          "unary_limit": self.unary_limit(),
      }
  
      return params
  
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 30 for calling the LSTMModel obj's underlying member method code with example usage: obj.get_params(skip_modules) and returns: params