code: def get_params(self, skip_modules=True):
      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,
          "config": self.config,
          "labels": self.labels,
          "extra_vocab": self.extra_vocab,
      }
      return params
  
imports: import logging
  import math
  import os
  import random
  import re
  from types import SimpleNamespace
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import stanza.models.classifiers.data
  from stanza.models.classifiers.base_classifier import BaseClassifier
  from stanza.models.classifiers.data import SentimentDatum
  from stanza.models.classifiers.utils import ExtraVectors, ModelType, build_output_layers
  from stanza.models.common.bert_embedding import extract_bert_embeddings
  from stanza.models.common.data import get_long_tensor, sort_all
  from stanza.models.common.foundation_cache import load_bert
  from stanza.models.common.vocab import PAD_ID, UNK_ID
label_desc: Use image node_id 9 for calling the CNNClassifier obj's underlying member method code with example usage: obj.get_params(skip_modules) and returns: params