code: def get_params(self, skip_modules=True):
      model_state = self.state_dict()
      # skip all of the constituency parameters here -
      # we will add them by calling the model's get_params()
      skipped = [
          k
          for k in model_state.keys()
          if k.startswith("tree_embedding.")
      ]
      for k in skipped:
          del model_state[k]
  
      tree_embedding = self.tree_embedding.get_params(skip_modules)
  
      params = {
          "model": model_state,
          "tree_embedding": tree_embedding,
          "config": self.config,
          "labels": self.labels,
      }
      return params
  
file_path: stanza/stanza/models/classifiers/constituency_classifier.py
imports: import logging
  from types import SimpleNamespace
  import torch
  import torch.nn
  import torch.nn.functional
  from stanza.models.classifiers.base_classifier import BaseClassifier
  from stanza.models.classifiers.data import SentimentDatum
  from stanza.models.classifiers.utils import ModelType, build_output_layers
  from stanza.models.common.utils import split_into_batches, sort_with_indices, unsort
label_desc: Use image node_id 5 for calling the ConstituencyClassifier obj's underlying member method code with example usage: obj.get_params(skip_modules) and returns: params