class_docstr: Dialogue State Tracking Model SGD-QA (https://arxiv.org/abs/2105.08049)
  
  The SGD-QA model is a fast multi-pass schema-guided state-tracking model, that is trained on the Google schema-guided state tracking dataset (https://arxiv.org/abs/1909.05855).
  The model takes dialogue as input and outputs the dialogue state, which includes slot-value pairs. 
  The model consists of two components: a neural natural language understanding model (NLU), and a rule-based state tracker.
  The NLU takes in a dialogue turn and different schema (entity) information options and outputs their match score. The state tracker takes the highest rated entities and composes
  the dialogue state across turns.
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docstr: Update data directories
  
  Args:
      data_dir: path to data directory
      dialogues_example_dir: path to preprocessed dialogues example directory, if not exists will be created.
docstr_tok: ["Update","data","directories","Args",":","data_dir",":","path","to","data","directory","dialogues_example_dir",":","path","to","preprocessed","dialogues","example","directory",",","if","not","exists","will","be","created","."]
code: def update_data_dirs(self, data_dir: str, dialogues_example_dir: str):
      """
      Update data directories
  
      Args:
          data_dir: path to data directory
          dialogues_example_dir: path to preprocessed dialogues example directory, if not exists will be created.
      """
      if not os.path.exists(data_dir):
          raise ValueError(f"{data_dir} is not found")
      self._cfg.dataset.data_dir = data_dir
      self._cfg.dataset.dialogues_example_dir = dialogues_example_dir
      logging.info(f"Setting model.dataset.data_dir to {data_dir}.")
      logging.info(
          f"Setting model.dataset.dialogues_example_dir to {dialogues_example_dir}."
      )
  
imports: import os
  from typing import List, Optional
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from nemo.collections.nlp.data.dialogue import DialogueSGDBERTDataset, DialogueSGDDataProcessor
  from nemo.collections.nlp.data.dialogue.sgd.evaluate import evaluate, get_in_domain_services
  from nemo.collections.nlp.data.dialogue.sgd.prediction_utils import write_predictions_to_file
  from nemo.collections.nlp.losses import SGDDialogueStateLoss
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.collections.nlp.modules import SGDDecoder, SGDEncoder
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 12 for calling the SGDQAModel obj's underlying member method code with example usage: obj.update_data_dirs(data_dir, dialogues_example_dir) without return types