docstr: Update data directory and get data stats with Data Descriptor.
  Also, reconfigures the classifier - to cope with data with e.g. different number of slots.
  
  Args:
      data_dir: path to data directory
docstr_tok: ["Update","data","directory","and","get","data","stats","with","Data","Descriptor",".","Also",",","reconfigures","the","classifier","-","to","cope","with","data","with","e.g",".","different","number","of","slots",".","Args",":","data_dir",":","path","to","data","directory"]
code: def update_data_dir_for_training(
      self, data_dir: str, train_ds, validation_ds
  ) -> None:
      """
      Update data directory and get data stats with Data Descriptor.
      Also, reconfigures the classifier - to cope with data with e.g. different number of slots.
  
      Args:
          data_dir: path to data directory
      """
      logging.info(f"Setting data_dir to {data_dir}.")
      self.data_dir = data_dir
      # Update configuration with new data.
      self._set_data_desc_to_cfg(
          self.cfg, data_dir, train_ds, validation_ds
      )
      # Reconfigure the classifier for different settings (number of intents, slots etc.).
      self._reconfigure_classifier()
  
imports: import os
  from typing import Dict, List, Optional
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from nemo.collections.common.losses import AggregatorLoss, CrossEntropyLoss
  from nemo.collections.nlp.data.dialogue.data_processor.assistant_data_processor import DialogueAssistantDataProcessor
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_bert_dataset import DialogueBERTDataset, DialogueIntentSlotInferenceDataset
  from nemo.collections.nlp.data.intent_slot_classification import IntentSlotDataDesc
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.metrics.dialogue_metrics import DialogueClassificationMetrics
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.collections.nlp.modules.common import SequenceTokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.classes import typecheck
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 6 for calling the IntentSlotClassificationModel obj's underlying member method code with example usage: obj.update_data_dir_for_training(data_dir, train_ds, validation_ds) without return types