docstr: Initializes BERT Joint Intent and Slot model.
          
docstr_tok: ["Initializes","BERT","Joint","Intent","and","Slot","model","."]
code: def __init__(self, cfg: DictConfig, trainer: Trainer = None):
      """Initializes BERT Joint Intent and Slot model."""
      self.max_seq_length = cfg.language_model.max_seq_length
      # init superclass
      # Check the presence of data_dir.
      if not cfg.data_dir or not os.path.exists(cfg.data_dir):
          # Set default values of data_desc.
          self._set_defaults_data_desc(cfg)
      else:
          self.data_dir = cfg.data_dir
          # Update configuration of data_desc.
          self._set_data_desc_to_cfg(
              cfg, cfg.data_dir, cfg.train_ds, cfg.validation_ds
          )
      super().__init__(cfg=cfg, trainer=trainer)
      # Initialize Classifier.
      self._reconfigure_classifier()
  
imports: import os
  import pathlib
  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.intent_slot_classification import IntentSlotClassificationDataset, IntentSlotDataDesc, IntentSlotInferenceDataset
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  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 1 to create a new IntentSlotClassificationModel object from inherited base classes: NLPModel with example: obj = IntentSlotClassificationModel(cfg, trainer)