docstr: No special modification required for Lightning, define it as you normally would
  in the `nn.Module` in vanilla PyTorch.
docstr_tok: ["No","special","modification","required","for","Lightning",",","define","it","as","you","normally","would","in","the","`","nn.Module","`","in","vanilla","PyTorch","."]
code: def forward(self, input_ids, attention_mask, token_type_ids):
      """
      No special modification required for Lightning, define it as you normally would
      in the `nn.Module` in vanilla PyTorch.
      """
      hidden_states = self.bert_model(
          input_ids=input_ids,
          token_type_ids=token_type_ids,
          attention_mask=attention_mask,
      )
      if isinstance(hidden_states, tuple):
          hidden_states = hidden_states[0]
  
      intent_logits, slot_logits = self.classifier(
          hidden_states=hidden_states
      )
      return intent_logits.float(), slot_logits.float()
  
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 8 for calling the IntentSlotClassificationModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, token_type_ids) and returns: intent_logits,  slot_logits