class_docstr: Token Classification Model with BERT, applicable for tasks such as Named Entity Recognition
class_docstr_tok: ["Token","Classification","Model","with","BERT",",","applicable","for","tasks","such","as","Named","Entity","Recognition"]
code: def forward(self, input_ids, attention_mask, token_type_ids):
      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]
      logits = self.classifier(hidden_states=hidden_states)
      return logits
  
imports: import os
  from typing import List, Optional, Union
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from nemo.collections.common.losses import CrossEntropyLoss
  from nemo.collections.nlp.data.data_utils.data_preprocessing import get_labels_to_labels_id_mapping
  from nemo.collections.nlp.data.token_classification.token_classification_dataset import BertTokenClassificationDataset, BertTokenClassificationInferDataset
  from nemo.collections.nlp.data.token_classification.token_classification_utils import get_label_ids
  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 TokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import get_classification_report, plot_confusion_matrix, tensor2list
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the TokenClassificationModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, token_type_ids) and returns: logits