code: def forward(self, input_ids, token_type_ids, attention_mask):
      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]
  
      output = self.pooler(hidden_states=hidden_states)
      return output
  
imports: import os
  from typing import Dict, Optional, Union
  import numpy
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.common.losses import CrossEntropyLoss, MSELoss
  from nemo.collections.nlp.data.glue_benchmark.glue_benchmark_dataset import GLUE_TASKS_NUM_LABELS, GLUEDataset
  from nemo.collections.nlp.models.glue_benchmark.metrics_for_glue import compute_metrics
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.collections.nlp.modules.common import SequenceClassifier, SequenceRegression
  from nemo.collections.nlp.parts.utils_funcs import list2str, tensor2list
  from nemo.core.classes import typecheck
  from nemo.core.neural_types import NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 6 for calling the GLUEModel obj's underlying member method code with example usage: obj.forward(input_ids, token_type_ids, attention_mask) and returns: output