code: def forward(self, ids, mask, token_type_ids, targets=None):
      transformer_out = self.transformer(
          ids,
          attention_mask=mask,
          token_type_ids=token_type_ids,
      )
      out = transformer_out.pooler_output
      out = self.dropout(out)
      output = self.output(out)
      loss = self.loss(output, targets)
      acc = self.monitor_metrics(output, targets)
      return output, loss, acc
  
file_path: tez/examples/nlp/imdb_sentiment_classification.py
imports: import pandas
  import torch
  import torch.nn
  from sklearn import metrics, model_selection
  from transformers import AutoConfig, AutoModel, AutoTokenizer, get_linear_schedule_with_warmup
  from tez import Tez, TezConfig
  from tez.callbacks import EarlyStopping
label_desc: Use image node_id 5 for calling the IMDBModel obj's underlying member method code with example usage: obj.forward(ids, mask, token_type_ids, targets) and returns: output,  loss,  acc