code: def __init__(self, model_name, model_dir, dropout, n_fc, n_classes):
      super(TransformerModel, self).__init__()
      self.config = AutoConfig.from_pretrained(
          model_name,
          num_labels=n_classes,
          output_hidden_states=n_fc,
          classifier_dropout=dropout,
          output_attentions=True,
      )
      self.transformer = (
          AutoModelForSequenceClassification.from_pretrained(
              model_name, cache_dir=model_dir, config=self.config
          )
      )
  
imports: import torch.nn
  from transformers import AutoConfig, AutoModelForSequenceClassification
label_desc: Use image node_id 1 to create a new TransformerModel object from inherited base classes: nn with example: obj = TransformerModel(model_name, model_dir, dropout, n_fc, n_classes)