code: def __init__(self, hparams):
      if type(hparams) == dict:
          hparams = Namespace(**hparams)
      hparams.glue_output_mode = glue_output_modes[hparams.task]
      num_labels = glue_tasks_num_labels[hparams.task]
  
      super().__init__(hparams, num_labels, self.mode)
  
imports: import argparse
  import glob
  import logging
  import os
  import time
  from argparse import Namespace
  import numpy
  import torch
  from torch.utils.data import DataLoader, TensorDataset
  from lightning_base import BaseTransformer, add_generic_args, generic_train
  from transformers import glue_compute_metrics
  from transformers import glue_convert_examples_to_features
  from transformers import glue_output_modes
  from transformers import glue_processors
  from transformers import glue_tasks_num_labels
label_desc: Use image node_id 1 to create a new GLUETransformer object from inherited base classes: BaseTransformer with example: obj = GLUETransformer(hparams)