code: def __init__(
      self,
      model_name_or_path: str,
      plugin: DPPluginBase = None,
      task_name: str = "mrpc",
      max_seq_length: int = 128,
      train_batch_size: int = 32,
      eval_batch_size: int = 32,
      **kwargs,
  ):
      super().__init__()
      self.model_name_or_path = model_name_or_path
      self.task_name = task_name
      self.max_seq_length = max_seq_length
      self.train_batch_size = train_batch_size
      self.eval_batch_size = eval_batch_size
      self.plugin = plugin
  
      self.text_fields = self.task_text_field_map[task_name]
      self.num_labels = self.glue_task_num_labels[task_name]
      self.tokenizer: PreTrainedTokenizer = (
          AutoTokenizer.from_pretrained(
              self.model_name_or_path, use_fast=True
          )
      )
      self.setup()
  
file_path: ColossalAI/colossalai/shardformer/examples/data.py
imports: import datasets
  from torch.utils.data import DataLoader
  from transformers import AutoTokenizer, PreTrainedTokenizer
  from colossalai.booster.plugin.dp_plugin_base import DPPluginBase
label_desc: Use image node_id 1 to create a new GLUEDataBuilder object  with example: obj = GLUEDataBuilder(model_name_or_path, plugin, task_name, max_seq_length, train_batch_size, eval_batch_size)